Designing an Explainable AI System for Personalized Cancer Treatment in Resource-Constrained Environments

Develop an explainable AI system that leverages real-time genomic data, electronic health records (EHRs), and local healthcare infrastructure to provide personalized cancer treatment recommendations to low- and middle-income countries.

Constraints:

1. Limited Computing Power: The system must operate on edge devices (e.g., single-board computers, smartphones) with restricted processing capabilities (< 2 GHz CPU, < 4 GB RAM).
2. Scalability: Handle a minimum of 10,000 patients and 50,000 patient-tumor interactions within a 24-hour period.
3. Explainability: Provide transparent and interpretable AI decisions, enabling clinicians to validate and improve the system.
4. Data Privacy: Ensure local data storage and anonymization to protect patient confidentiality.
5. Cultural Adaptability: Incorporate culturally sensitive and context-aware patient engagement strategies to promote treatment adherence.
6. Integration: Seamlessly integrate with existing EHRs and genomic sequencing platforms.

Evaluation Criteria:

1. Accuracy: > 90% concordance with external benchmarks (e.g., treatment outcomes, patient satisfaction).
2. Explainability: 80% of clinician-validated explanations accurately reflect system reasoning.
3. Scalability: System maintains performance under high patient-tumor interaction loads.
4. Security: Achieve HIPAA compliance and maintain robust data encryption.

Submission Guidelines:

1. Technical Report: Submit a 5-page technical report detailing the system architecture, methodology, and evaluation process.
2. Code: Provide a publicly accessible code repository (e.g., GitHub) with all system components and dependencies.
3. Results: Share a comprehensive evaluation report highlighting system performance metrics and clinical validation results.

Submission Deadline: March 31, 2026.

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