Four laws. One message: if your AI touches patients, you need independent verification.
California has enacted the most comprehensive healthcare AI regulatory stack in the United States. SB 1120 prohibits AI-only coverage denials. AB 3030 requires GenAI disclosure. AB 489 bans AI from impersonating clinicians. AB 2575 would eliminate the "the doctor should have caught it" defense for AI vendors. AVAAS provides independent evaluation of clinical AI systems for demographic bias, training data representativeness, and disparate impact — before a patient is harmed and before a lawsuit is filed.
Request evaluationon appeal (nH Predict litigation)
violation penalties
"doctor override" defense
Four laws, mapped to AVAAS capabilities
California's healthcare AI regulations form a layered compliance stack. Each law addresses a different dimension of AI risk. AVAAS addresses all four.
SB 1120 — Physicians Make Decisions Act
Prohibits AI-only coverage denials. Requires licensed physician review. AI must use individual patient data, not just population statistics. AI algorithms must be open to audit. Periodic review of AI performance required. Criminal penalties for willful violations.
AB 3030 — AI in Healthcare Services
Requires health facilities and physician offices to disclose when GenAI generates patient communications. Disclosure must appear prominently at the beginning of written communications and throughout chat-based interactions. Provider-reviewed communications are exempt but must be documented.
AB 489 — AI Impersonation Prohibition
Prohibits AI from using terms, titles, or design elements implying it is a licensed healthcare professional. Bans marketing language like "doctor-level" or "clinician-guided" unless genuinely supported by licensed professionals. Violations subject to Medical Board jurisdiction.
AB 2575 — Healthcare AI Disclosure & Liability
Requires disclosure of AI developer, funding source, foundation model, intended population, known risks, and training data demographics including known biases. Eliminates the defense that a physician's failure to override AI output severs the vendor's liability. Workers must be told they can override AI output.
What goes wrong without independent AI evaluation
These scenarios illustrate how California's healthcare AI regulations create liability for health systems, AI vendors, and insurers.
The utilization review denial
A health plan's AI denies a prior authorization for a cancer treatment. The patient is a 62-year-old Black woman. SB 1120 requires the denial to be based on her individual clinical circumstances, not population statistics. Post-hoc analysis reveals the AI's denial rate for Black women over 60 is 2.3x higher than for white women with identical clinical profiles. The health plan had no independent evaluation of the AI's demographic performance.
The diagnostic AI miss
A clinical decision support AI recommends against further workup for a 45-year-old Latina patient presenting with chest pain. The physician follows the AI recommendation. The patient later suffers a cardiac event. Under AB 2575, the AI vendor cannot assert that the physician's failure to override the AI is a superseding cause. Discovery reveals the AI's training data was 78% white male. No independent evaluation of demographic performance was conducted.
The malpractice premium increase
A medical group deploys five AI clinical tools without independent evaluation. Their malpractice carrier, reviewing the group's risk profile at renewal, identifies unassessed AI exposure. Premiums increase 15-25% across the group. Three competing medical groups in the same market have AVAAS certifications and receive preferred rates.
The class action
A patient advocacy group identifies that a major health system's AI triage tool consistently assigns lower acuity scores to patients who list Spanish as their primary language. The AI is not making an explicit language-based decision — the disparity flows through proxy variables correlated with language preference. A class action is filed under California's Unruh Civil Rights Act. The health system's defense: "we didn't know." The plaintiff's response: "you didn't look."
Three buyer types, one evaluation pipeline
California's healthcare AI regulations create compliance obligations for health systems, AI vendors, and the insurers who cover both.
Health systems & medical groups
Kaiser, Sutter, Cedars-Sinai, UCLA Health, Dignity Health, IPAs, and community health centers deploying clinical AI. You're deploying AI tools from vendors who may not have evaluated their systems for your patient population. AB 2575 requires you to disclose the AI's training data demographics to clinicians using it. SB 1120 requires your utilization review AI to be auditable. AVAAS evaluates each AI tool against your specific patient demographics — not the vendor's general claims.
Clinical AI vendors
Epic, Optum, Tempus, PathAI, Viz.ai, and clinical decision support startups selling to California health systems. Your buyers are about to ask for independent bias evaluation as a procurement condition. Under AB 2575, your liability isn't severed when a physician follows your AI's recommendation. Having an AVAAS certification before you go to market transforms a liability conversation into a trust conversation — and it differentiates you from vendors who haven't been independently evaluated.
Medical malpractice insurers
The Doctors Company, Cooperative of American Physicians, NORCAL Group, Medical Protective, and other carriers covering California physicians. Your insureds are deploying AI tools that create new liability exposure you can't currently quantify. AVAAS evaluation of your insureds' AI tools gives you the risk assessment data to price AI exposure accurately — and creates an incentive structure where physicians who evaluate their AI tools receive preferred rates.
How AVAAS compares for healthcare AI evaluation
Healthcare AI evaluation is an emerging field. The honest truth: most existing governance platforms weren't designed for clinical AI's unique challenges — demographic representativeness, proxy variable analysis, and malpractice-grade documentation.
Credo AI ($39M raised) and Holistic AI are strong governance platforms with healthcare capabilities. AVAAS is purpose-built for independent verification — it complements governance platforms rather than replacing them. A health system can use Credo AI for internal governance workflow and AVAAS for independent external verification.
What it costs — and what it prevents
Healthcare engagements are priced at the Enterprise tier due to the clinical complexity and malpractice-grade documentation requirements.
Full five-layer evaluation of one clinical AI tool. Training data demographic analysis. Per-demographic accuracy evaluation. Proxy variable detection. Prescriptive remediation. Litigation-ready certification report.
A health system deploying 5-10 clinical AI tools needs each one evaluated against its patient population. Annual re-evaluation as tools update. Enterprise agreements with volume pricing for health systems with 5+ tools.
33% discount for the first ten healthcare organizations. Priority access. Input into healthcare-specific evaluation methodology. Case study participation (optional). Name recognition as an AVAAS founding healthcare partner.
California's healthcare AI regulatory stack
California is building the most comprehensive healthcare AI regulatory framework in the United States. Each new law adds requirements — and each requirement creates evaluation obligations.
Four laws. One evaluation. Complete documentation.
California's healthcare AI regulations are the most comprehensive in the nation. Independent evaluation with causal attribution and malpractice-grade documentation is the standard of care for AI deployment. Don't wait for the claim.
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