The healthcare sector is a leader in AI adoption. Its approach to clinical AI deployment is closely watched, making rigorous safety checks crucial before launch.
Healthcare and life science teams must lead by example, modeling the highest standards for AI governance, HIPAA compliance, bias mitigation, de-identification, and Human-in-the-Loop (HITL) safeguards.
Add the following steps to your pre-deployment checklist.
1. AI Governance
AI safety starts with strict oversight. Every clinical AI project needs a comprehensive governance policy that establishes oversight committees. These committees should comprise the following stakeholders:
- Clinicians
- IT Specialists
- Lawyers and legal teams
- AI ethicists
- Data scientists
These stakeholders bring valuable insight to the table, ensuring a holistic approach to governance that covers every data point.
An example of clinical AI governance at work is the hiring of a professional life sciences law firm to oversee the legal aspects of digital therapeutics and HIPAA authorizations. Another example is the Data Protection Impact Assessment (DPIA). DIPAs identify and mitigate privacy risks before patient data is processed.
Governance policies must clarify vendor due diligence mandates, ensuring there are no instances of AI hallucinations in data sets. All AI vendor data sets should be available for review before development.
2. HIPAA Compliance
HIPAA compliance must reside at the heart of every clinical AI platform.
By law, HIPAA mandates that individuals own their health data, and organizations authorized to handle that data must take extra measures to ensure data privacy. This means defining strict communication of protected health information (PHI) by task and role, also known as data minimization.
Building AI systems with multi-factor authentication (MFA) and encryption prevents outside parties from accessing sensitive patient data. AI data services that work with healthcare organizations must enter into Business Associate Agreements (BAAs), acknowledging their own responsibility for HIPAA compliance.
Clinical AI training data is also subject to HIPAA compliance. Consent provenance verifies that any patient data used for training was obtained through HIPAA-compliant channels.
3. Mitigating AI Bias
AI-powered systems must go through a rigorous validation process that checks for AI data biases. This process involves multiple tests on local and diverse datasets. It’s important to avoid any spectrum bias in a model, which can occur if an AI model is trained on the wrong target population.
According to studies published in the National Library of Medicine, biases in AI data may also be caused by:
- Imbalanced sample sizes in training data
- Missing EHR data (due to socioeconomic factors)
- Label Misclassification
Without proper review, AI models may reflect a disconnect between sample populations and real-world patients.
4. De-identification and HITL Safeguards
AI models require human-in-the-loop (HITL) reviews to ensure HIPAA-defined identifiers are removed from unstructured data, like clinician notes, to ensure strict PHI data privacy.
It must be noted that clinical AI systems are digital assistants, not autonomous decision-makers. Every AI-driven recommendation, like an AI-powered knowledge base, must be approved before deployment.
Develop Your Own Safe Deployment Strategy
Don’t leave any stone unturned in your clinical AI review. Meticulously test for vulnerabilities, from HIPAA compliance risks to algorithm biases, to ensure a safe system deployment. Assemble a strong oversight committee and implement a governance policy that acts as a guiding source of truth throughout every stage.