ArticleBMJ health & care informatics2021
Clinician checklist for assessing suitability of machine learning applications in healthcare.
Article in BMJ health & care informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 4 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
60 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Pooled it
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Artificial Intelligence Adoption in Public Health Practice: A Cross-Sectional Study of Practical Determinants Among Healthcare Professionals.Healthcare (Basel, Switzerland) · 2026Article
- User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study.Acta oncologica (Stockholm, Sweden) · 2026Article
- Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review.Journal of medical Internet research · 2025Article
- Accelerating Digital Mental Health: The Society of Digital Psychiatry's Three-Pronged Road Map for Education, Digital Navigators, and AI.JMIR mental health · 2025Article
- Mindbench.ai: an actionable platform to evaluate the profile and performance of large language models in a mental healthcare context.NPP - digital psychiatry and neuroscience · 2025Article
- Multimodal Noninvasive Assessment of C-Reactive Protein for Systemic Inflammation in Adults: Cross-Sectional Study.JMIR formative research · 2025Article
- A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare.NPJ digital medicine · 2025Article
- Proposing core competencies for physicians in using artificial intelligence tools in clinical practice.Internal medicine journal · 2025Article
- Using expert-cited features to detect leg dystonia in cerebral palsy.medRxiv : the preprint server for health sciences · 2025Article
- Evaluation of changes in prediction modelling in biomedicine using systematic reviews.BMC medical research methodology · 2025Article
- Consistency Analysis of Practice Orientation of the Healthy Enterprise Program in China.BMC public health · 2025Article
- Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation.JMIR rehabilitation and assistive technologies · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology.Insights into imaging · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.
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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.