When discussing the implementation of AI in healthcare, the conversation often begins and ends with model accuracy. However, the reality is far more complex and human. That’s why the FDA introduced the Total Product Life Cycle (TPLC) framework for AI: a comprehensive roadmap for translating AI products into clinical practice with safety, effectiveness, and trust at the core.

What the TPLC reminds us is this: an AI model is only one part of a much larger product. If it’s going to operate in a real-world care environment, we have to plan for everything that comes before and after the algorithm itself.

Let’s walk through the key phases of the TPLC for AI in healthcare:

1.  Data Quality Foundation

Everything begins with data. We’re not just talking about having a lot of it — we’re talking about the right data, collected ethically and maintained with rigor. Poor data here often leads to poor outcomes later.

2.  Model Development

This includes training and tuning the model using the selected data. At this stage, clinical utility, fairness, and relevance should already be top of mind. AI is never “just math” — it’s built from human choices.

3.  Performance & Clinical Evaluation

Once the model performs well statistically, it still needs to demonstrate its effectiveness in the clinical domain. Does it generalize? Can clinicians trust it? Is it interpretable?

4.  Premarket Assurance

Before deployment, the AI must be evaluated for safety and efficacy, not just accuracy. This includes regulatory considerations and often requires multidisciplinary input from ethicists, clinicians, and technical experts.

5.  Deployed Model

At this point, the AI product enters the clinical ecosystem. But its journey is far from over — it now becomes part of clinical workflows, human decision-making, and organizational change.

6.  Model Monitoring

Live data enters the system, and the model must be continuously tracked for performance, bias, and unexpected behavior. This is not optional — it’s a clinical safety requirement.

7.  Feedback Loop & Retraining

Real-world performance leads to retraining, updates, or even complete overhauls. This is where many AI systems fail: they aren’t maintained, governed, or owned properly after deployment.

Six Questions to Ask Before You Deploy

Before you even begin the development process, TPLC encourages asking questions like:

• What is the clinical utility?
• Who are the stakeholders?
• Is your training data representative and up-to-date?
• Is your model interoperable with the real-world system?
• Will it interrupt or enhance clinical workflows?
• Who is responsible for ongoing maintenance?

The TPLC isn’t just a regulatory checklist. It’s a mindset — one that centers the patient, honors the clinician, and grounds AI in a real-world context. And in a healthcare system already strained by fragmentation and burnout, we owe it to everyone involved to build AI that’s not only smart, but safe, sustainable, and human-aware.

The FDA Just Made Post-Market Monitoring Non-Negotiable

I wrote the seven phases above before the FDA finished its homework. In January 2025, the agency issued draft guidance titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations FDA AI-enabled device lifecycle guidance, and it goes well past model monitoring as a nice-to-have. The guidance tells manufacturers to build a postmarket performance monitoring plan into the marketing submission itself, before the device ever reaches a patient FDA postmarket performance monitoring plan requirements.

That plan has to specify how the manufacturer will track drift, detect bias across subgroups, and figure out whether a performance problem originates in the data, the algorithm, or how clinicians are actually using the tool AI medical device post-market surveillance guidance. If that sounds like the FDA borrowed the concept from drug safety, it did. Researcher Kensaku Kawamoto and colleagues, building on work by Peter Embi, coined the term algorithmovigilance to describe exactly this: post-approval evaluation of an algorithm's real-world performance, modeled directly on pharmacovigilance's assessment, monitoring, and adverse-event tracking Algorithmovigilance, lessons from pharmacovigilance. For hospital leaders, the practical implication is this: if you're evaluating a vendor's AI tool, ask for their postmarket monitoring plan before you ask for their accuracy numbers. A tool with 94% sensitivity and no drift-detection plan is a bigger liability than one with 89% sensitivity and a documented recalibration cadence.

The FDA's Predetermined Change Control Plan, or PCCP, matters too. It lets a manufacturer pre-specify how they'll retrain a model without a brand-new submission every time FDA PCCP and Good Machine Learning Practice. That's efficient for vendors. It's also a governance question for you: does your contract require notifying your AI committee before a PCCP-covered update? If not, fix that before go-live.

Drift Isn't Theoretical — It's Measured, and It's Fast

I said in the original post that live data enters the system and the model must be continuously tracked. Here's why that's not caution for caution's sake. A longitudinal study of four deployed clinical AI systems published in PLOS Digital Health found that acceptable validation performance did not persist as a stable operational property after deployment — degradation was progressive across every system studied, and it typically showed up first as calibration drift rather than an obvious accuracy collapse Longitudinal evidence of post-deployment fragility in clinical AI. That's the uncomfortable finding for anyone who thought validation was a one-time hurdle: the researchers explicitly recommend treating validation as a baseline operating reference, not a durable safety guarantee.

Other research puts a number on how often you need to check. A study tracking a deployed clinical deterioration model for over two years found meaningful shifts tied to changing population prevalence Temporal stability and performance drift of a deployed AI clinical deterioration model, while a JAMA Network Open analysis of models tracking in-hospital mortality during COVID-19 found transfer learning and drift-triggered continual learning were both necessary to stop performance degrading across hospital types Detecting and remediating harmful data shifts in deployed clinical AI. Translation for the C-suite: a model validated at your flagship academic center won't automatically perform the same at your rural affiliate, and a model that worked in 2024 isn't guaranteed to work the same way in 2026.

This is where the six questions I listed in the original post need a seventh: how will you know when the model has drifted, and who is authorized to pull it offline? Bias and fairness research on the AI lifecycle points to the same governance gap — feedback loop bias, where an AI's own outputs get recorded in the EHR and later reused as training labels, can quietly amplify existing inequities unless someone is running subgroup audits and routine recalibration Bias and Fairness Across the Healthcare AI Lifecycle.

Who Actually Owns the Model After Go-Live?

The original post's sixth question — who is responsible for ongoing maintenance — deserves a full answer, because the accreditation landscape changed underneath hospital leaders in 2025. On September 17, 2025, the Joint Commission and the Coalition for Health AI jointly issued guidance stating that healthcare organizations should establish a governance structure to manage responsible AI use, including a mechanism to keep the governing body updated on uses, outcomes, and adverse events Joint Commission and CHAI hospital AI governance guidance. The guidance is explicit that this doesn't have to be a brand-new standalone committee — an existing body can carry the responsibility — but board-level accountability and reporting are expected either way.

In practice, the health systems getting this right assign three named owners to every production model: a clinical owner who confirms the use case still matches care standards, a technical owner who watches drift and performance, and a risk/compliance owner who holds access controls and shutdown authority Healthcare AI governance, risk, and compliance playbook. The American Hospital Association's trustee guidance frames it even more simply: clinicians remain accountable for clinical judgment, operational leaders remain accountable for business decisions, and the organization remains accountable for privacy, safety, and patient trust — no matter how good the vendor's marketing deck looks American Hospital Association AI governance framework for trustees.

Before your next AI deployment, three questions belong on the intake form, not the retrospective: Who is the named clinical owner and the named technical owner for this specific tool? What is the rollback procedure, and who has authority to invoke it without waiting for a committee meeting? And will performance and incident data on this tool reach the board on a defined cadence, or only if something goes wrong? If you can't answer all three today, you don't have a Total Product Life Cycle program — you have a procurement decision that stopped at go-live.

• Require vendors to disclose their postmarket performance monitoring plan and PCCP terms before signing, not after.
• Name a clinical owner, technical owner, and risk/compliance owner for every production AI tool — three people, not a committee, doing the daily watching.
• Set a subgroup-level recalibration schedule; don't wait for an obvious accuracy collapse, since drift shows up first as calibration drift, not a dramatic failure.
• Build board reporting on AI performance and incidents into existing quality or safety reporting cadences rather than creating a parallel process no one reads.

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