Public-safe research case study
Reliable Multimodal Medical AI for Neurodegenerative Disease Analysis
A public-safe research case study on MRI-clinical fusion, leakage-aware evaluation, calibration, uncertainty, explainability, and decision-support reliability in neurodegenerative disease AI.
This is a research case study, not a clinical diagnostic product or tool. The focus is on reliable AI methodology, retrospective evaluation, uncertainty-aware decision support, and responsible model assessment.
Problem Context
Medical AI systems require more than high benchmark scores. In neurodegenerative disease research, models may combine clinical variables with imaging-derived representations, but the evaluation must control leakage, patient overlap, preprocessing bias, calibration error, and uncertainty. This case study summarizes my research approach to building and evaluating multimodal decision-support pipelines responsibly.
Research Objective
- Combine structured clinical evidence with imaging-derived representations.
- Evaluate multimodal fusion under leakage-aware, patient-disjoint protocols.
- Measure not only classification performance, but also calibration, uncertainty, confidence, and review behavior.
- Translate model outputs into decision-support records that communicate probabilities, uncertainty, and review status.
- Keep the framing responsible: research support, not autonomous diagnosis.
Methodology Overview
Cohort & Label Governance
Define cohorts, labels, exclusions, and split rules before modeling so evaluation reflects the research question.
Train-Only Preprocessing
Fit preprocessing decisions on training data only to avoid leakage from validation or test cohorts.
Clinical Representation
Represent structured clinical variables while accounting for missingness, sensitive variables, and leakage-prone fields.
MRI / Imaging Representation
Use imaging-derived features or model representations in a controlled pipeline that preserves patient-level separation.
Multimodal Fusion
Study whether clinical and imaging evidence provide complementary signal under the same evaluation protocol.
Calibration & Uncertainty
Evaluate probability quality, confidence behavior, entropy, margin, and uncertainty signals beyond top-label accuracy.
Review-Aware Output
Produce structured research outputs that show probabilities, uncertainty indicators, review status, and provenance.
Technical Themes
Leakage-Aware Evaluation
Patient-disjoint splitting, train-only preprocessing, and exclusion of diagnosis-adjacent or leakage-prone variables to keep evaluation credible.
Multimodal Fusion
Combining clinical variables with imaging-derived representations to study whether modalities provide complementary signal under controlled evaluation.
Calibration and Uncertainty
Evaluating whether predicted probabilities are reliable, not just whether the top predicted class is correct.
Selective Prediction
Studying when a model should make a prediction and when uncertain cases should be deferred for human review.
Explainability
Summarizing clinical and imaging-related evidence in a way that supports auditability and technical review.
Reproducibility
Using locked splits, frozen preprocessing decisions, documented artifacts, and consistent evaluation protocols.
Beyond Accuracy
The research framing evaluates systems through multiple reliability lenses. Exact numeric results are intentionally omitted here unless they are approved for public release, but the evaluation protocol is designed to compare models using:
Decision-Support Output
A responsible research pipeline should produce structured records that support auditability and technical review. In this framing, the model output is not treated as a clinical deployment artifact; it is a decision-support research record that can communicate:
Public-Safe Evidence
What can be shown publicly
Research framing, methodology, architecture diagrams, leakage-control strategy, evaluation protocol, calibration/reliability concepts, and sanitized result summaries where approved.
What is intentionally not exposed
Restricted data, patient-level information, sensitive cohort details, unpublished internal files, or claims that exceed research validation.
What can be discussed in interviews
Fusion design, evaluation protocol, leakage control, calibration, uncertainty, selective prediction, explainability, and how reliability changes model selection.
What This Demonstrates
- Ability to work on high-stakes AI problems with evaluation discipline.
- Understanding that trustworthy AI requires calibration, uncertainty, leakage control, and interpretability.
- Research engineering ability: turning methodology into reproducible pipelines, figures, tables, and audit-ready outputs.
- Strong fit for healthcare AI, applied scientist, research engineer, and responsible AI roles.
- A complementary profile: production computer vision experience plus PhD-level reliable AI research.
Recruiter Takeaway
This case study shows how I approach AI systems where reliability, evaluation design, and responsible communication matter as much as model architecture. It complements my production computer vision experience by showing research depth in multimodal, calibrated, and uncertainty-aware AI.
Interested in reliable medical AI research?
I can discuss the fusion design, evaluation protocol, calibration strategy, uncertainty analysis, and review-aware outputs without exposing restricted data or overstating research validation.