Clinical Data Solutions
Transforming healthcare through advanced multimodal clinical dataset construction and AI-driven diagnostics.
Data Collection Phase
Collecting and annotating 5,000 cases for accurate diagnosis and treatment recommendations.
Prompt Engineering
Creating hierarchical templates for symptom extraction and diagnostic recommendations using real-time data.
In medical diagnostics, while large language models (LLMs) like the GPT series demonstrate promise for generating diagnostic reasoning, assisting with imaging reports, and summarizing patient notes, their black-box nature, miscalibrated confidence, and occasional hallucinations impede clinical adoption and physician trust. Thus, our core research question is: How can we “tame” LLMs so that they achieve high accuracy, explainability, and safety in medical diagnosis, while actively calibrating confidence and rejecting high-risk suggestions?
Sub-questions include:
Alignment with Medical Ontologies and Guidelines: Can we fine-tune and inject domain knowledge such that the model adheres to ICD-11, SNOMED CT, and evidence-based guidelines, reducing guideline-noncompliant inferences by up to 70%?
Chain-of-Thought Explainability: How can the model output a clear multi-step reasoning chain—symptoms → physical findings → ancillary tests → differential diagnoses—alongside conclusions for physician audit?
Confidence Calibration & Rejection Mechanism: When presented with complex cases or incomplete inputs, can the model provide well-calibrated confidence scores and automatically trigger “refer to specialist” alerts when risk exceeds predefined thresholds?
Multimodal Data Fusion: How to effectively integrate medical images, EHR text, and lab results to leverage LLM strengths in text reasoning and multimodal analysis for end-to-end diagnostic support?
We hypothesize that combining retrieval-augmented generation (RAG), domain ontology fine-tuning, and uncertainty regularization will boost diagnostic accuracy by ≥15%, reduce Expected Calibration Error (ECE) by ≥30%, and achieve an explanation coherence score ≥0.9 in expert evaluations.
