Three Problems with Big Data and Artificial Intelligence in Medicine

Chin‐Yee, Benjamin; Upshur, Ross

doi:10.1353/pbm.2019.0012

Cited by 58 publications

(38 citation statements)

References 62 publications

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“…This is particularly true when data are collected for other purposes (e.g., administration, clinical care, or even other research studies with differently defined outcome measures (Lipworth et al 2017 ; Lipworth 2019 ). Add to this the other limitations of data analysis using artificial intelligence, such as flaws in (often non-transparent) algorithms (Chin-Yee and Upshur 2019 ), and it becomes clear why even the best “big data” research needs to be undertaken with the utmost care.…”

Section: Failures Of Methodological Rigourmentioning

confidence: 99%

Science at Warp Speed: Medical Research, Publication, and Translation During the COVID-19 Pandemic

Lipworth

Gentgall²,

Kerridge

et al. 2020

Bioethical Inquiry

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In response to the COVID-19 pandemic, there has been a rapid growth in research focused on developing vaccines and therapies. In this context, the need for speed is taken for granted, and the scientific process has adapted to accommodate this. On the surface, attempts to speed up the research enterprise appear to be a good thing. It is, however, important to consider what, if anything, might be lost when biomedical innovation is sped up. In this article we use the case of a study recently retracted from the Lancet to illustrate the potential risks and harms associated with speeding up science. We then argue that, with appropriate governance mechanisms in place (and adequately resourced), it should be quite possible to both speed up science and remain attentive to scientific quality and integrity. Keywords COVID-19. Pandemic. Research quality. Research governance. Research integrity. Biomedical publication The COVID-19 pandemic has changed the world in profound ways and led to significant shifts in our social, political, economic, and scientific priorities. Among these has been a major shift in both public and private research funding towards COVID-19-related projects, with the aim of mapping the pandemic and its effects and developing vaccines and therapies (London and Kimmelman 2020). In this context, the need for speed is taken for granted, and the scientific process has adapted to accommodate this. Rather than going through the usual research-dissemination-translation pathway, existing drugs are being "repurposed," usual preclinical testing regimes are being bypassed or shortened, study sizes are being reduced, and timeconsuming randomized controlled trials are being replaced or supplemented with observational studies. The results of research are already being reported mere months after the epidemic's onset-often prior to formal peer review or after so-called "rapid" review. And regulators are "fast tracking" their review of potentially promising drugs and vaccines (

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Section: Failures Of Methodological Rigourmentioning

confidence: 99%

Science at Warp Speed: Medical Research, Publication, and Translation During the COVID-19 Pandemic

Lipworth

Gentgall²,

Kerridge

et al. 2020

Bioethical Inquiry

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show abstract

“…According to them, this is one of the central philosophical challenges confronting big data and machine learning in medicine. 27 Similarly, in their 'Barcelona declaration for the proper development and usage of artificial intelligence in Europe' Sloane and Silva (2020) argue that decisions made by machine learning AI are often opaque due to the black box nature of the patterns derived by these techniques. This can lead to unacceptable bias.…”

Section: Explainable and Accountable Crss To Facilitate Interaction With The Clinicianmentioning

confidence: 99%

“…Chin‐Yee and Upshur (2019), for example, argue that because of the black‐box nature of CRSS, using these systems conflicts with clinicians' ethical and epistemic obligation to the patient. According to them, this is one of the central philosophical challenges confronting big data and machine learning in medicine 27 …”

Section: Cdss As Clinical Reasoning Support Systemsmentioning

confidence: 99%

From clinical decision support to clinical reasoning support systems

Baalen

Boon

Verhoef

2021

Evaluation Clinical Practice

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Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. Clinical decision making involves a reasoning process in which clinicians combine different types of information into a coherent and adequate 'picture of the patient' that enables them to draw explainable and justifiable conclusions for which they bear epistemological responsibility. Therefore, we suggest that it is more appropriate to think of a CDSS as clinical reasoning support systems (CRSS). Developing CRSS that support clinicians' reasoning process therefore requires that: (a) CRSSs are developed on the basis of relevant and well-processed data; and (b) the system facilitates an interaction with the clinician. Therefore, medical experts must collaborate closely with AI experts developing the CRSS. In addition, responsible use of an CRSS requires that the data generated by the CRSS is empirically justified through an empirical link with the individual patient. In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CRSSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians' task to interpret, integrate and contextualize.

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“…This presents those responsible for overseeing healthcare systems with a 'wicked problem', meaning that the problem has multiple causes, is hard to understand and define, and hence will have to be tackled from multiple different angles. Against this background, policymakers, politicians, clinical entrepreneurs and computer and data scientists increasingly argue that a key part of the solution will be 'Artificial Intelligence' (AI), particularly Machine Learning (Chin-Yee & Upshur, 2019). The argument stems not from the belief that all healthcare needs will soon be taken care of by "robot doctors" (Chin-Yee & Upshur, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Against this background, policymakers, politicians, clinical entrepreneurs and computer and data scientists increasingly argue that a key part of the solution will be 'Artificial Intelligence' (AI), particularly Machine Learning (Chin-Yee & Upshur, 2019). The argument stems not from the belief that all healthcare needs will soon be taken care of by "robot doctors" (Chin-Yee & Upshur, 2019). Instead, the argument rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques (summarised in Figure 1 below) that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human.…”

Section: Introductionmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley

Machado²,

Burr

et al. 2019

SSRN Journal

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Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be 'Artificial Intelligence' (AI)-particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by "robot doctors." Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients' health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

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Three Problems with Big Data and Artificial Intelligence in Medicine

Cited by 58 publications

References 62 publications

Science at Warp Speed: Medical Research, Publication, and Translation During the COVID-19 Pandemic

Science at Warp Speed: Medical Research, Publication, and Translation During the COVID-19 Pandemic

From clinical decision support to clinical reasoning support systems

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

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