Editing AI in Healthcare (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a clinical AI deployment pipeline:

'''1. Problem scoping with clinical partners'''
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Clinical question: What outcome do we want to improve?
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Is AI the right intervention, or is workflow redesign better?
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Define: input (data available at decision time), output (action/alert/score)
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Establish performance requirements: sensitivity/specificity targets for clinical context
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Map to regulatory pathway: FDA, CE Mark, or research tool only?
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'''2. Data and model development'''
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EHR/PACS data extraction → IRB approval → de-identification
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Data quality audit (missing rates, coding inconsistencies, imaging protocol variation)
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Annotation: clinical expert labeling with inter-rater reliability measurement
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Stratified split: ensure similar demographics/disease severity across train/val/test
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Model training with class balancing, clinical augmentation
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Internal validation: AUC, calibration, subgroup analysis
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External validation: different institution's data
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'''3. Deployment and monitoring architecture'''
* Integration with EHR/PACS via FHIR or DICOM standards
* Real-time inference with latency < 30 seconds for urgent alerts
* Confidence display: show model certainty to clinician, not just binary output
* Override tracking: log when clinicians override AI recommendations (valuable training signal)
* Continuous performance monitoring: weekly AUC on new cases with known outcomes
* Drift detection: alert if input data distribution changes significantly

[[Category:Artificial Intelligence]]
[[Category:Healthcare]]
[[Category:Machine Learning]]
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