ArticleNature communications2024
Large language models streamline automated machine learning for clinical studies.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
What it found
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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.
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Who cites it
60 citing papers in PubMed.
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- Making large language models reliable data science programming copilots for biomedical research.Nature biomedical engineering · 2026Article
- Leveraging generative artificial intelligence for the development of non-interventional research study protocols: a proof-of-concept feasibility study.BMC medical research methodology · 2026Article
- Established machine learning matches tabular foundation models in clinical predictions.BMC medical informatics and decision making · 2026Article
- Explainable Lightweight AI for the Identification of Right-Sided Cardiac Dysfunction in a Saudi Arabian Diabetic Cohort.Journal of clinical medicine · 2026Article
- Integrating speech biomarkers and large language models for adolescent suicide risk detection with mobile application for real-world evaluation.Cell reports. Medicine · 2026Article
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Statistical Consistency in Artificial Intelligence-Assisted Computations: A Comparison of SPSS 31.0 and GPT-5.5.Cureus · 2026Article
- Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Unlikely Storyteller: Leveraging Narrative-Based Communication in LLM-Generated Medical Advice.Healthcare (Basel, Switzerland) · 2026Article
- Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous, tool-aware biomedical data analyses.Nature biomedical engineering · 2026Article
- Enhancing bone metastasis CT report analysis: a comparison of local and proprietary large language models for privacy and resource efficiency.BMC health services research · 2026Article
- Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (NProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Leveraging chatgpt' s advanced data analysis for forensic science research and applications.Forensic science, medicine, and pathology · 2026Article
- Gaps in large language model awareness, usage, and perceptions in the United States: Evidence from a nationally representative longitudinal survey.PNAS nexus · 2026Article
- Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.NPJ digital medicine · 2026Article
- Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings.Frontiers in dental medicine · 2026Article
- Multi-step retrieval and reasoning improves radiology question answering with large language models.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this gap and perform ML analyses efficiently. Real-world clinical datasets and study details from large trials across various medical specialties were presented to ChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed state-of-the-art ML models based on the original study's training data to predict clinical outcomes such as cancer development, cancer progression, disease complications, or biomarkers such as pathogenic gene sequences. Following the re-implementation and optimization of the published models, the head-to-head comparison of the ChatGPT ADA-crafted ML models and their respective manually crafted counterparts revealed no significant differences in traditional performance metrics (p ≥ 0.072). Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts. In conclusion, ChatGPT ADA offers a promising avenue to democratize ML in medicine by simplifying complex data analyses, yet should enhance, not replace, specialized training and resources, to promote broader applications in medical research and practice.
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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.