Evidence map›Paper›PMID 41176529›Full record

ArticleMedicine, health care, and philosophy2026

Epistemic authority and medical AI: epistemological differences and challenges in medical practice.

Angeliki Kerasidou, Charalampia Xaroula Kerasidou

Abstract read
In one paragraph

Article in Medicine, health care, and philosophy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Angeliki KerasidouEthox Centre, Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery Oxford, University of Oxford, Oxford, UK. angeliki.kerasidou@ethox.ox.ac.uk.ORCID http://orcid.org/0000-0001-9344-3297
Charalampia Xaroula KerasidouEthox Centre, Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery Oxford, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9794-8492

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent and ongoing advances in medical AI promise to revolutionise medicine by improving the accuracy, speed, and efficiency of clinical care. These promises are responses to the continuous quest of modern medicine to eliminate uncertainty and find answers to crucial questions of diagnosis, prognosis and treatment, while the impressive reported results of medical AI have raised the question of whether medical AI can be perceived as an epistemic authority that challenges the authority of doctors. In this paper, we examine this question by approaching it from the standpoint of what epistemic goods medical AI can offer, or else, what medical AI can claim to "know". Using Popowicz' account of epistemic authority in medical practice, which he locates in the scientific method that underpins the practice, we argue that medical AI uses a different scientific method to the one that has given rise and forms the epistemic foundations of traditional western medicine, and this presents a problem. As long as we are seeking not only statistically accurate correlations, but empirically grounded causations in medicine, AI cannot be treated as an epistemic authority in this field. We conclude that until medical practice finds ways to successfully incorporate such epistemological differences, medical AI should submit to the epistemic authority of medical practice and take its place on the long list of important and useful epistemic tools doctors can use to improve the health of patients.

Indexed as

Artificial IntelligenceKnowledgeHumansPhilosophy, MedicalAccuracyArtificial intelligenceEpistemic authorityEpistemic technologyMedical AIMedical practice

Identifiers

PMID41176529
PMCPMC12960419

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.