# Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses

arXiv: https://arxiv.org/abs/2405.06712 · HTML: https://mlsanity.com/paper/2405.06712/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare](https://mlsanity.com/paper/2312.11541/) (2023)
- [Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models](https://mlsanity.com/paper/2307.11991/) (2023)
- [Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching](https://mlsanity.com/paper/2303.16756/) (2023)
- [Health-LLM: Personalized Retrieval-Augmented Disease Prediction System](https://mlsanity.com/paper/2402.00746/) (2024)
- [Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionals](https://mlsanity.com/paper/2403.14691/) (2024)
- [Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM](https://mlsanity.com/paper/2403.08818/) (2024)
- [Revolutionizing Mental Health Care through LangChain: A Journey with a Large Language Model](https://mlsanity.com/paper/2403.05568/) (2024)
- [GPT-4's assessment of its performance in a USMLE-based case study](https://mlsanity.com/paper/2402.09654/) (2024)

## Built on by (most cited first, 8 of 8)

- [Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG](https://mlsanity.com/paper/2501.09136/) (2025)
- [ORFuzz: Fuzzing the "Other Side" of LLM Safety -- Testing Over-Refusal](https://mlsanity.com/paper/2508.11222/) (2025)
- [LLMs in Disease Diagnosis: A Comparative Study of DeepSeek-R1 and O3 Mini Across Chronic Health Conditions](https://mlsanity.com/paper/2503.10486/) (2025)
- [Truth, Trust, and Trouble: Medical AI on the Edge](https://mlsanity.com/paper/2507.02983/) (2025)
- [SwasthLLM: a Unified Cross-Lingual, Multi-Task, and Meta-Learning Zero-Shot Framework for Medical Diagnosis Using Contrastive Representations](https://mlsanity.com/paper/2509.20567/) (2025)
- [LLM-Based Support for Diabetes Diagnosis: Opportunities, Scenarios, and Challenges with GPT-5](https://mlsanity.com/paper/2509.21450/) (2025)
- [Towards Explainable Conversational AI for Early Diagnosis with Large Language Models](https://mlsanity.com/paper/2512.17559/) (2025)
- [When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening](https://mlsanity.com/paper/2605.23148/) (2026)

## Abstract (arXiv)

The recent swift development of LLMs like GPT-4, Gemini, and GPT-3.5 offers a transformative opportunity in medicine and healthcare, especially in digital diagnostics. This study evaluates each model diagnostic abilities by interpreting a user symptoms and determining diagnoses that fit well with common illnesses, and it demonstrates how each of these models could significantly increase diagnostic accuracy and efficiency. Through a series of diagnostic prompts based on symptoms from medical databases, GPT-4 demonstrates higher diagnostic accuracy from its deep and complete history of training on medical data. Meanwhile, Gemini performs with high precision as a critical tool in disease triage, demonstrating its potential to be a reliable model when physicians are trying to make high-risk diagnoses. GPT-3.5, though slightly less advanced, is a good tool for medical diagnostics. This study highlights the need to study LLMs for healthcare and clinical practices with more care and attention, ensuring that any system utilizing LLMs promotes patient privacy and complies with health information privacy laws such as HIPAA compliance, as well as the social consequences that affect the varied individuals in complex healthcare contexts. This study marks the start of a larger future effort to study the various ways in which assigning ethical concerns to LLMs task of learning from human biases could unearth new ways to apply AI in complex medical settings.