Algorithmovigilance and HART: Building Trust in Healthcare AI
November 18, 2025
In the early 1960s, the sedative thalidomide was withdrawn from markets worldwide after thousands of infants were born with severe congenital disabilities. The drug was released without adequate testing or post-market surveillance, its dangers revealed only after tragedy struck. From that loss emerged a new discipline—pharmacovigilance—the systematic, continuous monitoring of drugs after approval. It transformed patient safety from a one-time regulatory act into a living, learning process.
Today, healthcare stands at a similar inflection point. Artificial intelligence is becoming medicine’s newest therapeutic tool—one capable of diagnosing, predicting, and guiding treatment—but it evolves continuously. Unlike a pill, an algorithm does not remain stable once released. It learns, updates, and drifts. The same adaptive power that makes AI transformative also makes it unpredictable. We therefore require a new form of vigilance: algorithmovigilance, the post-deployment discipline that keeps AI accountable long after launch.
When Algorithms Drift
In previous articles, I described how the Healthcare AI Review and Transparency (HART) initiative would create a national clearinghouse for evaluating AI before deployment and maintaining a registry of its ongoing performance. But evaluation alone cannot prevent drift. Every AI system—whether predicting sepsis risk, prioritizing imaging reads, or summarizing clinical notes—slowly diverges from its original calibration as data, populations, and clinical practices change.
Unseen drift transforms reliability into risk. A model trained on one institution’s patient mix may perform poorly for another institution. Even routine software updates can unintentionally alter an algorithm’s behavior, introducing or amplifying bias if not re-validated after deployment.
Without active monitoring, these deviations can persist undetected until patient outcomes expose the problem. In pharmacology, such latent risks led to congenital disabilities and fatalities. In AI, they threaten trust—the most valuable asset in healthcare.
From Certification to Continuous Accountability
Traditional regulation was designed for static products: a stent, a scanner, a pill. AI demands a different rhythm—continuous observation rather than episodic inspection. Algorithmovigilance converts oversight from a checklist into a feedback loop. Its goal is not punishment but protection: to detect deviation early, correct it rapidly, and learn from every incident.
Like pharmacovigilance, algorithmovigilance operates on three layers:
- Technical surveillance—automated systems that track AI outputs, identify bias or drift, and flag anomalies in real time.
- Human oversight—clinicians, engineers, and ethicists who interpret the data and determine whether deviations represent innovation or error.
- Organizational accountability—a governance structure ensuring that warnings trigger measurable corrective action rather than vanish into bureaucracy.
These layers together form the immune system of digital medicine. They keep algorithms healthy by continuously measuring their behavior in the real world, not just in the laboratory.
Why HART Must Host Algorithmovigilance
Most healthcare organizations lack the scale or expertise to manage algorithmic safety on their own. Even large health systems struggle to maintain the staff, infrastructure, and expertise required for continuous model evaluation. Without coordination, each institution is forced to reinvent the same safety processes, duplicating cost and fragmenting learning.
HART offers the missing architecture. As a national public–private partnership, it can perform algorithmovigilance as a shared service—pooling data, expertise, and analytic infrastructure for all participants. Under HART, post-deployment monitoring would not depend on individual hospitals’ resources but on a common framework accessible to every clinician and developer.
Each registered algorithm would contribute de-identified performance data to the HART network, allowing continuous benchmarking across sites and populations. When drift or bias appears, HART would issue alerts, recommend mitigation, and document the response in its public registry. This transparency transforms safety into collective intelligence. Instead of one hospital quietly learning from failure, the entire system learns together.
Human Judgment at the Center
Technology alone cannot sustain trust. Algorithmovigilance depends on clinicians—the stewards who understand both the promise and peril of automation. Their observations, combined with patient feedback and real-world outcomes, complete the monitoring loop. Each alert or anomaly must be interpreted through the lens of clinical context and moral responsibility.
As discussed in Future Healthcare 2050 and throughout this series, humility is healthcare’s most enduring safeguard. Great physicians admit uncertainty; responsible algorithms must do the same. A model that signals “I’m not confident” invites review rather than blind acceptance. Embedding that humility into AI systems ensures they serve as partners in judgment, not replacements for it.
Through continuous collaboration among clinicians, data scientists, and ethicists, algorithmovigilance turns human oversight into a daily practice rather than an afterthought. It recognizes that safety emerges not from perfect code but from persistent curiosity about how technology behaves once it meets reality.
A National Discipline of Learning
The need for algorithmovigilance extends beyond compliance—it defines the next phase of healthcare modernization. Every instance of AI drift, every bias uncovered, and every corrective action documented becomes a contribution to a living library of digital safety. Over time, this shared intelligence can guide developers toward better model design, inform regulators about emerging risks, and help clinicians understand where AI adds value and where it does not.
Pharmacovigilance taught us that vigilance must be institutional, not optional. The same lesson applies now. Individual vigilance saves patients; collective vigilance transforms systems. By housing algorithmovigilance within HART, we can ensure that no healthcare organization stands alone in the task of monitoring its AI. Pooling expertise through a central, transparent framework transforms fragmented oversight into coordinated learning.
The Collaboration Ahead
The future of healthcare AI depends on collaboration, not competition. Vendors must share model-performance data openly. Hospitals must contribute outcome metrics without fear of exposure. Regulators must provide flexible pathways that reward transparency rather than secrecy. Together, these actions will form the social contract that keeps AI aligned with medicine’s moral compass.
HART can serve as the trusted convener of this contract—a national infrastructure for algorithmovigilance that ensures innovation never outruns accountability. Its mission echoes the regulatory awakening that followed thalidomide: to transform crisis into confidence through continuous observation and shared responsibility.
AI, like any therapeutic, evolves. Our oversight must evolve with it. Only through collaborative vigilance—technical, clinical, and moral—can we ensure that artificial intelligence remains humanity’s servant, not its risk.
Trust is what keeps healthcare moving forward.
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