ReviewJournal of general internal medicine2012
Differential diagnosis generators: an evaluation of currently available computer programs.
Review in Journal of general internal medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 3 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
60 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Managing diagnostic uncertainty in primary care: a systematic critical review.BMC family practice · 2017Pooled it
- The Effectiveness of Electronic Differential Diagnoses (DDX) Generators: A Systematic Review and Meta-Analysis.PloS one · 2016Pooled it
- Can clinical decision support systems be an asset in medical education? An experimental approach.BMC medical education · 2023Trial
- Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study.International journal of environmental research and public health · 2021Trial
- Multimodal foundation models exploit text to make medical image predictions.Nature communications · 2026Article
- Grounding large language models in clinical diagnostics.Nature communications · 2026Article
- Evaluation of the Diagnostic Capabilities of Artificial Intelligence (GPT-4) in a Cardiology Department in Sub-Saharan Africa: Cross-Sectional Study.Health science reports · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Cracking the code: a head-to-head comparison of expert clinicians and artificial intelligence in diagnosing rare diseases.Orphanet journal of rare diseases · 2025Article
- "Electronic Pediatrician", a non-machine learning prototype artificial intelligence software for pediatric computer-assisted pathophysiologic diagnosis - general presentation.World journal of methodology · 2025Article
- Application of Large Language Models in Complex Clinical Cases: Cross-Sectional Evaluation Study.JMIR medical informatics · 2025Article
- Large Language Models for Rare Disease Diagnosis at the Undiagnosed Diseases Network.JAMA network open · 2025Article
- A large language model improves clinicians' diagnostic performance in complex critical illness cases.Critical care (London, England) · 2025Article
- Article
- Dedicated AI Expert System vs Generative AI With Large Language Model for Clinical Diagnoses.JAMA network open · 2025Article
- Comparison of Frontier Open-Source and Proprietary Large Language Models for Complex Diagnoses.JAMA health forum · 2025Article
- Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study.JMIR medical informatics · 2024Article
- Evaluating ChatGPT-4's Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases.JMIR formative research · 2024Article
- Emergency department use of an electronic differential diagnosis generator in the evaluation of critically ill patients.Internal and emergency medicine · 2024Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferential diagnosis (DDX) generators are computer programs that generate a DDX based on various clinical data.
objectiveWe identified evaluation criteria through consensus, applied these criteria to describe the features of DDX generators, and tested performance using cases from the New England Journal of Medicine (NEJM©) and the Medical Knowledge Self Assessment Program (MKSAP©).
methodsWe first identified evaluation criteria by consensus. Then we performed Google® and Pubmed searches to identify DDX generators. To be included, DDX generators had to do the following: generate a list of potential diagnoses rather than text or article references; rank or indicate critical diagnoses that need to be considered or eliminated; accept at least two signs, symptoms or disease characteristics; provide the ability to compare the clinical presentations of diagnoses; and provide diagnoses in general medicine. The evaluation criteria were then applied to the included DDX generators. Lastly, the performance of the DDX generators was tested with findings from 20 test cases. Each case performance was scored one through five, with a score of five indicating presence of the exact diagnosis. Mean scores and confidence intervals were calculated. KEY
resultsTwenty three programs were initially identified and four met the inclusion criteria. These four programs were evaluated using the consensus criteria, which included the following: input method; mobile access; filtering and refinement; lab values, medications, and geography as diagnostic factors; evidence based medicine (EBM) content; references; and drug information content source. The mean scores (95% Confidence Interval) from performance testing on a five-point scale were Isabel© 3.45 (2.53, 4.37), DxPlain® 3.45 (2.63-4.27), Diagnosis Pro® 2.65 (1.75-3.55) and PEPID™ 1.70 (0.71-2.69). The number of exact matches paralleled the mean score finding.
conclusionsConsensus criteria for DDX generator evaluation were developed. Application of these criteria as well as performance testing supports the use of DxPlain® and Isabel© over the other currently available DDX generators.
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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.