Large language models streamline automated machine learning for clinical studies
Open accessAbstract
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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Arasteh et al. used the integrated data analysis tools of an LLM to analyze scientific data, achieving results on par with human researchers.
“Arasteh et al. used the integrated data analysis tools of an LLM to analyze scientific data, achieving results on par with human researchers19.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [19]
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- Authors
- Tayebi Arasteh, S., Han, T., Lotfinia, M., Kuhl, C., Kather, J. N., Truhn, D., & Nebelung, S.
- Affiliations
- Universitätsklinikum Aachen (DE)
- University Hospital Heidelberg (DE)
- Corresponding author
- Han, Tianyu
- ORCID
- Venue
- Nature Communications
- Published
- 2024-02-21
- Volume
- 15
- Issue
- 1
- Pages
- 1603-1603
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10881983
- OpenAlex
- W4392016947
- Cited by
- 114
- References
- 53
- Citations by year
- 2026: 34 · 2025: 53 · 2024: 27
- FWCI
- 10.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
- arXiv (Cornell University)
- RWTH Publications (RWTH Aachen)
- Funders
- European Commission (101057091)
- Deutsche Forschungsgemeinschaft (TR 1700/7‐1, NE2136/3-1, 2136/3-1)
- Bundesministerium für Bildung und Forschung (01KD2215B, 031L0312A, 01KX2021)
- HORIZON EUROPE Framework Programme (101057091)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- large language models, ChatGPT, automated machine learning, clinical trials, medical data analysis, GPT-4, clinical outcome prediction, biomarker prediction, tumor progression, model performance comparison
- MeSH terms
- Machine Learning, Algorithms, Humans, Language, Neoplasms, Benchmarking
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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