Efficient Antihallucinogenic AI for Tropical Medicine: A Probabilistic Framework for Differential Diagnosis

S.M. Sakeef Sani, Md. Shaown Miah, Taufiq Hasan

Keywords: Medical LLM, light-weight model, low-rank adaptation, differential diagnosis

TL;DR: We propose an efficient medical LLM for differential diagnosis suitable for low-resource setting focusing on tropical diseases

Abstract:

Clinical decision-making, particularly in the context of differential diagnosis in low-resource healthcare settings, poses significant challenges due to the complexity and variety of symptoms presented by patients and the lack of skilled doctors. This study introduces mLabLLM, a fine-tuned adaptation of the LLaMA 3.2 3B model designed to enhance clinical decision-making in differential diagnosis. Leveraging domain-specific datasets, including a curated tropical diseases dataset including Dengue, malaria, and chikungunya - prevalent health challenges in South Asian countries and employing optimization techniques like Low-Rank Adaptation (LoRA) and pruning to reduce computational overhead. The model achieves greater efficiency without compromising performance. A probabilistic framework integrates symptom-disease frequencies with Bayesian reasoning, enabling dynamic ranking of diagnoses during patient interactions. Experimental results show that mLabLLM significantly outperforms baseline models, achieving an 82.8% Top-3 accuracy in differential diagnosis, compared to 75.1% for Phi-3-128k and 72.4% for LLaMA 3.2 3B, positioning it as a scalable and practical solution for real-world clinical applications.

Submission Number: 65