You will be able register for a Cambridge University Press Higher Education account to request access to instructor resources for the book.

1. Introduction to Medical Software

This chapter begins by defining what medical software is and what makes it unique and describing the regulatory process that governs it (Section 1.1), including a brief introduction to industry standards. Following this, we discuss the constraints (both business and technical) placed on the software process by the medical environment (Section 1.2). The chapter concludes with a discussion of the challenges that result from the use of artificial intelligence/machine learning (AI/ML) techniques (Section 1.3). This section also provides references to other parts of this book where we discuss AI/ML issues.

2. The FDA and Software

This chapter describes the regulatory process for medical software, with a particular emphasis on the documents issued by the United States Food and Drug Administration (FDA). We first describe the FDA itself (Section 2.1), including a brief history of how the current process has evolved over the past century. We then review in detail some key FDA regulatory documents (Section 2.2) and provide an overview of the actual regulatory process (Section 2.3). After this, we survey related documents and agencies from the rest of the world (Section 2.4). Unsurprisingly, given that most regulatory agencies are participants in the International Medical Device Regulators Forum (IMDRF), there is significant convergence in the regulations operating in different countries. The next section (Section 2.5) discusses the implications of these regulations for the software process. The chapter concludes with a look at emerging regulatory guidance on the use of artificial intelligence/machine learning (AI/ML) techniques in medical software and some comments about the applicability of some of the concepts in the medical software regulations to other industries (Section 2.6).

3. Operating within a Healthcare System

This chapter provides background on the constraints imposed on software by the need to operate within a healthcare environment. We first present an overview of the environment and the constraints under which our software must operate (Section 3.1). A description of the current healthcare environment (Section 3.2) follows. We begin with a description of the complex system currently in place in the United States, and then discuss how healthcare operates in the rest of the world. The next section discusses clinical information technology (Section 3.3), with an emphasis on electronic health records (EHR) and imaging databases (PACS). Following this, we review issues related to data privacy (HIPAA and GDPR) in Section 3.4. We conclude this chapter with a discussion of Cybersecurity (Section 3.5.) This is one of the primary sources of security concerns in medical software and a topic of increasing importance in today's connected world.

4. Quality Management Systems

This chapter provides an overview of quality management systems (QMS). There is an increasing emphasis of regulators on the `organization' as opposed to the `product,' which places an even greater emphasis on the use of a QMS. We first introduce what a QMS is (Section 4.1) and provide some regulatory background, including a discussion of the recent FDA precertification program. Next we discuss various international standards that apply (Section 4.2). The core of this chapter is an extended discussion of the IMDRF QMS regulatory document (Section 4.3), which provides an excellent and SaMD-specific description of this important topic. The chapter concludes with a brief discussion (Section 4.4) of the implications of quality systems (or rather their absence) on research and scientific software and how failure to have/adhere to a QMS (Section 4.5) can lead to serious problems.

5. Risk Management

This chapter presents an introduction to risk management, a core regulatory requirement for all medical software. We begin with an overview of the regulatory background (Section 5.1) and then review both the international standard ISO 14971:2019 and a recent guidance document from the IMDRF. Next we describe the process of risk analysis (Section 5.2), including issues related to the use of artificial intelligence/machine learning techniques. We then discuss risk evaluation (Section 5.3) and risk control strategies (Section 5.4). The chapter concludes with a section (Section 5.5) that briefly outline the role of risk management in the software life cycle process described in Part III of this book, where risk management will be our ever-present companion.

6. Taking an Idea to Market: Understanding the Product Journey

This chapter presents the `business' view of medical software. It takes a company to bring an idea to market and, ultimately, clinical use. We begin with a brief description of issues related to entrepreneurship (Section 6.1): should somebody who has a promising idea consider starting a company on their own? Next, we discuss the issues of user-centered design (Section 6.2) and of articulating the value proposition of a new product (Section 6.3). We then take a detour through the minefield that is ensuring proper intellectual property protections (Section 6.4). Finally, we discuss the process of raising capital to support a new venture (Section 6.5). While the material in this chapter is focused on startups, it is also important for those that are employed in `large' companies, as this material will give them an understanding of what their `managers' are worrying about. In a large company, the capital raising and marketing may happen internally as a particular group within a company tries to obtain support for a new product from higher level management. The process looks different, but the fundamental concepts are just as applicable. The same concepts also apply to university research.

7. Medical Software Applications and Growth Drivers

This chapter presents an overview of current medical software applications and the factors that promise to drive growth in this area. We begin this chapter by defining and discussing some important terms such as Digital Biomarkers and Digital Health (Section 7.1). Next, we describe the current technological transformation of healthcare (Section 7.2). In Section 7.3, we present the major challenges faced by the healthcare sector and in particular the triple aim of improving patient experience (better care), improving the health of the population (better quality) and reducing the cost of care (better value). Section 7.4 discusses the promising opportunities that will be become available as new technologies are adopted in healthcare and Sections 7.5--7.10 discuss the drivers of the current wave of healthcare technology growth, including financing, innovations by the Food and Drug Administration (FDA), and increased collaboration across the healthcare continuum. The promises of this technological transformation of healthcare are examined in clinical care, digital medicine, research & development, and remote monitoring. We conclude the chapter with some recommendations for software developers entering this field (Section 7.11)

8. Mathematical Background

Much of the work involved in the development of medical software (and in particular the process of software validation) depend critically on an understanding of topics such as probability theory, statistics and increasingly machine learning. The goal of this chapter is to provide students with some theoretical grounding in this general area. After a brief high-level introduction to decision theory (Section 8.1), we discuss each topic in turn, beginning with an introduction to probability (Section 8.2) and statistics (Section 8.3), including an extended discussion of the topic of signal detection, which is particularly important in medical applications. We then present an overview of machine learning and some of the issues involved in the use of these techniques (Section 8.4). Next, we introduce the concept of statistical significance (Section 8.5) and conclude the chapter with a discussion of the importance of randomization (Section 8.6) in clinical studies.

9. Topics in Software Engineering

This chapter provides some basic background on selected topics in software engineering. This should be useful for those readers whose background is primarily in basic science and engineering, who may have not been previously exposed to this type of material. This chapter is meant to complement the introduction to mathematical topics presented in Chapter 8. We begin with a brief overview of software engineering (Section 9.1). Next, we discuss the software life cycle (Section 9.2), the organizing foundation of any software project. Following this, we present issues related to the use of artificial intelligence/machine learning (AI/ML) techniques (Section 9.3) in software, in particular the centrality of data in this process. Next, we discuss modern computing environments such as cloud-based infrastructures and mobile devices (Section 9.4). The chapter concludes with a description of two core software engineering topics: software testing (Section 9.5) and source code management (Section 9.6).

10. The Overall Process

This chapter presents an example medical software life cycle process. We first introduce the topic (Section 10.1), and introduce our example project -- the image-guided neuro-navigation system -- that we will use to illustrate the process over the next few chapters. Next we summarize our life cycle `recipe' (Section 10.3) for medical software. This recipe follows the guidelines in the international standard IEC 62304. It begins with identifying user needs and concludes with the satisfaction of these needs via a structured process of first establishing requirements and then designing, implementing, testing and releasing our software. We also briefly discuss software safety classifications (Section 10.4) and present some thoughts on traceability and planning for the overall process (Section 10.5). The chapter concludes with a section that uses the children's game `Telephone' to illustrate some of the potential pitfalls in this long process (Section 10.5.4).

11. Identifying User Needs

This chapter discusses the processes of identifying the needs of our users which is the first step in the software life cycle. We begin with an overview of the structure of creating new projects. Next, we apply the "Brown Cow" model (Section 11.1) as a guide for how to go about understanding the user's current situation and future needs. We will then touch on the formal process for creating a Use Specification as discussed in the guidance document IEC 62366-2 (Section 11.2). Finally, we `begin' our example image-guided neuro-navigation project (Section 11.3) and use this to illustrate the concepts presented in this chapter. We will return to this project in the next few chapters as we continue along our software life cycle journey.

12. The System Requirements Specification

This chapter describes the process of creating a systems requirements document which is one of the most important steps in the software life cycle. First, we orient ourselves as to our position in this life cycle (Section 12.1). Next, we present a brief review of key regulatory issues (Section 12.2), and following this we describe a template for creating this document (Section 12.3). Next, we discuss in more detail the process of writing a requirement (Section 12.4). As always, we then revisit risk management (Section 12.5) and address how to evaluate our requirements for potential harms. The next section (Section 12.6) discusses how to review systems requirements. The chapter concludes by continuing our discussion of the example image-guided neuro-navigation project by presenting an outline of what the systems requirements document for this might look like (Section 12.7).

13. The Software Design Document

This chapter describes the software design document. This is the first step in the software life cycle where we leave the abstract plane and begin to think more concretely about the product. The structure of this chapter is as follows. First, we discuss (Section 13.1) that we are at the point of creating a bridge from the system requirements to the actual code. Next, we present a brief review of key regulatory issues (Section 13.2) involved in creating this document. We then describe a template for the software design document (Section 13.3). As always, we then revisit risk management (Section 13.4) and address how to evaluate our design for potential harms. Within risk management we address the important issue of human factors. The chapter concludes (Section 13.6) by continuing our discussion of the example image-guided neuro-navigation project by presenting an outline of what the software design document for this might look like.

14. Software Construction and Testing and Verification

This chapter describes software construction and testing. These are the very concrete steps in the software life cycle (Section 14.1). Next we discuss `construction,' or coding (Section 14.2) beginning with a brief review of key regulatory issues. We follow this with a presentation of programming topics and conclude this section with an extended discussion of risk management in the context of the programming process. The next section (Section 14.3) focuses on testing and again begins with a review of regulatory issues before moving to brief section on risk management in the context of testing. In the final section (Section 14.4), we provide some pointers as to how one would go about creating a verification plan.

15. Software Validation

This chapter describes the process of software validation. We first begin with a brief overview of what validation is. We then review the regulatory guidance for validation (Section 15.1) in general, and then provide an extended discussion on the issues that affect the validation of software modules that use artificial intelligence/machine learning techniques in particular. Next, we present a description of human subject studies and clinical trials (Section 15.2), including a description of the necessary ethical constraints on such studies. In the last part of this chapter (Section 15.3), we present a simplified strategy for designing a validation plan appropriate for an introductory class.

16. Deployment, Maintenance and Decommissioning

In this short chapter, we discuss the final three steps in the software life cycle: (i) deployment (Section 16.1), (ii) maintenance (Section 16.2), and (iii) decommissioning (Section 16.3). These are complex topics in their own right. Our goal here is, to simply and briefly, highlight some information about each of these processes so as to make the reader aware of what the major issues are for the sake of completeness.

17. [Vignette] Therac 25: Software that Killed

This vignette describes the Therac-25 radiation therapy machine, whose software bugs and lack of hardware safety interlocks led to six serious accidents from 1982-1987. Three lives were claimed due to overconfidence in software and loose design regulations. Ultimately, these events were a catalyst for the FDA to begin investigating and regulating medical software.

18. [Vignette] Mars Climate Orbiter: Lost Without A Trace

This vignette describes the accidental destruction of a multi-million-dollar outer space satellite, primarily due to inconsistency of mathematical units used in different components of the system. Poor fault analysis, decision-making, integration testing, and auditing led the Mars Climate Orbiter to burn up in Mars' atmosphere, never to be contacted again.

19. [Vignette] Healthcare.gov: The Failed Launch of a Critical Webpage

This vignette discusses the failed launch of the online marketplace for purchasing individual healthcare insurance that was created as part of the Affordable Care Act (ACA) in Fall 2013. A key component of this effort was the webpage (Healthcare.gov), through which individuals would be able to buy health insurance. The launch of this webpage failed, resulting in significant problems in the implementation of the ACA. This was a major process failure: software best practices were not followed, resulting in major cost overruns.

20. [Vignette] The 2020 Iowa Caucus App: An Unreliable App that Caused National Embarrassment

On Monday, February 3rd, 2020, the results of the Democratic Iowa Caucus, the first caucus in the U.S. election cycle, were delayed as a result of bugs in the software used to report the results. This software (the IowaReporter App) was not properly tested prior to the elections, and the whole reporting system failed during the event. This led to multi-day delays in reporting the results of the caucus, throwing the entire primary election process into confusion and disarray.

21. [Vignette] The Boeing 737-MAX Disasters: Using Software to Fix Hardware Problems

In the case of the Boeing 737 MAX disaster, the manufacturer tried to address hardware problems with software fixes in order to avoid the costs of re-certification of what would have been a new airplane (had the fixes been done in hardware). Overconfidence in the software and insufficient testing and pilot training led to two fatal crashes and 346 fatalities. Boeing has already paid a substantial fine to settle some of the legal issues arising from the case, though other legal procedures are still ongoing.

22. [Vignette] The Averted Y2K Crisis: Successful Crisis and Risk Management

The changeover from 1999 to 2000 introduced a major risk of system failure in computer systems worldwide. This had to do with older software's use of 2-digit numbers to store the year. As the year (in two digits) moved from 99 to 00, there were serious possibilities of many systems failing. This resulted in a major effort to fix this problem across multiple major computer systems. In the end, despite minor issues, the change to the new millennium happened without any major problems.

1. Association for the Advancement of Medical Instrumentation (AAMI). AAMI TIR45:2012/(R)2018 Guidance on the use of AGILE practices in the development of medical device software. Washington, DC, 20 August 2012. (https://webstore.ansi.org/standards/aami/aamitir452012tir45)

2. Acumen Research and Consulting. Digital Health Market Value to Reach USD 511 Billion by 2026: The global digital health market size was valued at USD 95.7 billion in 2018 and expanding at a CAGR of 27.7% from 2019 to 2026, 12 November 2019. (https://www.prnewswire.com/news-releases/digital-health-market-value-to-reach-usd-511-billion-by-2026-acumen-research-and-consulting-300956297.html)

3. T. Aeppel. Manufacturers Plan to Set Aside Extra Inventory as Y2K Safeguard. The Wall Street Journal, 9 February 1999. (https://www.wsj.com/articles/SB918519219843389000)

4. N. Akhtar and A. Mian. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey. IEEE Access, 6:14410--14430, 2018. (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186)

5. T. A. Alspaugh. Software Process Models, (Accessed January 2021). (https://www.thomasalspaugh.org/pub/fnd/softwareProcess.html)

6. S. Amershi, A. Begel, C. Bird, R. DeLine, H. Gall, E. Kamar, N. Nagappan, B. Nushi, and T. Zimmermann. Software engineering for machine learning: A case study. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP '19, page 291–300. IEEE Press, 2019. (https://doi.org/10.1109/ICSE-SEIP.2019.00042)

7. ABC123 (Anonymous). The failed launch of www.healthcare.gov. HBS Digital Initiative, 18 November 2016. (https://digital.hbs.edu/platform-rctom/submission/the-failed-launch-of-www-healthcare-gov/)

8. American National Standards Institute (ANSI) and The Santa Fe Group /Internet Security Alliance. The financial impact of brached protected health information: A business case for enhanced PHI security. Technical report, ANSI, Washington DC, USA, 2012.

9. Arterys FDA 510K Premarket Notification, May 2017. (https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K163253)

10. Arterys Inc, (Accessed January 2021). (https://arterys.com/)

11. American Society for Quality (ASQ), (Accessed January 2021). (https://asq.org)

12. J. Atherton. Development of the Electronic Health Record. Virtual Mentor, 13(3):186--189, 2011. (https://journalofethics.ama-assn.org/article/development-electronic-health-record/2011-03)

13. B. Aulet. Disciplined Enterpreneurship. Wiley, 2013.

14. J. Avorn. The $2.6 billion pill--methodologic and policy considerations. N Engl J Med, 372(20):1877--1879, May 2015. (https://www.ncbi.nlm.nih.gov/pubmed/25970049)

15. K. Beck. Extreme Programming Explained: Embrace Change. Addison-Wesley, Reading, Massachusetts, 2000. (https://www.amazon.com/Extreme-Programming-Explained-Embrace-Change/dp/0321278658)

16. K. Beck, M. Beedle, A. van Bennekum, A. Cockburn, W. Cunningham, M. Fowler, J. Grenning, J. Highsmith, A. Hunt, R. Jeffries, J. Kern, B. Marick, R. C. Martin, S. Mellor, K. Schwaber, J. Sutherland, and D. Thomas. Manifesto for Agile Software Development, 2001. (http://www.agilemanifesto.org/)

17. S. Benjamens, P. Dhunnoo, and B. Meskó. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med, 3:118, 2020. (https://doi.org/10.1038/s41746-020-00324-0)

18. J. M. Bland and D. G. Altman. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet, 346(8982):1085--1087, Oct 1995.

19. W. Boston. How Volkswagen's $50 Billion Plan to Beat Tesla Short-Circuited: Faulty software set back a bid by the world's largest car maker for electric-vehicle dominance. The Wall Street journal, 19 January 2021. (https://www.wsj.com/articles/how-volkswagens-50-billion-plan-to-beat-tesla-short-circuited-11611073974)

20. BrainLAB, Munich, Germany. BrainLAB VectorVision Cranial, (Accessed January 2021). (http://www.brainlab.com/)

21. Britannica. Y2K Bug, 18 May 2020. (https://www.britannica.com/technology/Y2K-bug)

22. R. Buechi, L. Faes, L. M. Bachmann, M. A. Thiel, N. S. Bodmer, M. K. Schmid, O. Job, and K. R. Lienhard. Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis. BMJ Open, 7(12):e018280, December 2017. (https://pubmed.ncbi.nlm.nih.gov/29247099/)

23. L. A. Burke and A. M. Ryan. The Complex Relationship between Cost and Quality in US Health Care. Virtual Mentor, 16(2):124--130, 2014. (https://journalofethics.ama-assn.org/article/complex-relationship-between-cost-and-quality-us-health-care/2014-02)

24. R. Buys, J. Claes, D. Walsh, N. Cornelis, K. Moran, W. Budts, C. Woods, and V. A. Cornelissen. Cardiac patients show high interest in technology enabled cardiovascular rehabilitation. BMC Med Inform Decis Mak, 16(95), 2016. (https://doi.org/10.1186/s12911-016-0329-9)

25. D. Campbell. The Ancient Computers in the Boeing 737 MAX are Holding up a Fix. The Verge, 9 April 2020. (https://www.theverge.com/2020/4/9/21197162/boeing-737-max-software-hardware-computer-fcc-crash)

26. Health Canada. Software as a Medical Device (SaMD): Definition and Classification, 18 Decemeber 2019. (https://www.canada.ca/en/health-canada/services/drugs-health-products/medical-devices/application-information/guidance-documents/software-medical-device-guidance-document.html)

27. ASCO CancerLinQ, (Accessed March 2021). (https://www.cancerlinq.org/)

28. S. Chacon and B. Straub. Pro Git. Apress, 2nd edition, 2014. (https://git-scm.com/book/en/v2)

29. China Med Device (CMD), (Accessed January 2021). (https://chinameddevice.com/)

30. K. Chinzei, A. Shimizu, K. Mori, K. Harada, H. Takeda, M. Hashizume, M. Ishizuka, N. Kato, R. Kawamori, S. Kyo, K. Nagata, T. Yamane, I. Sakuma, K. Ohe, and M. Mitsuishi. Regulatory Science on AI-based Medical Devices and Systems. Advanced Biomedical Engineering, 7:118--123, 2018. (https://www.jstage.jst.go.jp/article/abe/7/0/7_7_118/_article/-char/en)

31. N. Chiu, A. Kramer, and A. Shah. 2020 Midyear Digital Health Market Update: Unprecedented funding in an unprecedented time, 2020. (https://rockhealth.com/reports/2020-midyear-digital-health-market-update-unprecedented-funding-in-an-unprecedented-time/)

32. J. Cleland-Huang. Don't Fire the Architect! Where Were the Requirements? IEEE Software, 31(2):27--29, 2014. (https://ieeexplore.ieee.org/document/6774318)

33. C. Cochran. ISO 9001:2015 In Plain English. Paton Professional, Chico, California, 2015. (https://www.amazon.com/ISO-9001-2015-Plain-English/dp/1932828729)

34. J. Cohen. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37--46, 1960. (https://doi.org/10.1177/001316446002000104)

35. V. Combs. Why shortcuts lead to failure: Lessons from app disaster in Iowa caucus. Tech Republic, 6 February 2020. (https://www.techrepublic.com/article/why-shortcuts-lead-to-failure-lessons-from-app-disaster-in-iowa/)

36. A. Cooper. The Inmates Are Running the Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity. Sams - Pearson Education, 1st edition, 24 February 2004. (https://www.amazon.com/Inmates-Are-Running-Asylum-Products/dp/0672326140)

37. N. Corasaniti, S. Frenkel, and N. Perlroth. App Used to Tabulate Votes Is Said to Have Been Inadequately Tested. The New York Times, 3 Feburary 2020. (https://www.nytimes.com/2020/02/03/us/politics/iowa-caucus-app.html)

38. A. Coravos, J. C. Goldsack, D. R. Karlin, C. Nebeker, E. Perakslis, N. Zimmerman, and M. K. Erb. Digital Medicine: A Primer on Measurement. Digit Biomark, 3:31--71, 2019. (https://www.karger.com/Article/FullText/500413)

39. Clinical Ttrials Ttransformation Initiative, (Accessed September 2020). (https://www.ctti-clinicaltrials.org/)

40. Clinical Ttrials Ttransformation Initiative (CTTI): Decentralized Clinical Trials, (Accessed January 2021). (https://www.ctti-clinicaltrials.org/projects/decentralized-clinical-trials)

41. Clinical Ttrials Ttransformation Initiative (CTTI): Digital Health Trials, (Accessed January 2021). (https://www.ctti-clinicaltrials.org/projects/mobile-technologies)

42. Clinical Ttrials Ttransformation Initiative (CTTI): Project: Real-World Data, (Accessed January 2021). (https://www.ctti-clinicaltrials.org/projects/real-world-data)

43. Decibio Insights. How Consumerization is Driving Evolution of Digital Health through Strategic Partnerships, 7 November 2019. (https://www.decibio.com/2019/11/07/digital-strategic-partnerships/)

44. Deloitte Centre for Health Solutions. Measuring the return from pharmaceutical innovation, 2019. (https://www2.deloitte.com/uk/en/pages/life-sciences-and-healthcare/articles/measuring-return-from-pharmaceutical-innovation.html)

45. P. Densen. Challenges and opportunities facing medical education. Trans Am Clin Climatol Assoc, 122:48--58, 2011. (https://www.ncbi.nlm.nih.gov/pubmed/21275727)

46. U.S. Department of Health and Human Services (DHSS). CMS Management of the Federal Marketplace, A Case Study, February 2016. (https://oig.hhs.gov/oei/reports/oei-06-14-00350.pdf)

47. U.S. Department of Health and Human Services (DHSS): Office of Civil Rights. Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule, 26 November 2012. (https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html)

48. U.S. Department of Health and Human Services (DHSS): Office of Civil Rights. HIPAA Administrative Simplification: Regulation Text., March 2013. (https://www.hhs.gov/sites/default/files/hipaa-simplification-201303.pdf)

49. J. A. DiMasi, H. G. Grabowski, and R. W. Hansen. Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47:20 -- 33, 2016. (https://pubmed.ncbi.nlm.nih.gov/26928437/)

50. The Digital Medicine (DiMe) Society, (Accessed September 2020). (https://www.dimesociety.org/)

51. German Institute for Standardization (DIN -- Deutsches Institut fur Normung). DIN SPEC 92001-1:2019-4 Artificial Intelligence – Life Cycle Processes and Quality Requirements – Part 1: Quality Meta Model. Berlin, Germany, April 2019. (https://webstore.ansi.org/standards/din/dinspec920012019)

52. T. Doherty and T. Lindeman. The problems that led to the Boeing 737 MAX grounding. Politico, 15 March 2019. (https://www.politico.com/story/2019/03/15/boeing-737-max-grounding-1223072)

53. Digital Therapeutics Alliance, (Accessed September 2020). (https://www.dtxalliance.org/)

54. R. Duda and P. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1973.

55. The Editorial Board. Boeing's Fatal Lessons. The Wall Street Journal, 17 December 2019. (https://www.wsj.com/articles/boeings-fatal-lessons-11576628330)

56. A. Eklund, T. E. Nichols, and H. Knutsson. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences, 113(28):7900--7905, 2016.

57. M. Enos. Fortune, Shadow Inc.: How a company with 120 Facebook likes ended up at the center of the Iowa Caucus firestorm. Fortune, 6 February 2020. (https://fortune.com/2020/02/06/shadow-app-acronym-iowa-caucus-results/)

58. Epic. Verona, WI, (Accessed January 2021). (https://www.epic.com/)

59. Epic HL7v2, (Accessed January 2021). (https://open.epic.com/Interface/HL7v2)

60. A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean. A guide to deep learning in healthcare. Nat Med, 25(1):24--29, 01 2019. (https://pubmed.ncbi.nlm.nih.gov/30617335/)

61. EU Medical Device Coordination Group. MDCG 2019-11: Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 – MDR and Regulation (EU) 2017/746 – IVDR, October 2019. MDCG 2019-11 rev. 1. (https://ec.europa.eu/health/sites/health/files/md_topics-interest/docs/md_mdcg_2019_11_guidance_en.pdf)

62. European Medicines Agency. Questions and answers: Qualification of digital technology-based methodologies to support approval of medicinal products, 1 June 2020. (https://www.ema.europa.eu/en/documents/other/questions-answers-qualification-digital-technology-based-methodologies-support-approval-medicinal_en.pdf)

63. European Union. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC, as amended, 5 April 2017. Document 02017R0745-20200424. (https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0745)

64. European Union. General Data Protection Regulation (GDPR), 2018. (https://gdpr.eu)

65. Heads of Medicines Agencies and European Medicines Agency. HMA-EMA Joint Big Data Taskforce – Summary report, 13 February 2019. (https://www.ema.europa.eu/en/documents/minutes/hma/ema-joint-task-force-big-data-summary-report_en.pdf)

66. P. Farrugia, B. A. Petrisor, F. Farrokhyar, and M. Bhandari. Practical tips for surgical research: Research questions, hypotheses and objectives. Canadian journal of surgery, 53(4):278–281, 2010. (https://pubmed.ncbi.nlm.nih.gov/20646403/)

67. FDA's Technology Modernization Action Plan, 18 September 2019. (https://www.fda.gov/about-fda/reports/fdas-technology-modernization-action-plan)

68. FDA Launches the Digital Health Center of Excellence, 22 September 2020. (https://www.fda.gov/news-events/press-announcements/fda-launches-digital-health-center-excellence)

69. U.S. Food and Drug Administration (FDA). Medical Devices; Current Good Manufacturing Practice Final Rule; Quality System Regulation. Federal Register, 61(195), 7 October 1996. (https://www.fda.gov/medical-devices/postmarket-requirements-devices/quality-system-qs-regulationmedical-device-good-manufacturing-practices)

70. U.S. Food and Drug Administration (FDA). General Principles of Software Validation; Final Guidance for Industry and FDA Staff, 11 January 2002. (https://www.fda.gov/regulatory-information/search-fda-guidance-documents/general-principles-software-validation)

71. U.S. Food and Drug Administration (FDA). Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, 11 May 2005. (https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-content-premarket-submissions-software-contained-medical-devices)

72. U.S. Food and Drug Administration (FDA). Medical Device Reporting Regulation History, 22 March 2018. (https://www.fda.gov/medical-devices/mandatory-reporting-requirements-manufacturers-importers-and-device-user-facilities/medical-device-reporting-regulation-history)

73. U.S. Food and Drug Administration (FDA). PMA Labeling, 27 September 2018. (https://www.fda.gov/medical-devices/premarket-approval-pma/pma-labeling)

74. U.S. Food and Drug Administration (FDA). Rregulatory Controls, 27 March 2018. (https://www.fda.gov/medical-devices/overview-device-regulation/regulatory-controls)

75. U.S. Food and Drug Administration (FDA). Statistical Assessment Methodology for Diagnostics and Biomarkers, 6 August 2018. (https://www.fda.gov/medical-devices/cdrh-research-programs/statistical-assessment-methodology-diagnostics-and-biomarkers)

76. U.S. Food and Drug Administration (FDA). Clinical Decision Support Software. Draft Guidance for Industry and Food and Drug Administration Staff, 27 September 2019. (https://www.fda.gov/media/109618/download)

77. U.S. Food and Drug Administration (FDA). De Novo Classification Request, 20 November 2019. (https://www.fda.gov/medical-devices/premarket-submissions/de-novo-classification-request)

78. U.S. Food and Drug Administration (FDA). General Wellness: Policy for Low Risk Devices. Guidance for Industry and Food and Drug Administration Staff, 27 September 2019. (https://www.fda.gov/media/90652/download)

79. U.S. Food and Drug Administration (FDA). Investigational Device Exemption (IDE), 13 December 2019. (https://www.fda.gov/medical-devices/how-study-and-market-your-device/investigational-device-exemption-ide)

80. U.S. Food and Drug Administration (FDA). Policy for Device Software Functions and Mobile Medical Applications. Guidance for Industry and Food and Drug Administration Staff, 27 September 2019. (https://www.fda.gov/media/80958/download)

81. U.S. Food and Drug Administration (FDA). Premarket Approval (PMA), 16 May 2019. (https://www.fda.gov/medical-devices/premarket-submissions/premarket-approval-pma)

82. U.S. Food and Drug Administration (FDA). Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback, 2 April 2019. (https://www.fda.gov/media/122535/download)

83. U.S. Food and Drug Administration (FDA). Regonized Consensus Standards 051,13-19, 14 January 2019. (https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfstandards/detail.cfm?standard__identification_no=38829)

84. U.S. Food and Drug Administration (FDA). Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data in Premarket Notification (510(k)) Submissions: Guidance for Industry and FDA Staff, 22 Jan 2020. (https://www.fda.gov/media/77642/download)

85. U.S. Food and Drug Administration (FDA). Digital Health Software Precertification (Pre-Cert) Program, 14 September 2020. (https://www.fda.gov/medical-devices/digital-health/digital-health-software-precertification-pre-cert-program)

86. U.S. Food and Drug Administration (FDA). Premarket Notification 510(k), 13 March 2020. (https://www.fda.gov/medical-devices/premarket-submissions/premarket-notification-510k)

87. U.S. Food and Drug Administration (FDA). Cybersecurity Guidelines, Accessed February 2021. (https://www.fda.gov/medical-devices/digital-health-center-excellence/cybersecurity)

88. U.S. Food and Drug Administration (FDA): Center for Devices and Radiological Health. Design Control Guidance for Medical Device Manufacturers, 11 March 1997. (https://www.fda.gov/media/116573/download)

89. U.S. Food and Drug Administration (FDA): Center for Devices and Radiological Health. Applying Human Factors and Usability Engineering to Medical Devices; Guidance for Industry and Food and Drug Administration Staff, 3 February 2016. (https://www.fda.gov/regulatory-information/search-fda-guidance-documents/applying-human-factors-and-usability-engineering-medical-devices)

90. U.S. Food and Drug Administration (FDA): Center for Devices and Radiological Health. Digital Health Innovation Plan, 2020. (https://www.fda.gov/media/106331/download)

91. U.S. Food and Drug Administration (FDA): Center for Devices and Radiological Health. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, January 2021. (https://www.fda.gov/media/145022/download)

92. U.S. Food and Drug Administration (FDA): Center for Devices and Radiological Health. Software as Medical Device (SAMD): Clinical Evaluation. Guidance for Industry and Food and Drug Administration Staff, 8 December 2017. This is a re-issue of IMDRF/SaMD WG/N41. (https://www.fda.gov/medical-devices/digital-health/software-medical-device-samd)

93. Figma, (Accessed January 2021). (https://www.figma.com/)

94. R.A. Fisher. Statistical methods for research workers. Edinburgh Oliver & Boyd, 1925.

95. K. Forsberg and H. Mooz. The Relationship of Systems Engineering to the Project Cycle. In First Annual Symposium of the National Council On Systems Engineering (NCOSE), October 1991.

96. A. Gawande. Why Doctors hate their computers. The New Yorker, 5 November 2018. (https://www.newyorker.com/magazine/2018/11/12/why-doctors-hate-their-computers)

97. D. Gladstone and L. Gladstone. Venture Capital Investing: The Complete Handbook for Investing in Private Businesses for Outstanding Profits. FT Prentice-Hall, 2004. (https://www.amazon.com/Venture-Capital-Investing-Businesses-Outstanding/dp/013101885X)

98. E. Glazer, D. Seetharaman, and A. Corse. The Shoestring App Developer Behind the Iowa Caucus Debacle. The Wall Street Journal, 5 February 2020. (https://www.wsj.com/articles/the-shoestring-app-developer-behind-the-iowa-caucus-debacle-11580904037)

99. A. Goldstein. HHS failed to heed many warnings that HealthCare.gov was in trouble. The Washington Post, 23 February 2016. (http://wapo.st/1oZmeTF?tid=ss_tw)

100. I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016. (https://www.deeplearningbook.org/)

101. Google Patents, (Accessed January 2021). (https://patents.google.com/)

102. W.E.L Grimson, R. Kikinis, F. Jolesz, and P. McL. Black. Image Guided Surgery. Scientific American, pages 62--69, June 1999. (https://www.scientificamerican.com/article/image-guided-surgery/)

103. C. Halton. Y2K. Investopedia, 18 March 2020. (https://www.investopedia.com/terms/y/y2k.asp)

104. M. Hampson, D. Scheinost, M. Qiu, J. Bhawnani, C. M. Lacadie, J. F. Leckman, R. T. Constable, and X. Papademetris. Biofeedback of real-time functional magnetic resonance imaging data from the supplementary motor area reduces functional connectivity to subcortical regions. Brain Connect, 1(1):91--98, 2011. (https://pubmed.ncbi.nlm.nih.gov/22432958/)

105. X. He and E. Frey. ROC, LROC, FROC, AFROC: an alphabet soup. J Am Coll Radiol, 6(9):652--655, Sep 2009. (https://doi.org/10.1016/j.jacr.2009.06.001)

106. M. L. Head, L. Holman, R. Lanfear, A. T. Kahn, and M. D. Jennions. The extent and consequences of p-hacking in science. PLoS Biology, 13(3), 2015. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4359000)

107. Health Level Seven International. Health Level 7, (Accessed January 2021). (https://www.hl7.org/)

108. R. M. Henig. The Dalkon Shield Disaster. The Washington Post, 17 November 1985. (https://www.washingtonpost.com/archive/entertainment/books/1985/11/17/the-dalkon-shield-disaster/6c58f354-fa50-46e5-877a-10d96e1de610/)

109. I. Hernandez, A. San-Juan-Rodriguez, C. B. Good, and W. F. Gellad. Changes in List Prices, Net Prices, and Discounts for Branded Drugs in the US, 2007-2018. JAMA, 323(9):854--862, March 2020. (https://www.ncbi.nlm.nih.gov/pubmed/30589626)

110. How Stuff Works. How Causes Work: The Iowa Caucus, (Accessed January 2021). (https://people.howstuffworks.com/question7211.htm)

111. Singapore Health Sciences Authority (HSA). Regulatory Guidelines for Software Medical Devices – A Lifecycle Approach, December 2019. (https://www.hsa.gov.sg/docs/default-source/announcements/regulatory-updates/regulatory-guidelines-for-software-medical-devices--a-lifecycle-approach.pdf)

112. H.K. Huang. Short history of PACS. Part I: USA. European Journal of Radiology, 78(2):163 -- 176, 2011. (https://pubmed.ncbi.nlm.nih.gov/21440396/)

113. S. B. Hulley, S. R. Cummings, W. S. Browner, D. G. Grady, and T. B. Newman. Designing Clinical Research. Wolters Kluwer, 2013. (https://www.amazon.com/Designing-Clinical-Research-Stephen-Hulley/dp/0781782104)

114. P. Hunter. The big health data sale: As the trade of personal health and medical data expands, it becomes necessary to improve legal frameworks for protecting patient anonymity, handling consent and ensuring the quality of data. EMBO Rep, 17(8):1103--1105, 08 2016. (https://www.ncbi.nlm.nih.gov/pubmed/27402546)

115. P. S. Hussey, S. Wertheimer, and A. Mehrotra. The Association Between Health Care Quality and Cost: A Systematic Review. Ann Internal Med, 158(1):27--34, January 2013. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4863949/)

116. IDEO. Human Centered Design Toolkit. IDEO, second edition, 1 July 2011.

117. International Electrotechnical Commission (IEC). IEC 62304 Medical device software -- Software life cycle processes. Geneva, CH, May 2006. (https://webstore.ansi.org/Standards/IEC/IEC62304Ed2006)

118. Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/IEC 62304:2006/A1:2016 Medical device software -- Software life cycle processes. (Amendment). Washington, DC, 2016. This amendment of the IEC 62304 standard was produced by the AAMI and it is an American National Standard. (https://webstore.ansi.org/Standards/IEC/IEC62304AmdEd2015)

119. International Electrotechnical Commission (IEC). IEC 62366-1 Medical devices – Part 1: Application of usability engineering to medical devices. Geneva, CH, February 2015. (https://webstore.ansi.org/Standards/IEC/IEC62366Ed2015)

120. International Electrotechnical Commission (IEC). IEC 62366-1 Medical devices – Part 1: Application of usability engineering to medical devices (Amendment 1). Geneva, CH, June 2020. (https://webstore.ansi.org/Standards/IEC/IEC62366Ed2020)

121. International Electrotechnical Commission (IEC). IEC 62366-2 Medical devices – Part 2: Guidance on the application of usability engineering to medical devices. Geneva, CH, April 2016. (https://webstore.ansi.org/Standards/IEC/IEC62366TRMedicalDevices)

122. International Medical Devices Regulator Forum (IMDRF): SaMD Working Group. Software as a Medical Device (SaMD): Key Definitions, 9 December 2013. IMDRF/SaMD WG/N10. (http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-definitions-140901.pdf)

123. International Medical Devices Regulator Forum (IMDRF): SaMD Working Group. Software as Medical Device: Possible Framework for Risk Categorization and Corresponding Considerations, 18 September 2014. IMDRF/SaMD WG/N12. (http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-140918-samd-framework-risk-categorization-141013.pdf)

124. International Medical Devices Regulator Forum (IMDRF): SaMD Working Group. Software as a Medical Device (SaMD): Application of Quality Management System, 2 October 2015. IMDRF/SaMD WG/N23. (http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-151002-samd-qms.pdf)

125. International Medical Devices Regulator Forum (IMDRF): Management Committee. Statement regarding Use of IEC 62304:2006 ``Medical device software -- Software life cycle processes'', 2 October 2015. IMDRF/MC/N35. (http://www.imdrf.org/docs/imdrf/final/procedural/imdrf-proc-151002-medical-device-software-n35.pdf)

126. International Medical Devices Regulator Forum (IMDRF): Medical Device Cybersecurity Working Group. Principles and Practices for Medical Device Cybersecurity, 18 March 2020. IMDRF/CYBER WG/N60. (http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-200318-pp-mdc-n60.pdf)

127. International Medical Devices Regulator Forum (IMDRF): Medical Device Clinical Evaluation Working Group. Clinical Investigation, 10 October 2019. IMDRF MDCE WG/N57. (http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-191010-mdce-n57.pdf)

128. India Ministry of Health and Family Welfare. Medical Device Rules, 31 January 2017.

129. Institute for Human Data Science (IQVIA). The Growing Value of Digital Health: Evidence and Impact on Human Health and the Healthcare System, 7 November 2017. (https://www.iqvia.com/insights/the-iqvia-institute/reports/the-growing-value-of-digital-health)

130. F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, December 2020.

131. International Standards Organization (ISO). ISO:9001 Quality Management Systems. Geneva, CH, fifth edition, 2015. (https://webstore.ansi.org/Standards/ISO/ISO90012015)

132. International Standards Organization (ISO). ISO:13485 Medical devices -- Quality management systems -- Requirements for regulatory purposes. Geneva, CH, 2016. (https://webstore.ansi.org/Standards/ISO/ISO134852016)

133. International Standards Organization (ISO). ISO:14971:2019 Medical devices -- Application of risk management to medical devices. Geneva, CH, 2019. (https://webstore.ansi.org/Standards/ISO/ISO149712019)

134. International Standards Organization (ISO). ISO/IEC/IEEE:90003 Software Engineering — Guidelines for the application of ISO 9001:2015 to computer software. Geneva, CH, first edition, 2018. (https://standards.ieee.org/standard/90003-2018.html)

135. International Standards Organization (ISO). Friendship among equals. Recollections from ISO's first fifty years, 1997. (https://www.iso.org/files/live/sites/isoorg/files/about%20ISO/docs/en/Friendship_among_equals.pdf)

136. Janssen Leverages Wearable Technology to Reimagine Clinical Trial Design, 16 November 2019. (https://www.prnewswire.com/news-releases/janssen-leverages-wearable-technology-to-reimagine-clinical-trial-design-300959419.html)

137. Government of Japan. Act on Securing Quality, Efficacy and Safety of Products Including Pharmaceuticals and Medical Devices, 1969.

138. A. Jarvis and P. Palmes. ISO 9001:2015: Understand, Implement, Succeed! Addison-Wesley, 2015. (https://www.amazon.com/ISO-9001-Understand-Implement-Succeed/dp/0134524438/)

139. Johnson & Johnson Launches Heartline the First-of-its-Kind, Virtual Study Designed to Explore if a New iPhone App and Apple Watch Can Help Reduce the Risk of Stroke, 25 Feburary 2020. (https://www.prnewswire.com/news-releases/johnson--johnson-launches-heartline-the-first-of-its-kind-virtual-study-designed-to-explore-if-a-new-iphone-app-and-apple-watch-can-help-reduce-the-risk-of-stroke-301010792.html)

140. Johner Institute North America, (Accessed January 2021). (https://johner-institute.com/)

141. E. M. Johnson. Factbox: Key changes to Boeing's 737 MAX after fatal crashes. Reuters, 17 November 2020. (https://www.reuters.com/article/us-boeing-737max-changes-factbox/factbox-key-changes-to-boeings-737-max-after-fatal-crashes-idUSKBN27X33M)

142. N. Kano, S. Nobuhiku, Fumio T., and Shinichi T. Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control (in Japanese), 14(2):39–48, April 1984.

143. B. Kappe. Accelerating Medical Product Development: Applying Agile Methods to Shorten Timelines, Reduce Risk and Improve Quality. Orthogonal, 2020. (http://orthogonal.io/insights/)

144. D. Kelley and T. Kelley. Creative Confidence: Unleashing the Creative Potential Within Us All. William Collins, 2013. (https://www.amazon.com/Creative-Confidence-Unleashing-Potential-Within/dp/038534936X)

145. D. Kidder and H. Hindi. The Startup Playbook: Secrets of the Fastest-Growing Startups from Their Founding Entrepreneurs. Chronicle Books, 2012. (https://www.amazon.com/Startup-Playbook-Fastest-Growing-Startups-Entrepreneurs/dp/1452105049)

146. J. Koebler and E. Mailberg. Here's the Shadow Inc. App That Failed in Iowa Last Night. Vice, 4 Feburary 2020. (https://www.vice.com/en/article/y3m33x/heres-the-shadow-inc-app-that-failed-in-iowa-last-night)

147. G. Kolata. The Sad Legacy of the Dalkon Shield. The New York Times, 6 December 1987. (https://www.nytimes.com/1987/12/06/magazine/the-sad-legacy-of-the-dalkon-shield.html)

148. D. Kopf. The Guinness Brewer Who Revolutionized Statistics, (Accessed January 2021). (https://priceonomics.com/the-guinness-brewer-who-revolutionized-statistics/)

149. Korean Software Testing Qualifications Board and Chinese Software Testing Qualifications Board. Certified Tester: AI Testing - Testing AI-Based Systems (AIT – TAI). Foundation Level Syllabus, 2019. (https://imbus.cn/upFile/Uploadfiles/AI%20Testing_Testing%20AI-Based%20System%20Syllabus%20v1.3.pdf)

150. Republic of Korea Ministry of Food and Drug Safety. Regulations, Accessed February 2021. (https://www.mfds.go.kr/eng/brd/m_40/list.do)

151. Republic of Korea Ministry of Food and Drug Safety: Medical Device Evaluation Department. Guideline on Review and Approval of Artificial Intelligence(AI) and big data-based Medical Devices (For Industry), 4 November 2020. (https://www.mfds.go.kr/eng/brd/m_40/down.do?brd_id=eng0011&seq=72623&data_tp=A&file_seq=1)

152. G. Kurian, editor. A Historical Guide to the U.S. Government. Oxford University Press, 1988. (https://www.amazon.com/Historical-Guide-U-S-Government/dp/0195102304)

153. J. J. Laffont and J. Tirole. The politics of government decision-making: A theory of regulatory capture. The quarterly journal of economics, 106(4):1089--1127, 1991. (https://www.jstor.org/stable/2937958)

154. Y. LeCun, Y. Bengio, and G. Hinton. Deep Learning. Nature, 521(7553):436--444, 2015. (https://pubmed.ncbi.nlm.nih.gov/26017442/)

155. G. Lee and J. Brumer. Managing Mission-Critical Government Software Projects: Lessons Learned from the HealthCare.gov Project, 2017. (https://www.businessofgovernment.org/sites/default/files/Viewpoints%20Dr%20Gwanhoo%20Lee.pdf)

156. N. G. Leveson. Safeware: System Safety and Computers. Addison-Wesley, 1995. (http://sunnyday.mit.edu/book.html)

157. N. G. Leveson. The Therac-25: 30 Years Later. Computer, 50(11):8--11, 2017. (https://ieeexplore.ieee.org/document/8102762)

158. N. G. Leveson and C. S. Turner. An investigation of the Therac-25 accidents. Computer, 26(7):18--41, 1993. (https://ieeexplore.ieee.org/document/274940)

159. C. Linnane. Teladoc-Livongo $18.5 billion merger is a huge step forward for digital health, analysts say. MarketWatch, 6 August 2020. (https://www.marketwatch.com/story/teladoc-livongo-185-billion-merger-is-a-huge-step-forward-for-digital-health-analysts-say-2020-08-05)

160. B. Littlewood and L. Strigini. The Risks of Software. Scientific American, pages 62--75, November 1992. (https://www.scientificamerican.com/article/the-risks-of-software/)

161. Z. Loeb. The lessons of Y2K, 20 years later: Y2K became a punchline, but 20 years ago we averted disaster. The Washington Post, 30 December 2019. (https://www.washingtonpost.com/outlook/2019/12/30/lessons-yk-years-later/)

162. S. Lohr. For Big-Data Scientists, `Janitor Work' is Key Hurdle to Insights. The New York Times, August 2014. (https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html)

163. E. N. Lorenz. The Essence of Chaos (Jessie and John Danz Lectures). University of Washington Press, first edition, 1995. (https://www.amazon.com/Essence-Chaos-Jessie-John-Lectures/dp/0295975148)

164. A. Loten. Testing Could Have Prevented Iowa Caucus App Failure, Experts Say. The Wall Street Journal, 4 February 2020. (https://www.wsj.com/articles/testing-could-have-prevented-iowa-caucus-app-failure-experts-say-11580856659)

165. J. Lynch. The Worst Computer Bugs in History: Rapid unanticipated disassembly of the Mars Climate Orbiter. bugsnug, 12 September 2017. (https://www.bugsnag.com/blog/bug-day-mars-climate-orbiter)

166. N. MacDonald. Y2K 20 Years Later: An oral history of the final moments of the 20th century at Seattle City Light, 20 December 2019. (https://powerlines.seattle.gov/2019/12/20/y2k/)

167. V. Martindale and A. Menache. The PIP scandal: an analysis of the process of quality control that failed to safeguard women from the health risks. J R Soc Med, 106(5):173--177, 05 2013. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3676226/)

168. Mayo Clinic. Radiation Therapy, (Accessed January 2021). (https://www.mayoclinic.org/tests-procedures/radiation-therapy/about/pac-20385162)

169. E. McCallister, T. Grance, and K. Scarfone. National Institute of Standards and Technology (NIST) Special Publication 800-122: Guide to Protecting the Confidentiality of Personally Identifiable Information (PII), April 2020. (https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-122.pdf)

170. J. McCarthy. What is Artificial Inteligence?, 12 November 2007. (http://jmc.stanford.edu/articles/whatisai/whatisai.pdf)

171. J. J. McGough and S. V Faraone. Estimating the size of treatment effects: moving beyond p values. Psychiatry, 6(10):21--29, 2009. (https://pubmed.ncbi.nlm.nih.gov/20011465/)

172. M. Meadows. Promoting Safe and Effective Drugs for 100 Years. FDA Consumer magazine: The Centennial Edition, January-February 2006. (https://www.fda.gov/about-fda/histories-product-regulation/promoting-safe-effective-drugs-100-years)

173. Medical Imaging and Technology Alliance. DICOM: Digital Imaging and Communications in Medicine, (Accessed January 2021). (https://www.dicomstandard.org/current/)

174. Medical Imaging and Technology Alliance. DICOMWeb: DICOM standard for web-based medical imaging, (Accessed January 2021). (https://www.dicomstandard.org/dicomweb/)

175. Medtronic Surgical Navigation Technologies, Louisville, CO. Medtronic StealthStation Surgical Navigation System, (Accessed January 2021). (https://www.medtronic.com/us-en/healthcare-professionals/products/neurological/surgical-navigation-systems/stealthstation.html)

176. U.K. Medicines & Healthcare products Regulatory Agency (MHRA). Human Factors and Usability Engineering -- Guidance for Medical Devices Including Drug-device Combination Products, September 2017. (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/645862/HumanFactors_Medical-Devices_v1.0.pdf)

177. U.K. Medicines & Healthcare products Regulatory Agency (MHRA). Guidance: Medical device stand-alone software including apps (including IVDMDs), November 2020. (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/957090/Software_flow_chart_Ed_1-07b-UKCA__002__FINAL.pdf)

178. D. Michaels, A. Tangel, and A. Pasztor. Boeing Reaches $2.5 Billion Settlement of U.S. Probe Into 737 MAX Crashes: Agreement with Justice Department allows aerospace giant to avoid prosecution. The Wall Street Journal, 7 January 2021. (https://www.wsj.com/articles/boeing-reaches-2-5-billion-settlement-of-u-s-probe-into-737-max-crashes-11610054729)

179. Microsoft. Threat modeling, (Accessed July 2022). (https://www.microsoft.com/en-us/securityengineering/sdl/threatmodeling)

180. MONAI: Medical Open Network for AI, (Accessed January 2021). (https://monai.io/)

181. B. Montgomery. Closed Loop Systems: Future Treatment for Diabetes?, 3 June 2020. (https://www.thediabetescouncil.com/closed-loop-systems-future-treatment-for-diabetes/)

182. E. Morath, J. Hilsenrath, and S. Chaney. Record Rise in Unemployment Claims Halts Historic Run of Job Growth. The Wall Street Journal, 26 March 2020. (https://www.wsj.com/articles/the-long-run-of-american-job-growth-has-ended-11585215000)

183. T. Moynihan. Samsung Finally Reveals Why the Note 7 Kept Exploding. Wired, 22 Janury 2017. (https://www.wired.com/2017/01/why-the-samsung-galaxy-note-7-kept-exploding/)

184. R. Mullin. Tufts Study Finds Big Rise In Cost Of Drug Development. Chemical and Engineering News, 20 Novemberr 2014. (https://cen.acs.org/articles/92/web/2014/11/Tufts-Study-Finds-Big-Rise.html)

185. Mishap Investigation Board Phase I Report. Mars Climate Orbiter, November 1999. (https://llis.nasa.gov/llis_lib/pdf/1009464main1_0641-mr.pdf)

186. National Aeronautics and Space Administration (NASA). Mars Mission, Press Kit, December 1998. (https://www.jpl.nasa.gov/news/press_kits/mars98launch.pdf)

187. National Cancer Institute. External Beam Radiation Therapy to Treat Cancer, (Accessed January 2021). (https://www.cancer.gov/about-cancer/treatment/types/radiation-therapy/external-beam)

188. National Cancer Institute. Radiation Therapy to Treat Cancer, (Accessed January 2021). (https://www.cancer.gov/about-cancer/treatment/types/radiation-therapy)

189. CBS News. The phishing email that hacked the account of John Podesta, 28 October 2016. (https://www.cbsnews.com/news/the-phishing-email-that-hacked-the-account-of-john-podesta/)

190. National Institute of Standards and Technology (NIST). Framework for Improving Critical Infrastructure Cybersecurity, 16 April 2018. (https://www.nist.gov/cyberframework)

191. China National Medical Products Administration (NMPA). Rules for Classification of Medical Devices: (Decree No. 15 of China Food and Drug Administration), 10 November 2019. (http://subsites.chinadaily.com.cn/nmpa/2019-10/11/c_415411.htm)

192. China National Medical Products Administration (NMPA). Technical Guideline on AI-Aided Software, June 2019. We used (and verified) an unofficial translation made available through the webpage of the consulting company ChinaMed Device. (https://chinameddevice.com/)

193. The Novartis Biome: A catalyst for impactful digital collaboration, (Accessed January 2021). (https://www.novartis.com/our-science/novartis-biome)

194. R. Nunn, J. Parsons, and J. Shambaugh. A Dozen Facts about the Economics of the U.S. Health-Care System, March 2020. (https://www.brookings.edu/research/a-dozen-facts-about-the-economics-of-the-u-s-health-care-system/)

195. The Nuremberg Code. Trials of War Criminals before the Nuremberg Military Tribunals under Control Council Law No. 10, 1949. (https://history.nih.gov/display/history/Nuremberg+Code)

196. J. Oberg. Why the Mars probe went off course [accident investigation]. IEEE Spectrum, 36(12):34--39, December 1999. (https://ieeexplore.ieee.org/document/809121)

197. Organization for Economic Co-operation and Development (OECD). Improving Healthcare Quality in Europe: Characteristics, Effectiveness and Implementation of Different Strategies, 17 October 2019. (https://www.oecd.org/els/improving-healthcare-quality-in-europe-b11a6e8f-en.htm)

198. Organization for Economic Co-operation and Development (OECD) and The European Union. Health at a Glance: Europe 2016, 2016. (https://www.oecd-ilibrary.org/content/publication/9789264265592-en)

199. OFFIS e.V. DCMTK -- Dicom Toolkit, (Accessed January 2021). (https://dicom.offis.de/dcmtk.php.en)

200. Otsuka and Proteus Announce the First U.S. FDA Approval of a Digital Medicine System: Abilify Mycite (aripiprazole tablets with sensor), press release, 14 November 2017. (https://www.proteus.com/press-releases/otsuka-and-proteus-announce-the-first-us-fda-approval-of-a-digital-medicine-system-abilify-mycite/)

201. A. Panny, K. Krueger, and A. Acharya. Achieving the `True' Triple Aim in Healthcare. In Acharya A., Powell V., Torres-Urquidy M., Posteraro R., and Thyvalikakath T., editors, Integration of Medical and Dental Care and Patient Data, pages 11--32. Springer, 01 2019. (https://www.springer.com/gp/book/9783319982960)

202. X. Papademetris, C. DeLorenzo, S. Flossmann, M. Neff, K.P. Vives, D.D. Spencer, L.H. Staib, and J.S. Duncan. From medical image computing to computer-aided intervention: development of a research interface for image-guided navigation. Int J Med Robot, 5(2):147--157, June 2009. (https://pubmed.ncbi.nlm.nih.gov/19301361/)

203. N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami. The Limitations of Deep Learning in Adversarial Settings. In 2016 IEEE European Symposium on Security and Privacy (EuroS P), pages 372--387, 2016. (https://arxiv.org/abs/1511.07528)

204. A. Papoulis and S. U. Pillai. Probability, Random Variables, and Stochastic Processes. McGraw Hill, Boston, fourth edition, 2002. (https://www.amazon.com/Probability-Random-Variables-Stochastic-Processes/dp/0071226613)

205. A. Pasztor. Boeing Finds New Software Problem That Could Complicate 737 MAX's Return. The Wall Street Journal, 17 January 2020. (https://www.wsj.com/articles/boeing-finds-new-software-problem-that-could-complicate-737-max-return-11579290347)

206. A. Pasztor. Congressional Report Faults Boeing on MAX Design, FAA for Lax Oversight. The Wall Street Journal, 6 March 2020. (https://www.wsj.com/articles/congressional-report-says-max-crashes-stemmed-from-boeings-design-failures-and-lax-faa-oversight-11583519145)

207. T. M. Pawlik and J. A. Sosa, editors. Clinical Trials. Springer, 2nd edition, 2020.

208. Pear Obtains FDA Clearance of the First Digital Therapeutic to Treat Disease, press release, 14 September 2017. (https://peartherapeutics.com/fda-obtains-fda-clearance-first-prescription-digital-therapeutic-treat-disease/)

209. J. Pearl and D. Mackenzie. The Book of Why: The new science of cause and effect. Basic Books, New York, NY, 2018. (https://www.amazon.com/Book-Why-Science-Cause-Effect-ebook/dp/B075CR9QBJ)

210. PERFORCE. Helix RM, Accessed March 2021. (https://www.perforce.com/products/helix-requirements-management)

211. Pharmaceutical Group of the European Union. PGEU Statement: eHealth Solutions in European Community Pharmacies, November 2016. (https://www.pgeu.eu/wp-content/uploads/2019/07/161102E-PGEU-Statement-on-eHealth-Final.pdf)

212. O. S. Pianykh. Digital Imaging and Communications in Medicine (DICOM): A Practical Introduction and Survival Guide. Springer, 2nd edition, 2012. (https://www.amazon.com/Digital-Imaging-Communications-Medicine-DICOM/dp/3642108490)

213. J. J. Plecs and J. H. Cochrane. Imagine What We Could Cure if draconian privacy regulations didn't keep key data from medical researchers. The Wall Street Journal, 25 November 2018. (https://www.wsj.com/articles/imagine-what-we-could-cure-1543176157)

214. PyTorch, (Accessed January 2021). (https://pytorch.org/)

215. Radiopaedia, (Accessed January 2021). (http://www.radiopaedia.com)

216. R. Rasmussen, T. Hughes, J. R. Jenks, and J. Skach. Adopting Agile in an FDA Regulated Environment. In Agile Conference, pages 151--155, 2009. (https://ieeexplore.ieee.org/document/5261092)

217. F. Redmill and J. Rajan. Human Factors in Safety-Critical Systems. Butterworth-Heinemann, 1997. (https://www.amazon.com/Human-Factors-Safety-Critical-Systems-Redmill/dp/0750627158)

218. S. Robertson and J. Robertson. Mastering the Requirements Process: Getting Requirements Right. Addison-Wesley Professional, 3rd edition, 2012. (https://www.amazon.com/Mastering-Requirements-Process-Getting-Right/dp/0321815742)

219. O. Ronneberger, P. Fischer, and T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. In N. Navab, J. Hornegger, W. Wells, and A. Frangi, editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI. Springer, 2015. (https://doi.org/10.1007/978-3-319-24574-4_28)

220. Y. Ronquillo, A. Meyers, and S. J. Korvek. Digital Health. StatPearls (Internet), July 4 2020. (https://www.ncbi.nlm.nih.gov/books/NBK470260/)

221. B. W. Rose. Fatal Dose - Radiation Deaths linked to AECL Computer Errors, 1994. (http://www.ccnr.org/fatal_dose.html)

222. D. S. Rose. Angel Investing: The Gust Guide to Making Money and Having Fun Investing in Startups. Wiley, 2014. (https://www.amazon.com/Angel-Investing-Making-Having-Startups/dp/1118858255)

223. L. Rosen. Open Source Licensing: Software Freedom and Intellectual Property Law. Prentice Hall, 2004. (https://www.amazon.com/Open-Source-Licensing-Software-Intellectual/dp/0131487876/)

224. L. Rothman. Remember Y2K? Here's How We Prepped for the Non-Disaster. Time, 31 December 2014. (https://time.com/3645828/y2k-look-back/)

225. W. Royce. Managing the development of large software systems: concepts and techniques. In IEEE WESCON, page 1–9, 1970. Reprinted in ICSE'87 proceedings, pages 328–338. (https://leadinganswers.typepad.com/leading_answers/files/original_waterfall_paper_winston_royce.pdf)

226. W. Royce. TRW's Ada Process Model for incremental development of large software systems. In [1990] Proceedings. 12th International Conference on Software Engineering, pages 2--11, 1990.

227. K. S. Rubin. Essential Scrum: A Practical Guide to the Most Popular Agile Process. Addison-Wesley, 1st edition, 2012. (https://www.amazon.com/Essential-Scrum-Practical-Addison-Wesley-Signature/dp/0137043295)

228. C. Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206--215, 2019. (https://arxiv.org/abs/1811.10154)

229. T. Russotto and B. T. Wilson. Who's to Blame for Boeing's 737 MAX Saga? The Wall Street Journal, 29 December 2019. (https://www.wsj.com/articles/congressional-report-says-max-crashes-stemmed-from-boeings-design-failures-and-lax-faa-oversight-11583519145)

230. B. Sahiner, B. Friedman, C. Linville, C. Ipach, E. Montgomery, E. Steinle Alexander, and et al. Perspectives and Good Practices for AI and Continuously Learning Systems in Healthcare, Summer 2018. (https://www.exhibit.xavier.edu/health_services_administration_faculty/21/)

231. R. Salay and K. Czarnecki. Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262. CoRR, abs/1808.01614, 2018. (http://arxiv.org/abs/1808.01614)

232. E. Sauerwein, F. Bailom, K. Matzler, and H. H. Hinterhuber. The kano model: How to delight your customers. In International working seminar on production economics, volume 1, pages 313--327. is.muni.cz, 1996.

233. D. Scheinost, M. Hampson, M. Qiu, J. Bhawnani, R. T. Constable, and X. Papademetris. A graphics processing unit accelerated motion correction algorithm and modular system for real-time fMRI. Neuroinformatics, 11(3):291--300, July 2013. (https://pubmed.ncbi.nlm.nih.gov/23319241/)

234. The United States Senate: Special Committee on the Year 2000 Technology Problem. Investigating the Impact of the Year 2000 Problem, 24 February 1999. (https://www.govinfo.gov/content/pkg/GPO-CPRT-106sprt10/pdf/GPO-CPRT-106sprt10.pdf)

235. R. Siegel. What If Healthcare Could Start With Technology? -- Bernard Tyson, CEO Kaiser Permanente. The Industrialist's Dilemma, 11 February 2016. (https://medium.com/the-industrialist-s-dilemma/what-if-healthcare-could-start-with-technology-bernard-tyson-ceo-kaiser-permanente-5052658a6212)

236. D. Simas. Why We Passed the Affordable Care Act in the First Place? The White House: President Barack Obama, 30 October 2013. (https://obamawhitehouse.archives.gov/blog/2013/10/30/why-we-passed-affordable-care-act-first-place)

237. 3D-Slicer -- Self-Test Module, (Accessed January 2021). (https://www.slicer.org/wiki/Documentation/Nightly/Developers/Tutorials/SelfTestModule)

238. P. Spence. When the human body is the biggest data platform, who will capture value? EY, 17 May 2018. (https://www.ey.com/en_us/digital/when-the-human-body-is-the-biggest-data-platform-who-will-capture-value)

239. R. Spronk. Ringholm Whitepaper: HL7 Message examples: version 2 and version 3, 16 November 2007. Final version 1.0 (http://www.ringholm.de/docs/04300_en.htm)

240. Stanford Medicine News Center. Through Apple Heart Study, Stanford Medicine researchers show wearable technology can help detect atrial fibrillation, 13 November 2019. (https://med.stanford.edu/news/all-news/2019/11/through-apple-heart-study--stanford-medicine-researchers-show-we.html)

241. I. Stanley-Becker and M. Scherer. Iowa Democrats kept their App secret to prevent hacks. Instead, they got confusion and chaos. The Washington Post, 4 Feburary 2020. (https://www.washingtonpost.com/politics/iowa-democrats-kept-their-app-secret-to-prevent-hacks-instead-they-got-confusion-and-chaos/2020/02/04/fbd99654-4784-11ea-bc78-8a18f7afcee7_story.html)

242. N. Statt. Apple confirms cloud gaming services like xCloud and Stadia violate App Store guidelines: New cloud gaming services from Google and Microsoft won't work on iOS. The Verge, 6 August 2020. (https://www.theverge.com/2020/8/6/21357771/apple-cloud-gaming-microsoft-xcloud-google-stadia-ios-app-store-guidelines-violations)

243. N. Statt. The App that broke the Iowa Caucuses was sent out through beta testing platforms. The Verge, 4 February 2020. (https://www.theverge.com/2020/2/4/21122737/iowa-democractic-caucus-voting-app-android-testfairy-screenshots-app-store)

244. S. Stern. Pillpack. MIT Case Study, MIT Sloan School of Management, (unknown).

245. J. P. Swann. FDA's Origin, 1 February 2018. U.S. Food and Drug Administration (FDA): History Office, (https://www.fda.gov/about-fda/fdas-evolving-regulatory-powers/fdas-origin)

246. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus. Intriguing properties of neural networks. In Yoshua Bengio and Yann LeCun, editors, 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014. (https://arxiv.org/abs/1312.6199)

247. H. Takeuchi and I. Nonaka. The New New Product Development Game. Harvard Business Review, January 1986. (https://hbr.org/1986/01/the-new-new-product-development-game)

248. N. N. Taleb. The Black Swan. Random House, New York, second edition, 2010. (https://www.amazon.com/Black-Swan-Second-Improbable-Incerto-ebook/dp/B00139XTG4)

249. N. N. Taleb. Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications. STEM Academic Press, 2020. (https://www.amazon.com/Statistical-Consequences-Fat-Tails-Preasymptotics/dp/1544508050/)

250. A. Tangel, A. Pasztor, and M. Maremont. The Four-Second Catastrophe: How Boeing Doomed the 737 MAX. The Wall Street Journal, 16 August 2019. (https://www.wsj.com/articles/the-four-second-catastrophe-how-boeing-doomed-the-737-max-11565966629)

251. Teladoc Health Inc. Teladoc Health Reports First Quarter 2019 Results, 30 April 2019. (https://www.globenewswire.com/news-release/2019/04/30/1813070/0/en/Teladoc-Health-Reports-First-Quarter-2019-Results.html)

252. TensorFlow, (Accessed January 2021). (https://www.tensorflow.org/)

253. M. Terry. The Median Cost of Bringing a Drug to Market is $985 Million, According to New Study. BioSpace.com, 4 March 2020. (https://www.biospace.com/article/median-cost-of-bringing-a-new-drug-to-market-985-million/)

254. Therapeutic Goods Administration (TGA), Department of Health, Australian Government. Actual and potential harm caused by medical software: A rapid literature review of safety and performance issues, July 2020. (https://www.tga.gov.au/resource/actual-and-potential-harm-caused-medical-software)

255. D. Thompson. Health Care Just Became the U.S.'s Largest Employer. The Atlantic, 9 January 2018. (https://www.theatlantic.com/business/archive/2018/01/health-care-america-jobs/550079)

256. E. Tjoa and C. Guan. A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI. CoRR, abs/1907.07374, 2019. (http://arxiv.org/abs/1907.07374)

257. E. Topol. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, illustrated edition edition, 2019. (https://www.amazon.com/Deep-Medicine-Artificial-Intelligence-Healthcare-ebook/dp/B07FMHFGLT)

258. E. Topol. The Topol Review: An independent report on behalf of the Secretary of State for Health and Social Care. NHS, Health Education England, February 2019. (https://topol.hee.nhs.uk/wp-content/uploads/HEE-Topol-Review-2019.pdf)

259. G. Travis. How the Boeing 737 Max Disaster Looks to a Software Developer. IEEE Spectrum, 18 April 2019. (https://spectrum.ieee.org/aerospace/aviation/how-the-boeing-737-max-disaster-looks-to-a-software-developer)

260. F. Trotter and D. Uhlman. Hacking Healthcare: A Guide to Standards, Workflows, and Meaningful Use. O'Reilly Media, 1st edition, 2011. (https://www.amazon.com/Hacking-Healthcare-Standards-Workflows-Meaningful/dp/1449305024)

261. T. C. Tsai, E. J. Orav, and K. E. Joynt. Disparities in surgical 30-day readmission rates for Medicare beneficiaries by race and site of care. Ann Surg, 259(6):1086--1090, June 2014. (https://www.ncbi.nlm.nih.gov/pubmed/16432363)

262. D.A. Vogel. Medical Device Software Verification, Validation, and Compliance. Artech House, 2011. (https://www.amazon.com/Medical-Software-Verification-Validation-Compliance/dp/1596934220)

263. Voluntis Announces Marketing Authorization for Oleena, First Digital Therapeutic in Oncology, press release, 31 July 2019. (http://voluntis.com/en/news/news-1/2019/voluntis-announces-market-authorization-for-oleena-first-digital-therapeutic-in-oncology)

264. D. Volz, T. Parti, A. Corse, and R. McMillan. Iowa's Tally-by-App Experiment Fails. The Wall Street Journal, 4 February 2020. (https://www.wsj.com/articles/iowa-caucus-results-delayed-by-apparent-app-issue-11580801699)

265. J. Vomhof Jr. Geek Squad Agents Reflect On 20th Anniversary Of Y2K, 30 December 2019. (https://corporate.bestbuy.com/geek-squad-agents-reflect-on-20th-anniversary-of-y2k/)

266. J. Weiner, C. Marks, and M. Pauly. Effects of the ACA on Health Care Cost Containment. Issue Brief. Leondard Davis Institute of Health Economics, University of Pennsylvania. (https://ldi.upenn.edu/brief/effects-aca-health-care-cost-containment, 2 March 2017)

267. R. C. Welsh, J. E. Hardee, and S. Peltier. Slice-time correction in resting-state (and fMRI) gone bad. In Organization of Human Brain Mapping Annual Meeting, June 2014.

268. World Health Organization (WHO), The Organization for Economic Co-Operation and Development (OECD), and The World Bank. Delivering Quality Health Services: A Global Imperative for Universal Health Coverage, 5 July 2018. (https://www.worldbank.org/en/topic/universalhealthcoverage/publication/delivering-quality-health-services-a-global-imperative-for-universal-health-coverage)

269. World Health Organization (WHO): Noncommunicable Diseases and Mental Health. Innovative Care for Chronic Conditions: Building Blocks for Action, 2002. (https://www.who.int/chp/knowledge/publications/icccreport/en/)

270. L. Williams, E. M. Maximilien, and M. Vouk. Test-driven development as a defect-reduction practice. In 14th International Symposium on Software Reliability Engineering, 2003. ISSRE 2003, pages 34--45. (https://ieeexplore.ieee.org/document/1251029)

271. C. M. Wilson, D. P. Schreiber, J. D. Russell, and P. Hitchcock. Electron Beam Versus Photon Beam Radiation Therapy for the Treatment of Orbital Lymphoid Tumors. Medical Dosimetry, 17(3):161 -- 165, 1992. (https://pubmed.ncbi.nlm.nih.gov/1388683/)

272. A. Wirth, C. Gates, and J. Smith. Medical Device Cybersecurity for Engineers and Manufacturers. Artech House, 2020. (https://www.amazon.com/Medical-Device-Cybersecurity-Engineers-Manufacturers/dp/1630818151)

273. World Medical Association. Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects, 1964--2013. (https://www.wma.net/wp-content/uploads/2016/11/DoH-Oct2013-JAMA.pdf)

274. P. Workman, G. F. Draetta, J. H. M. Schellens, and R. Bernards. How Much Longer Will We Put Up With $100,000 Cancer Drugs? Cell, 168(4):579--583, 02 2017. (https://www.ncbi.nlm.nih.gov/pubmed/28187281)

275. E. Wu, K. Wu, R. Daneshjou, D. Ouyang, D. E. Ho, and J. Zou. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med, Apr 2021. (https://pubmed.ncbi.nlm.nih.gov/33820998/)

276. Zacks Equity Research, January 2018.