AI Diagnostic Support Developer Interview Questions

Explain DICOM as a file format and protocol for medical imaging, covering pixel data, metadata (patient info, modality, scanner parameters), and its role as the universal interchange format in radiology.

Define both metrics with clinical intuition: sensitivity = ability to catch true positives (sick patients correctly flagged), specificity = ability to rule out true negatives (healthy patients correctly cleared), and why both matter for patient safety.

Cover PHI (Protected Health Information), the need for de-identification or proper authorization, data encryption at rest and in transit, and the legal consequences of non-compliance.

Explain pre-training on large datasets (ImageNet, RadImageNet) then fine-tuning on smaller, expensive-to-annotate medical datasets, reducing data requirements and improving convergence.

Describe FHIR as the modern interoperability standard for healthcare data exchange, how AI systems read/write clinical resources (Observation, DiagnosticReport), and its RESTful architecture.

Discuss strategies: focal loss or class-weighted cross-entropy, oversampling techniques (SMOTE for tabular features, augmentations for images), threshold tuning with precision-recall curves, and evaluation using AUPRC rather than AUROC.

Cover document chunking strategy for clinical guidelines, embedding with a domain-specific model (e.g., MedCPT), vector store selection (Pinecone, Weaviate), retrieval with reranking, prompt engineering with guardrails, and citation of source documents.

Highlight MONAI's domain-specific transforms (intensity normalization for CT/MRI, spatial cropping respecting anatomical priors), pre-built architectures (UNet, SwinUNETR), pre-trained models, and its integration with medical data loaders.

Discuss DVC (Data Version Control) with S3/Azure Blob backends, dataset registries, hash-based deduplication, and the importance of reproducibility in regulated environments where you must prove which data version trained which model.

Describe OMOP CDM as a standardized relational schema for observational health data, its use in multi-site studies and federated analytics, and how it enables consistent feature engineering across institutions.

Cover shadow deployment (both models score, only baseline is shown), prospective validation design, clinician blinding, sample size calculation for non-inferiority, and primary endpoints (diagnostic accuracy, time-to-diagnosis, false alert rate).

Discuss annotation scheme design (BIO tagging), using pre-trained clinical BERT models, annotation tools (Prodigy, Label Studio), inter-annotator agreement measurement, and post-processing with medical ontology linking (UMLS CUI mapping).

Address data residency requirements, network air-gapping, hardware constraints (GPUs in hospital data centers), latency requirements for real-time diagnostics, IT governance, and the role of containerized deployments (Docker/K8s on-prem).

Define calibration as the alignment between predicted probabilities and observed frequencies, discuss reliability diagrams and ECE metrics, and explain why a miscalibrated model at 90% confidence that is only 70% accurate could cause harmful clinical decisions.

Discuss missingness mechanisms (MCAR, MAR, MNAR), imputation strategies (MICE, learned embeddings), clinical domain knowledge to distinguish meaningful absence (no lab ordered) from true missingness, and robustness validation.

Describe architecture choices: separate encoders for each modality (ViT for images, ClinicalBERT for text, MLP for labs), fusion strategy (cross-attention, late fusion with gradient boosting), training with multi-task loss, and evaluation with modality ablation studies.

Cover FedAvg or FedProx algorithm selection, client-server architecture, differential privacy noise injection, communication-efficient techniques (compressed gradients), handling non-IID data distributions across sites, and aggregation strategies for heterogeneous data quality.

Discuss the SaMD risk categorization framework (severity of condition × role of AI output), 510(k) vs. De Novo vs. PMA pathways, the Predetermined Change Control Plan for adaptive algorithms, clinical validation requirements, and Good Machine Learning Practice principles.

Explain covariate shift vs. concept shift detection (MMD, classifier two-sample test), continuous monitoring with statistical process control charts, domain adaptation techniques, recalibration strategies, and escalation protocols when shift is detected.

Cover multi-level explanations: pixel attribution (Grad-CAM++), concept-based explanations (similar case retrieval from a curated atlas), counterfactual analysis (what lesion features would change the prediction), and structured confidence reporting with uncertainty quantification.

Discuss modality-specific preprocessing pipelines, domain adaptation between scanner vendors, age-stratified model evaluation, population-specific calibration, and whether to build one generalist model or a mixture-of-experts architecture.

Cover self-supervised pre-training (DINO, MAE) on large unlabeled medical imaging corpora, contrastive learning across modalities, adapter-based fine-tuning for downstream tasks, and benchmarking against task-specific models on public leaderboards.

Discuss SMART on FHIR application framework, alert prioritization algorithms, clinician-configurable thresholds, workflow integration points (order entry, results review), UX principles for alert design, and metrics for measuring alert fatigue (override rate, time-to-action).

Cover stratified evaluation across protected attributes, fairness metrics (equalized odds, predictive parity, calibration across groups), bias mitigation at data level (re-sampling, augmentation), model level (adversarial debiasing), and post-hoc (threshold adjustment per subgroup).

Discuss stream processing architecture (Kafka, Flink), temporal models (LSTM, Transformer on time-series), feature engineering from high-frequency vitals, alert generation with lead-time optimization, integration with nurse call systems, and false alarm minimization strategies.

Systematically investigate: data distribution differences (scanner vendor, patient demographics, disease prevalence), preprocessing inconsistencies, annotation quality gaps, and then apply domain adaptation, recalibration, or targeted fine-tuning on partner hospital data.

Conduct root-cause analysis (false positive classification), review the model's explanation for that case, check calibration at that probability threshold, implement a confidence-based escalation protocol, add a human-in-the-loop review step, and communicate transparently with the clinical team.

Discuss the Predetermined Change Control Plan concept, maintaining the approved model in production while validating the new model in shadow mode, preparing regulatory submission materials for the update, and defining version transition protocols with rollback capabilities.

Prioritize by conducting a systematic re-annotation audit, quantify the impact on model performance with error analysis, retrain on corrected data with validation, assess whether the deployed model's errors are clinically consequential, and implement quality gates for future annotation workflows.

Redesign for edge deployment: model compression (quantization, pruning, knowledge distillation), optimize for CPU inference or low-power GPUs, implement offline-first architecture with sync-when-connected, and validate performance degradation is clinically acceptable.

Apply few-shot and meta-learning techniques, leverage transfer learning from related common conditions, use aggressive data augmentation validated by domain experts, consider synthetic data generation (GANs), prioritize high sensitivity over specificity, and design prospective validation with clinical partners.

Explain non-IID data challenges in federated learning, strategies like FedProx or scaffold for handling heterogeneity, weighted aggregation proportional to dataset size and quality, per-site calibration after federation, and validation on a balanced holdout set.

Describe clinical governance: the AI is a decision support tool, not a decision maker; the physician always has override authority; the override should be logged for retrospective analysis; the system should provide sufficient explanation to facilitate productive clinical discussion, not mandate compliance.

Implement RAG with verified medical sources, constrain generation with structured output schemas, add a factuality checker that cross-references claims against source documents, use medical-specific models (Med-PaLM, GatorTron), and always require human review before any LLM output reaches a patient record.

Apply post-hoc calibration (Platt scaling, isotonic regression) to convert logits to well-calibrated probabilities, design a clinician-friendly confidence tier system (high/medium/low with actionable guidance), validate calibration on held-out data, and add UI components that present uncertainty clearly.

Walk through: DICOM ingestion and de-identification, metadata extraction, quality filtering, annotation workflow, train/val/test splitting (patient-level), model training with MONAI, experiment tracking with W&B, model registration in MLflow, containerized deployment with Docker, and API endpoint with FHIR-compatible response format.

Describe the agent architecture: document loaders for clinical guidelines and PubMed abstracts, text splitting with medical-aware chunking, embedding with a domain-specific model, retrieval from a vector store, a medical prompt template with chain-of-thought reasoning, output parsing into structured differential diagnosis format, and guardrails against hallucination.

Describe: code in GitHub with CI/CD via Actions (lint, test, build), training jobs triggered on SageMaker with MLflow tracking server, model registry with staging/production stages, automated evaluation gates (performance thresholds, bias checks), deployment to SageMaker endpoints with canary rollout, and monitoring with CloudWatch and custom drift detection.

Explain the interactive annotation workflow: set up MONAI Label server, connect to 3D Slicer or OHIF viewer, use pre-trained models for auto-segmentation suggestions, implement active learning to prioritize the most informative slices for expert review, and iteratively improve the model as annotations accumulate.

Cover: project and run organization, logging metrics (AUC, sensitivity, specificity per subgroup), artifact tracking (datasets, models), sweep configurations for hyperparameter optimization, report generation for clinical stakeholders, and integration with W&B Tables for error analysis on individual predictions.

Describe: input feature drift detection (population stability index, KS test), prediction distribution monitoring, reference data baselines, automated alerts via PagerDuty, periodic re-validation against newly curated ground truth, and retraining triggers with human approval gates.

Cover: selecting a base model (BioGPT, ClinicalBERT), loading a radiology report dataset (MIMIC-CXR reports), tokenization with domain-specific tokenizer or standard tokenizer, fine-tuning with the Trainer API, evaluation with ROUGE and clinical accuracy metrics, and sharing the model on HF Hub with a model card documenting intended use and limitations.

Describe: FLARE system architecture (server + clients), site provisioning with secure communication, defining the federated training workflow (e.g., FedAvg), configuring local training scripts, handling non-IID data with weighted aggregation, monitoring convergence across sites, and evaluating the global model on a held-out test set.

Cover: defining the study population and inclusion/exclusion criteria, ground truth establishment (adjudicated expert panel), sample size calculation, prospective vs. retrospective study design, primary/secondary endpoints, statistical analysis plan, STARD-AI reporting guidelines compliance, and stakeholder communication of results.

Describe: registering a SMART on FHIR app, obtaining authorization via OAuth2, querying patient context (Patient, Observation, DiagnosticReport resources), rendering AI results in an embedded iframe, handling launch contexts (patient-level vs. encounter-level), and testing with sandbox EHR environments like SMART Health IT Sandbox.

Demonstrate clear communication skills, ability to translate technical metrics into clinical impact, empathy for stakeholder concerns, and a constructive approach to collaborative problem-solving without overselling or underselling the technology.

Show integrity, proactive identification of issues (bias, privacy, safety), willingness to escalate concerns through proper channels, ability to propose actionable solutions, and commitment to responsible AI principles even under time pressure.

Reference concrete habits: reading top conferences (MICCAI, ML4H, CHIL), following key researchers on Twitter/X, participating in Kaggle medical imaging challenges, engaging with communities (Hugging Face, MONAI), attending webinars, and maintaining a personal learning log or blog.

Demonstrate self-awareness, growth mindset, ability to conduct honest post-mortems, extract actionable lessons, and apply them to future work. Ideally the story involves a technical failure that led to a process or architectural improvement.

Show maturity in risk management: phased rollouts (shadow mode, limited deployment), minimum viable validation (clinically meaningful thresholds, not perfection), transparent communication of known limitations, and always maintaining patient safety as the non-negotiable priority.