I went deep on blob storage and upload flows first, which felt safe but probably wasn't what they wanted to spend time on.
Start by clarifying the product goals, users, and constraints, then propose a high-level architecture covering data ingestion, ML pipeline, and result delivery. Emphasize trade-offs between accuracy, latency, and compliance, and discuss cross-functional alignment with medical, legal, and engineering teams.
Pro tip: Frame the solution around patient safety and regulatory compliance (e.g., FDA, HIPAA) from the start, and propose a phased rollout with human-in-the-loop validation to build trust and mitigate risks.
Ask questions to understand the scope: types of X-rays, expected turnaround time, accuracy requirements, integration with existing systems, and regulatory constraints. Identify key stakeholders: doctors, radiologists, ML engineers, compliance officers.
Propose metrics like diagnostic accuracy, time-to-result, doctor satisfaction, and adoption rate. Define a minimal viable product focusing on a specific condition (e.g., pneumonia detection) to validate the approach before scaling.
Outline components: secure upload interface, data storage (DICOM), preprocessing pipeline, ML model inference, result generation, and notification system. Consider cloud services (e.g., Google Cloud Healthcare API) and integration with EHRs.
Discuss trade-offs: model accuracy vs. interpretability, latency vs. batch processing, build vs. buy ML models. Mitigate risks: data privacy, bias, false positives/negatives, and regulatory approval.
Outline collaboration with medical experts for labeling and validation, legal for compliance, and engineering for implementation. Suggest a phased rollout with feedback loops and continuous monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.