mlsanityData to 7 Oct 2026

The Potential and Pitfalls of using a Large Language Model such as ChatGPT or GPT-4 as a Clinical Assistant

arXiv 2307.08152 ↗First posted 16 Jul 2023cs.CLcited by 6

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Abstract (arXiv)

Recent studies have demonstrated promising performance of ChatGPT and GPT-4 on several medical domain tasks. However, none have assessed its performance using a large-scale real-world electronic health record database, nor have evaluated its utility in providing clinical diagnostic assistance for patients across a full range of disease presentation. We performed two analyses using ChatGPT and GPT-4, one to identify patients with specific medical diagnoses using a real-world large electronic health record database and the other, in providing diagnostic assistance to healthcare workers in the prospective evaluation of hypothetical patients. Our results show that GPT-4 across disease classification tasks with chain of thought and few-shot prompting can achieve performance as high as 96% F1 scores. For patient assessment, GPT-4 can accurately diagnose three out of four times. However, there were mentions of factually incorrect statements, overlooking crucial medical findings, recommendations for unnecessary investigations and overtreatment. These issues coupled with privacy concerns, make these models currently inadequate for real world clinical use. However, limited data and time needed for prompt engineering in comparison to configuration of conventional machine learning workflows highlight their potential for scalability across healthcare applications.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 16 Jul 2023.

Builds on

14 of 33 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2023A Survey of Large Language Modelscited by 2,537
  6. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  7. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  8. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  9. 2020Problems with Shapley-value-based explanations as feature importance measurescited by 198
  10. 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
  11. 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 100
  12. 2020Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkitcited by 66
2 more
  1. 2021Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Casecited by 7
  2. 2022A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseasescited by 1

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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  2. 2024Addressing cognitive bias in medical language modelscited by 34
  3. 2023Quantifying Uncertainty in Natural Language Explanations of Large Language Modelscited by 31
  4. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  5. 2025FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Frameworkcited by 8
  6. 2024High Order Reasoning for Time Critical Recommendation in Evidence-based Medicinecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation
  2. 2025ClinicalGPT-R1: Pushing reasoning capability of generalist disease diagnosis with large language model
  3. 2023Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential
  4. 2023Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification
  5. 2023Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
  6. 2025Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
  7. 2023Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine
  8. 2023Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task