ArticleJournal of medical Internet research2025
Exploring the Dilemma of AI Use in Medical Research and Knowledge Synthesis: A Perspective on Deep Research Tools.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study.JMIR medical informatics · 2026Article
- Artificial Intelligence Tools for Automating Evidence Synthesis: Scoping Review.Journal of medical Internet research · 2026Article
- Deep Research Agents: Major Breakthrough or Incremental Progress for Medical AI?Journal of medical Internet research · 2026Review
- The Long road to AI-physician partnership starts with learners: a map for AI in medical education.BMJ digital health & AI · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: Advances in artificial intelligence (AI) promise to reshape the landscape of scientific inquiry. Amidst all these, OpenAI's latest tool, Deep Research, stands out for its potential to revolutionize how researchers engage with the literature. However, this leap forward presents a paradox; while AI-generated reviews offer speed and accessibility with minimal effort, they raise fundamental concerns about citation integrity, critical appraisal, and the erosion of deep scientific thinking. These concerns are particularly problematic in the context of biomedical research, where evidence quality may influence clinical practice and decision-making. In this piece, we present an empirical evaluation of Deep Research and explore both its remarkable capabilities and inherent limitations. Through structured experimentation, we assess its effectiveness in synthesizing literature, highlight key shortcomings, and reflect on the broader implications of these tools for research training, and the integrity of evidence-based practice. With AI tools increasingly blurring the lines between knowledge generation and critical inquiry, we argue that while AI democratizes access to knowledge, wisdom remains distinctly human.
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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.