ArticleMedicine, health care, and philosophy2022
The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach.
Article in Medicine, health care, and philosophy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.BMC medical ethics · 2024Pooled it
- Review
- Epistemic authority and medical AI: epistemological differences and challenges in medical practice.Medicine, health care, and philosophy · 2026Article
- The role of AI and personalized medicine in healthcare: balancing technological advancements and the art of medicine.BMC medical education · 2025Article
- When time is of the essence: ethical reconsideration of XAI in time-sensitive environments.Journal of medical ethics · 2025Article
- IMI-Instrumentation for Myopia Management.Investigative ophthalmology & visual science · 2025Review
- What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Impact of personalized risk scores on shared decision making in left ventricular assist device implantation: Findings from a qualitative study.Patient education and counseling · 2025Article
- Clinicians' roles and necessary levels of understanding in the use of artificial intelligence: A qualitative interview study with German medical students.BMC medical ethics · 2024Article
- Estimating biological age from retinal imaging: a scoping review.BMJ open ophthalmology · 2024Article
- Impacts of Clinical Decision Support Systems on the Relationship, Communication, and Shared Decision-Making Between Health Care Professionals and Patients: Multistakeholder Interview Study.Journal of medical Internet research · 2024Article
- Physician's autonomy in the face of AI support: walking the ethical tightrope.Frontiers in medicine · 2024Article
- Responsibility and decision-making authority in using clinical decision support systems: an empirical-ethical exploration of German prospective professionals' preferences and concerns.Journal of medical ethics · 2023Article
- [Factors influencing the implementation of AI-based decision support systems for antibiotic prescription in hospitals: a qualitative analysis from the perspective of health professionals].Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) · 2023Article
- Medicine and machines.Medicine, health care, and philosophy · 2022Article
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Authors and funding
1 author.
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Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.