ArticleJournal of biomedical informatics2024
Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.
Article in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- A data-driven method for research trend analysis in a scientific discipline: Application to the journal of biomedical informatics.Journal of biomedical informatics · 2026Article
- A neural-symbolic AI agent system for biomedical concept mapping.NPJ digital medicine · 2026Article
- Meeting the challenges of electronic health record (EHR) optimization.NPJ digital medicine · 2025Review
- Scalable scientific interest profiling using large language models.Journal of biomedical informatics · 2025Article
- Enhancing Evidence Synthesis Efficiency: Leveraging Large Language Models and Agentic Workflows for Optimized Literature Screening.Cochrane evidence synthesis and methods · 2025Article
- CLEAR: A vision to support clinical evidence lifecycle with continuous learning.Journal of biomedical informatics · 2025Article
- Accelerating clinical evidence synthesis with large language models.NPJ digital medicine · 2025Article
- Leveraging long context in retrieval augmented language models for medical question answering.NPJ digital medicine · 2025Article
- Large Language Models and Their Applications in Drug Discovery and Development: A Primer.Clinical and translational science · 2025Review
- From GPT to DeepSeek: Significant gaps remain in realizing AI in healthcare.Journal of biomedical informatics · 2025Article
- Semi-supervised learning from small annotated data and large unlabeled data for fine-grained Participants, Intervention, Comparison, and Outcomes entity recognition.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Evaluating a large language model's ability to answer clinicians' requests for evidence summaries.Journal of the Medical Library Association : JMLA · 2025Article
- Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training.BMC medical education · 2024Review
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
- Article
- Simulated misuse of large language models and clinical credit systems.NPJ digital medicine · 2024Review
- Article
- Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare.ArXiv · 2024Article
- Closing the gap between open source and commercial large language models for medical evidence summarization.NPJ digital medicine · 2024Article
Corrections and comments
- Update of
Authors and funding
14 authors.
Funding
Abstract
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.
Indexed as
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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.