This is like giving doctors a very smart, talkative assistant that can explain why it is suggesting a diagnosis or treatment, instead of just giving a black‑box answer. It combines ChatGPT-style conversation with explainable AI tools so clinicians can see the reasoning and evidence behind each suggestion.
Clinicians face information overload and opaque AI models that are hard to trust. This work aims to create an AI assistant that supports medical decision making while also explaining its recommendations in a human-understandable way, improving trust, safety, and adoption in clinical workflows.
Domain-tuned clinical reasoning workflows plus explainability methods tightly integrated into decision support (e.g., combining large language models with interpretable models or XAI techniques on healthcare data). Over time, access to real-world clinical data and validation studies could become a strong moat.
Hybrid
Vector Search
High (Custom Models/Infra)
Context window and inference cost for LLM-backed decision support at clinical scale, plus regulatory and data-governance constraints on using real patient data.
Early Adopters
Focus on explainable, clinically oriented use of ChatGPT-style models—rather than generic medical chatbots—by pairing LLMs with explicit XAI techniques and structured healthcare data to make recommendations auditable and trustworthy.