# Large Language Models Streamline Automated Machine Learning for Clinical Studies arXiv: https://arxiv.org/abs/2308.14120 ยท HTML: https://mlsanity.com/paper/2308.14120/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 87 references that are in the map. - First posted on arXiv 27 Aug 2023. ## Builds on (most cited first, 9 of 9) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023) - [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022) - [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019) - [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023) - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023) - [AutoML-GPT: Automatic Machine Learning with GPT](https://mlsanity.com/paper/2305.02499/) (2023) - [On the Explainability of Natural Language Processing Deep Models](https://mlsanity.com/paper/2210.06929/) (2022) - [GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles](https://mlsanity.com/paper/2010.13972/) (2020) ## Built on by (most cited first, 24 of 24) - [MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science](https://mlsanity.com/paper/2506.04405/) (2025) - [Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework](https://mlsanity.com/paper/2411.16707/) (2024) - [Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery](https://mlsanity.com/paper/2603.05860/) (2026) - [Panacea: A foundation model for clinical trial search, summarization, design, and recruitment](https://mlsanity.com/paper/2407.11007/) (2024) - [RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering](https://mlsanity.com/paper/2407.15621/) (2024) - [A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges](https://mlsanity.com/paper/2411.00024/) (2024) - [BioDSA-1K: Benchmarking Data Science Agents for Biomedical Research](https://mlsanity.com/paper/2505.16100/) (2025) - [Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline](https://mlsanity.com/paper/2406.17215/) (2024) - [SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://mlsanity.com/paper/2503.23314/) (2025) - [Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications](https://mlsanity.com/paper/2506.00660/) (2025) - [Multi-step retrieval and reasoning improves radiology question answering with large language models](https://mlsanity.com/paper/2508.00743/) (2025) - [Safety and accuracy follow different scaling laws in clinical large language models](https://mlsanity.com/paper/2605.04039/) (2026) - [GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis](https://mlsanity.com/paper/2406.15341/) (2024) - [From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine](https://mlsanity.com/paper/2502.09242/) (2025) - [Intrinsic Model Weaknesses: How Priming Attacks Unveil Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2502.16491/) (2025) - [Can Large Language Models Replace Data Scientists in Biomedical Research?](https://mlsanity.com/paper/2410.21591/) (2024) - [Resolution scaling governs DINOv3 transfer performance in chest radiograph classification](https://mlsanity.com/paper/2510.07191/) (2025) - [Agentic retrieval-augmented reasoning reshapes collective reliability under model variability in radiology question answering](https://mlsanity.com/paper/2603.06271/) (2026) - [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024) - [Making Sense of Data in the Wild: Data Analysis Automation at Scale](https://mlsanity.com/paper/2502.15718/) (2025) - [The Role of Accuracy and Validation Effectiveness in Conversational Business Analytics](https://mlsanity.com/paper/2411.12128/) (2024) - [A Composable Multimodal Framework for cine CMR-Text-Driven Prediction of Heart Failure Outcomes](https://mlsanity.com/paper/2502.16548/) (2025) - [Platform for Representation and Integration of multimodal Molecular Embeddings](https://mlsanity.com/paper/2507.07367/) (2025) - [From Clinical Intent to Clinical Model: Autonomous Coding-Agents for Clinician-driven AI Development](https://mlsanity.com/paper/2604.17110/) (2026) ## Abstract (arXiv) 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>0.071). 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.