Evidence map›Paper›PMID 42063107›Full record

ArticleBMC medical informatics and decision making2026

The 'Hippocratic Oath' for AI-based clinical decision support systems.

Solomon Bracey, Ben Ainsworth, Joseph Alderman, Christopher R S Banerji, Tapabrata Chakraborti, Kathrin Cresswell, Alisha Davies, Victoria Hellon, Chris Harbron, Jonathan Nash and 5 more

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Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Solomon BraceyFaculty of Medicine, University of Southampton, Southampton, UK.
Ben AinsworthUniversity of Southampton, Southampton, UK.
Joseph AldermanUniversity of Birmingham, Birmingham, UK.
Christopher R S BanerjiKings College London, London, UK.
Tapabrata ChakrabortiThe Alan Turing Institute, London, UK.
Kathrin CresswellUsher Institute, University of Edinburgh, Edinburgh, Scotland, UK.
Alisha DaviesThe Alan Turing Institute, London, UK.
Victoria HellonTools, Practices and Systems, The Alan Turing Institute, London, UK.
Chris HarbronRoche Products Ltd, Welwyn Garden City, UK.
Jonathan NashUniversity Hospitals Sussex NHS Foundation Trust, Brighton, UK.
Olga KostopoulouDepartment of Surgery & Cancer, Imperial College London, London, UK.
Emma KarouneTools, Practices and Systems, The Alan Turing Institute, London, UK.
Jeremy C WyattUniversity of Southampton, Southampton, UK.
Ben D MacArthurThe Alan Turing Institute, London, UK.
Nicholas FuggleThe Alan Turing Institute, London, UK. nrf@mrc.soton.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe implementation of Artificial Intelligence assisted Clinical Decision Support Systems (AI-CDSS) shows significant potential to improve healthcare. However, implementing AI-CDSS has many associated challenges. This article introduces the 'Hippocratic Oath' for AI which promotes safe and effective AI-CDSS development and implementation.

methodsThis paper summarises discussions which took place during the Turing-Roche Clinical AI Interest Group Joint Workshop. The workshop began with scoping lectures from AI experts, leading into focus group discussions of key themes surrounding AI-CDSS implementation. These include the ethics, trust, evaluation, regulation, human factors and challenges involved with implementing AI-CDSS into healthcare settings. Focus group outcomes, alongside insight from lectures, were used to formulate the arguments in this paper.

resultsThis article presents a consensus definition of AI-CDSS and outlines a comprehensive table of implementation challenges alongside mitigation measures. It introduces the 'Hippocratic Oath for AI' and discusses its potential to promote safe and effective AI-CDSS implementation through addressing human factors and explainability.

conclusionsThe 'Hippocratic Oath for AI', can be used by AI-CDSS implementers and developers as a framework to mitigate challenges involved with implementing AI-CDSS into healthcare settings. This framework is likely to promote safe and effective implementation and maximise HCP uptake of the AI-CDSS. Through facilitating AI-CDSS use, this oath can transform health care practice via reducing medical errors, healthcare costs and improving patient outcomes.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalHippocratic OathHumansArtificial intelligenceClinical decision support systemsEthicsHealthcareHippocratic Oath

Identifiers

PMID42063107
PMCPMC13439839

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.