ReviewNature medicine2025
Generative artificial intelligence in medicine.
Review in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07640906 (A Multicenter Comparative Study Evaluating the Impact of an AI-Assisted Chest CT Reporting System on Real-world Radiologist Performance), which is not on this map. Cited by 63 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.
A Multicenter Comparative Study Evaluating the Impact of an AI-Assisted Chest CT Reporting System on Real-world Radiologist Performance: The DOUBLE-ACE Study
Who cites it
63 citing papers in PubMed.
- Beyond social media: the era of generative AI and intelligent digital platforms in nephrology education.Renal failure · 2026Article
- End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- What Are Physicians For? How Psychiatry's Competencies Endure Into the Age of AI.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Development and evaluation of a large language model-based, retrieval-augmented generation application for query response in early oncology clinical trials.ESMO real world data and digital oncology · 2026Article
- Augmenting patient safety surveillance in radiation oncology with large language model-based root cause analysis: A proof-of-concept study.PLOS digital health · 2026Article
- Exploring needs and priorities in digital health management for rare disease patients and their caregivers: A mixed-methods study.PLOS digital health · 2026Article
- Reply to ensuring trustworthy AI assisted guideline development for clinical practice.NPJ digital medicine · 2026Article
- From Molecular Pathways to Artificial Intelligence: Advancing the Understanding and Management of Osteosarcopenia.International journal of molecular sciences · 2026Review
- Legal liability of physicians and new governance in the AI era.Japanese journal of radiology · 2026Article
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- A S.C.O.R.E. framework for evaluating open-ended responses from large language models in healthcare.Cell reports. Medicine · 2026Article
- Towards a framework for implementing artificial intelligence in clinical medicine.BMJ health & care informatics · 2026Article
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.Journal of medical systems · 2026Article
- From Operating System to Operating Society: The Next Mission of Medical Informatics.Healthcare informatics research · 2026Article
- Enhancing Physician Resilience to Generative AI: Multilevel Framework for Shared Authority, Verification, and Skill Preservation.Journal of medical Internet research · 2026Article
- Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study.JMIR medical informatics · 2026Article
- Generative AI dependency on programming among university students: a scale development and validation study.BMC psychology · 2026Article
- Integrating mission-aligned value with cost to assess the economic impact of AI in healthcare.NPJ digital medicine · 2026Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Generative artificial intelligence (GAI) can automate a growing number of biomedical tasks, ranging from clinical decision support to design and analysis of research studies. GAI uses machine learning and transformer model architectures to generate useful text, images and sound data in response to user queries. While previous biomedical deep-learning applications have used general-purpose datasets and enormous volumes of labeled data for training, evidence now suggests that GAI models may perform better while requiring less training data-for example, using smaller, domain-specific datasets. Moreover, AI techniques have progressed from fully supervised training to less label-intensive approaches, such as weakly supervised or unsupervised fine-tuning and reinforcement learning. Recent iterations of GAI, such as agents, mixture-of-expert models and reasoning models, have further extended their capabilities to assist with complex and multistage tasks. Here, we provide an overview of recent technical advancements in GAI. We explore the potential of the latest generation of models to improve healthcare for clinicians and patients, and discuss validation approaches using specific examples to illustrate challenges and opportunities for further work.
Indexed as
Identifiers
41053447What 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.