As of April 30, 2025, large language models (LLMs) have significantly advanced in the field of disease diagnosis, offering promising tools to augment clinical decision-making. This review highlights the most notable LLMs in medical diagnostics, based on recent peer-reviewed studies and industry developments.
ClinicalGPT-R1 – A New Benchmark in Diagnostic Reasoning
ClinicalGPT-R1 is a specialized medical LLM trained on over 20,000 real clinical records. Unlike general-purpose LLMs (like GPT-4), this model focuses specifically on clinical decision-making and reasoning.
Strengths:
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- Built with medical logic and probabilistic reasoning layers
- Outperforms GPT-4 on Chinese diagnosis datasets
- Comparable to GPT-4 in English cases
- Handles symptom progression, timelines, and ambiguous symptoms better than general models
Key Applications:
- Internal medicine
- Multi-system syndromes
- Emergency triage decision support
Use Case Example: A patient presenting with chest pain, fatigue, and mild fever: ClinicalGPT-R1 can differentiate between cardiac, infectious, and autoimmune etiologies better than GPT-4.
DeepSeek-R1 vs O3 Mini – Real-World Model Benchmarking
A study compared DeepSeek-R1 and O3 Mini across 7 disease categories including:
- Mental health
- Endocrine disorders
- Neurological diseases
- Autoimmune diseases
DeepSeek-R1:
- Accuracy: 76% (disease-level), 82% (overall)
- Strongest in mental health, neuro, and oncology
- Slight lag in respiratory diagnoses
O3 Mini:
- Accuracy: 72% (disease-level), 75% (overall)
- Performed best in autoimmune and dermatological cases
- Faster inference, but shallower reasoning
Clinical Use Tip: DeepSeek-R1 is better suited for in-hospital triage; O3 Mini may be a better fit for telemedicine and screening tools.
LLM-Enhanced EHR Disease Detection
A novel method uses LLMs to process free-text EHR data and detect diseases like:
- Diabetes
- Hypertension
- Acute Myocardial Infarction (AMI)
Highlights:
- Higher sensitivity and NPV than traditional ICD code methods
- Uses chain-of-thought prompting and clinical document context
- Less likely to miss edge-case diagnoses
Why It Matters: This approach can turn years of unstructured notes into real-time clinical flags, improving early detection in public health.
MERA (Memorize and Rank Approach)
MERA is a hybrid system combining LLMs with contrastive learning and knowledge-enhanced pretraining.
What It Does:
- Memorizes patterns from medical cases
- Ranks possible diagnoses hierarchically (differential diagnosis engine)
- Trained on ICU-level data (MIMIC-III, MIMIC-IV)
Best For:
- Critical care decision support
- Differential diagnosis under uncertainty
- Predicting future diagnoses based on early clinical features
ChatGPT / GPT-4 in Medical Diagnosis
While not designed for healthcare, ChatGPT (especially GPT-4) has been shown to:
- Reach ~90% diagnostic accuracy on simulated patient vignettes
- Perform better than average physicians when used as a co-pilot
- Provide explanations, differential lists, and confidence levels
Limitations:
- Prone to hallucinations without guardrails
- Not trained on real EHR or clinical data
- Lacks regulatory clearance for medical use
Use GPT-4 only for second-opinion style queries — not as a primary diagnostic tool.
Ethical & Practical Challenges
Even the best LLMs face risks:
Bias: LLMs may underperform on underrepresented populations or mimic training data biases
Overconfidence: Some models confidently present wrong answers
Lack of explainability: Hard to audit or validate model logic in real time
Legal and ethical: Not yet FDA/EMA approved for primary diagnosis
Conclusion: What’s Best in 2025?
These tools are augmentative, not replacements for clinical judgment. Used wisely, they can enhance safety, catch missed diagnoses, and reduce inequality — especially in resource-limited settings.
Also Read: Large Language Models in Disease Diagnosis: A 2025 Technical Overview
