ArticleThe Lancet. Digital health2020
Time to reality check the promises of machine learning-powered precision medicine.
Article in The Lancet. Digital health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 4 of them syntheses that pooled 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.
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
99 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study.BMJ (Clinical research ed.) · 2023Pooled it
- Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach.International journal of environmental research and public health · 2022Pooled it
- Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review.JAMA network open · 2022Pooled it
- How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials.Diabetologia · 2023Trial
- Type 2 diabetes subtypes for precision medicine: methodological challenges and alternative prediction-based approaches.Diabetologia · 2026Review
- Potential Roles of Neuroimaging in Motor Neuron Disease.Current neurology and neuroscience reports · 2026Review
- AI Cautionary Guide: Pitfalls and Strategies for the Use of Machine Learning in Medical Research.Clinical pharmacology and therapeutics · 2026Review
- Development and Evaluation of Artificial Intelligence-Based Two-Step Model for Automated Serum Quality Assessment in Clinical Laboratories.Annals of laboratory medicine · 2026Article
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- Characterizing patients who benefit from mature medical AI models in real-world clinical applications.PLOS digital health · 2026Article
- Deep generative hidden Markov models for synthetic patient data generation: a novel approach for medical AI research.BMC medical informatics and decision making · 2026Article
- Multimodal machine learning for staging laparoscopy: a combined image analysis and morphologic tool for the discrimination of peritoneal metastasis.International journal of surgery (London, England) · 2026Article
- Toward precision neuroscience in Alzheimer's disease: the role of multimodal AI.Frontiers in aging neuroscience · 2026Review
- Assessment of precision medicine potential in diabetes mellitus: A meta-regression analysis of dose-dependent glycaemic control data from 44 randomised controlled trials.Diabetes, obesity & metabolism · 2025Article
- Predicting lymphovascular invasion in stage IA lung adenocarcinoma: a CT-based classification and regression tree model.European radiology · 2025Article
- Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets.Scientific reports · 2025Article
- Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India.The Lancet regional health. Southeast Asia · 2025Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.
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What OpenQuestion holds
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.