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MedLingo: Medical LLM with Mixture of Experts

MedLingo: Medical LLM with Mixture of Experts

MedLingo is a clinical decision-support system designed to bring specialization and precision into medical AI. Unlike general-purpose language models that try to cover everything but often lack depth in critical areas, MedLingo is built around a Mixture-of-Experts framework. Instead of relying on a single, monolithic model, it coordinates multiple smaller, domain-specific language models—each trained in a specialty such as ophthalmology, neurology, or oncology. This modular setup allows MedLingo to act more like a team of medical experts: when a clinician presents a case, the system routes the input to the most relevant specialists, who then collaborate and reconcile their findings. By integrating multimodal data (clinical text, lab results, medical imaging), the platform delivers context-aware analysis that mirrors the way real doctors synthesize evidence.

This project combines natural language understanding with medical image encoding and structured data processing, ensuring no critical detail is left out of the diagnostic process. The system is evaluated on a wide range of benchmarks, from MIMIC-III and PubMedQA for clinical text to CheXpert and ISIC for imaging, ensuring that its performance is robust across diverse data types. Its architecture is carefully designed for efficiency: the Mixture-of-Experts controller dynamically selects the most relevant models for each query, reducing unnecessary computation and maintaining low latency. This makes MedLingo not only accurate but also scalable, allowing new expert models to be added over time without overhauling the system.

The impact of MedLingo extends beyond technical novelty. For healthcare professionals, it offers faster, more reliable diagnostic support that could reduce errors, accelerate clinical decision-making, and improve patient outcomes. For researchers, it provides a modular blueprint for how AI can be structured in medicine, demonstrating how collaboration between expert models can enhance reasoning in ways a single system cannot. And for the broader community, the project contributes open-access code, datasets, and methodologies, fostering transparency and future innovation in medical AI. By making clinical decision-support systems more specialized, adaptable, and multimodal, MedLingo represents a step toward AI that works not as a black box, but as an intelligent and trusted partner in healthcare.

Faculty

Students

  • Muhammad Samiullah
  • Sarmad Sultan
  • Ahmad Jan

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