Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review

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Abstract

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI’s true effects and limitations in health care.

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Most cited references 52

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PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

Adrian Aldcroft, Chantelle Garritty, Simon Lewin … (2018)

Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.

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Dissecting racial bias in an algorithm used to manage the health of populations

Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

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Is Open Access

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

Xiaoxuan Liu, Livia Faes, Aditya U Kale … (2019)

Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101751302

Journal ID (pubmed-jr-id): 48799

Journal ID (nlm-ta): Lancet Digit Health

Journal ID (iso-abbrev): Lancet Digit Health

Title: The Lancet. Digital health

ISSN (Electronic): 2589-7500

Publication date Nihms-submitted: 27 April 2024

Publication date (Print): May 2024

Publication date PMC-release: 03 May 2024

Volume: 6

Issue: 5

Pages: e367-e373

Affiliations

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA (R Han MS, P Rajpurkar PhD); Department of Computer Science, Stanford University, Stanford, CA, USA (R Han); University of California Los Angeles–Caltech Medical Scientist Training Program, Los Angeles, CA, USA (R Han); Department of Neurology, Yale School of Medicine, New Haven, CT, USA (J N Acosta MD); Rad AI, San Francisco, CA, USA (J N Acosta); Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada (Z Shakeri PhD); Stanford Prevention Research Center, Department of Medicine (Prof J P A Ioannidis MD DSc), and Meta-Research Innovation Center at Stanford (Prof J P A Ioannidis), Stanford University, Stanford, CA, USA; Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA (Prof E J Topol MD)

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Contributors

RH and PR conceptualised the scoping review. JPAI, PR, and EJT supervised the scoping review. RH, JPAI, and PR contributed to the design of the scoping review. RH, JNA, and PR did the screening of the search results and data extraction. RH drafted the manuscript. ZS contributed to the presentation of data. All authors had access to the data, interpreted the analyses, and critically revised and edited the manuscript.

[*]

Contributed equally

Correspondence to: Prof Eric J Topol, Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA, etopol@ 123456scripps.edu

Article

Manuscript ID: NIHMS1989111

DOI: 10.1016/S2589-7500(24)00047-5

PMC ID: 11068159

PubMed ID: 38670745

SO-VID: bc5f27e8-d7bd-449d-811f-cbf686e17ce5

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This is an Open Access article under the CC BY-NC-ND 4.0 license.

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Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review

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Artificial Intelligence in Medicine

Most cited references 52

PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

Dissecting racial bias in an algorithm used to manage the health of populations

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

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