Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship

Funer, Florian

doi:10.1007/s13347-022-00505-7

Cited by 7 publications

(1 citation statement)

References 44 publications

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“…In the first place, he or she needs a sufficient informational basis for the validation of AI support statement in individual case situations (cf. the debate on explicability, interpretability and transparency of AI tools for health care) (2,(25)(26)(27)(28).…”

Section: Discussing the Impact Of Ai Support On The Decision-making A...mentioning

confidence: 99%

Physician’s autonomy in the face of AI support: walking the ethical tightrope

Funer,

Wiesing

2024

Front. Med.

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The introduction of AI support tools raises questions about the normative orientation of medical practice and the need to rethink its basic concepts. One of these concepts that is central to the discussion is the physician’s autonomy and its appropriateness in the face of high-powered AI applications. In this essay, a differentiation of the physician’s autonomy is made on the basis of a conceptual analysis. It is argued that the physician’s decision-making autonomy is a purposeful autonomy. The physician’s decision-making autonomy is fundamentally anchored in the medical ethos for the purpose to promote the patient’s health and well-being and to prevent him or her from harm. It follows from this purposefulness that the physician’s autonomy is not to be protected for its own sake, but only insofar as it serves this end better than alternative means. We argue that today, given existing limitations of AI support tools, physicians still need physician’s decision-making autonomy. For the possibility of physicians to exercise decision-making autonomy in the face of AI support, we elaborate three conditions: (1) sufficient information about AI support and its statements, (2) sufficient competencies to integrate AI statements into clinical decision-making, and (3) a context of voluntariness that allows, in justified cases, deviations from AI support. If the physician should fulfill his or her moral obligation to promote the health and well-being of the patient, then the use of AI should be designed in such a way that it promotes or at least maintains the physician’s decision-making autonomy.

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Section: Discussing the Impact Of Ai Support On The Decision-making A...mentioning

confidence: 99%

Physician’s autonomy in the face of AI support: walking the ethical tightrope

Funer,

Wiesing

2024

Front. Med.

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Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

et al. 2023

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Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

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The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach

Funer

2022

Med Health Care and Philos

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Developments in Machine Learning (ML) have attracted attention in a wide range of healthcare fields to improve medical practice and the benefit of patients. Particularly, this should be achieved by providing more or less automated decision recommendations to the treating physician. However, some hopes placed in ML for healthcare seem to be disappointed, at least in part, by a lack of transparency or traceability. Skepticism exists primarily in the fact that the physician, as the person responsible for diagnosis, therapy, and care, has no or insufficient insight into how such recommendations are reached. The following paper aims to make understandable the specificity of the deliberative model of a physician-patient relationship that has been achieved over decades. By outlining the (social-)epistemic and inherently normative relationship between physicians and patients, I want to show how this relationship might be altered by non-traceable ML recommendations. With respect to some healthcare decisions, such changes in deliberative practice may create normatively far-reaching challenges. Therefore, in the future, a differentiation of decision-making situations in healthcare with respect to the necessary depth of insight into the process of outcome generation seems essential.

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Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship

Cited by 7 publications

References 44 publications

Physician’s autonomy in the face of AI support: walking the ethical tightrope

Physician’s autonomy in the face of AI support: walking the ethical tightrope

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach

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