Project case study

Multimodal AI for Early Disease Detection

Award-winning applied AI work that fuses multiple modalities into a stronger disease-detection workflow.

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Problem

An AI-driven healthcare system that combines medical images, patient context, and text-heavy clinical signals to improve early disease detection workflows.

Context

The project was developed from June 2025 as a research-heavy attempt to combine medical images, electronic health records, genetic information, and symptoms rather than treating diagnosis as a single-input prediction problem.

My Role

AI/ML Engineer, Data Systems Builder & Motion UI Developer

Contribution

Defined a multimodal disease-detection workflow spanning medical images, electronic health records, genetic information, and patient-reported symptoms.
Structured deep-learning feature extraction and data fusion as separate stages so each modality can be evaluated before fusion.
Prepared the research-heavy system as a demonstrable Project Expo submission.

Architecture

Modality inputs
Collects medical images, health records, genetic information, and symptom signals.
Feeds Feature encoders
Feature encoders
Extracts modality-specific representations with deep-learning models.
Feeds Fusion layer
Fusion layer
Combines the modality representations into one prediction context.
Feeds Detection output
Detection output
Presents the early-detection result for review in the project workflow.
Terminal stage

Engineering Decisions

Fuse multiple modalities instead of relying on one source

Reason: The project targets patterns that can be missed when images, records, genetics, or symptoms are evaluated alone.

Tradeoff: The system depends on aligned, representative data across modalities and is harder to validate than a single-input model.

Keep modality processing separable before fusion

Reason: Separate encoders make each input path easier to inspect and replace.

Tradeoff: The pipeline has more interfaces and synchronization work than one end-to-end input path.

Outcomes

Team lead on an award-winning multimodal disease-detection system, second prize at the Project Expo. The fusion model is under patent registration, and the work is published in IRJMETS Vol 7 Issue 12.
Sources: cert-14, cert-20

Limitations

The available evidence verifies Project Expo recognition, not clinical accuracy, safety, or deployment readiness.
No public clinical benchmark, model card, or patient-facing deployment is linked.

Stack

Python
Deep Learning
ResNet
BERT
Healthcare AI
Data Fusion