SynthesisJournal of the American Medical Informatics Association : JAMIA2024
Question answering systems for health professionals at the point of care-a systematic review.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed, 19 citations in OpenAlex.
- Assisting patients with reliable information gathering: A guide to building efficient QA systems for medical use.PLOS digital health · 2026Article
- Performance evaluation of large language models in real-world perinatal medication consultations: a cross-sectional study.International journal of clinical pharmacy · 2026Article
- Adaptive confidence ensemble reranking for reliable knowledge-intensive question answering.Scientific reports · 2026Article
- Assessing large language model responses to pediatric depression FAQs: a cross-sectional study on readability, accuracy, and sentiment.Frontiers in psychiatry · 2026Article
- Comparative Evaluation of Advanced Chunking for Retrieval-Augmented Generation in Large Language Models for Clinical Decision Support.Bioengineering (Basel, Switzerland) · 2025Article
- Development of a Conversational Artificial Intelligence-Based Web Application for Medical Consultations: Prototype Study.JMIRx med · 2025Article
- Exploring the utility of Preevid, an evidence-based answer service to clinical questions, during the COVID-19 pandemic.Health information and libraries journal · 2025Article
- Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models.Bioengineering (Basel, Switzerland) · 2025Article
- Redefining digital health interfaces with large language models.Frontiers in artificial intelligence · 2025Article
- RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
objectivesQuestion answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. MATERIALS AND
methodsWe searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems.
resultsWe included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians' questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. DISCUSSION: While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.
Indexed as
Identifiers
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.