Explain Medical AI Data and Evaluation
Company: Oracle
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Overview: This question evaluates competence in medical conversational AI covering data sourcing and cleaning, leakage-aware dataset splitting, model choice (prompting, fine-tuning, RAG/hybrid), evaluation design (automatic and human metrics), risk identification (hallucination, unsafe advice, calibration errors, subgroup bias, distribution shift), and experimental rigor including baselines, ablations, and statistical checks. Commonly asked in Machine Learning and Clinical NLP interviews because it probes both conceptual understanding of trade-offs and practical application skills for building safe, reliable medical QA or conversational systems within reproducibility and regulatory-sensitive data handling constraints.
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