ReviewAPL bioengineering2026
ChatGPT in precision medicine.
Review in APL bioengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence, notably the generative language model ChatGPT, into precision medicine precedes a paradigm shift in healthcare. This Review explores the capabilities of ChatGPT to process large data sets, understand contextual details, and generate human-like responses, making it a transformative tool for various applications in precision medicine. By analyzing the genetic sequences, identifying biomarkers, and predicting disease risks, ChatGPT facilitates earlier diagnosis and intervention. Moreover, its natural language processing capabilities enhance patient communication and engagement by elucidating complex medical concepts. This Review further examines ChatGPT's role in streamlining research, augmenting clinical trial recruitment, and supporting healthcare professionals through access to updated information and skill assessment. While acknowledging the complexities and potential concerns regarding reliance on publicly sourced data, this Review highlights ChatGPT's substantial capacity to optimize patient prognoses and enhance the operational efficacy of the healthcare delivery system.
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.