Much of the debate about artificial intelligence (AI) in medicine still turns on a single question: Is the model accurate? Accuracy matters, but it is a poor proxy for whether patients are actually helped. A diagnostic algorithm can produce a technically correct prediction and still sit at the center of preventable harm because the prediction is only the first link in a long chain of human and organizational actions. The most accountable AI system is not the one that produces the right answer, but the one that reliably converts that answer into appropriate, completed care for every patient. Building on emerging accountability and governance scholarship, we propose the concept of care-pathway accountability—accountability that extends from detection through assignment, action, communication, and completion—and propose what health systems must measure, design, and govern to achieve it.
AI Can Be Right While the System Still Fails
The distinction that anchors this argument is between technical performance and care-delivery performance. A clinical AI tool operates inside a socio-technical system: clinicians, workflows, inboxes, scheduling processes, and governance structures all shape whether a correct output produces a correct outcome. Habli, Lawton, and Porter (2020) make the point directly in their analysis of accountability and safety, arguing that the safety of clinical AI cannot be resolved at the design stage alone and depends on a dynamic, deployment-aware model of assurance rather than on predictive performance in isolation.
This pattern is not exotic. Long before contemporary AI, clinical decision support (CDS) research documented that correct alerts and recommendations are frequently ignored or only partially acted upon, owing to alert fatigue, ambiguous responsibility, and competing clinical priorities. AI does not eliminate that gap; if anything, it widens the volume of recommendations flowing into workflows that were never designed to guarantee follow-through. Evaluating such a tool solely on accuracy or area under the curve, therefore, measures the wrong thing. What matters clinically is whether the recommendation reliably results in completed care.
Accountability Extends Beyond the Prediction
If accuracy is insufficient, where should accountability attach? An emerging body of work argues that it must span the full lifecycle of an AI system rather than concentrating on the moment of prediction. Alelyani (2025) presents an empirically validated framework for responsible AI in healthcare autonomous systems whose dimensions span technical, ethical, and operational categories—covering not only model development but also deployment, clinical integration, and post-deployment monitoring. Donia (2025), drawing on science and technology studies, reinforces this from a different angle: accountability in health systems is distributed across human and non-human actors and is always tied to existing clinical practices, so it cannot be located in the algorithm alone.
These perspectives support a layered account of what “accountable AI” must cover along the care pathway:
- Prediction accountability—Is the result technically sound, calibrated for its population, and communicated in a usable form?
- Process accountability—Is there a defined owner for triage, task assignment, and execution of the recommended follow-up?
- Outcome accountability—Does the organization monitor whether care was actually completed, and whether disparities emerge in who receives that completed care?
Framed this way, the detection → assignment → action → communication → completion chain is an accountability structure. Each transition is a point at which responsibility can be assigned—or quietly dropped. The governance question is whether every link has a named owner and a mechanism to detect when the chain breaks.
AI Exposes Existing Ownership Gaps
One of AI’s most useful and uncomfortable effects is diagnostic in a second sense: it surfaces pre-existing fragmentation in care. Responsibility for following up on incidental or high-risk findings has long been ambiguous and unevenly distributed among radiology, the emergency department, outpatient specialists, and primary care. AI-generated risk scores and follow-up recommendations do not create this ambiguity; they make it concrete and visible because there is now an explicit recommendation whose non-completion can be traced.
Donia (2025) is again instructive: because algorithmic accountability is distributed and often obscured across professionals, information systems, and organizational structures, introducing an algorithm can render that distribution legible without automatically resolving it. A risk score can reveal a gap where no service line fully “owns” the patient once a risk is identified. The alerts and dashboards that AI introduces become new boundary objects—shared artifacts that sit between departments and demand explicit governance to assign responsibility among radiology, the ED, outpatient specialists, and navigation teams. Left ungoverned, a boundary object becomes a place where accountability evaporates: everyone can see it, and no one owns it.
The implication for leaders is that deploying clinical AI is partly an organizational-design exercise. The tool will expose the seams in the care pathway; the question is whether the organization treats those seams as defects to be assigned and closed, or allows the technology to industrialize a longstanding handoff failure.
Measure Completed Care, Not Just Model Performance
If accountability spans the pathway, so must measurement. Standard evaluation—accuracy, sensitivity, specificity, AUC—describes the model, not the care. Both the responsible-AI framework advanced by Alelyani (2025) and the stewardship model described by Lee (2025) point toward end-to-end evaluation that includes operational and equity outcomes, not only predictive quality. The CAOS framework, in particular, emphasizes ongoing monitoring for algorithmic drift, bias, and disparities (Lee, 2025); that same monitoring logic can and should be extended from model behavior to care completion.
A health system serious about care-pathway accountability might track a small set of process-and-outcome metrics alongside its model metrics:
- Time from AI detection to assignment of a responsible clinician
- Proportion of AI-identified high-risk cases with documented outreach to patient and provider
- Rate of recommended orders/referrals placed and completed, stratified by demographic and socioeconomic subgroup
- Number and proportion of cases unresolved past defined time thresholds
Stratification is essential rather than optional. An aggregate completion rate can look healthy while masking systematic gaps for patients who are harder to reach, less richly insured, or less able to navigate the system. Measuring completion without accounting for its distribution risks certifying an inequitable process as successful.
Design for Predictable Failure
Resilience engineering and safety science offer a further reframing: robust systems are designed around their failure modes rather than around their ideal functioning. Habli et al. (2020) argue that safety considerations are not fully resolvable before deployment, which means failure must be anticipated in operation rather than assumed away in design. Clinical AI introduces reasonably predictable failure modes—automation bias, silent non-use, alert overload, and orphaned recommendations—that prospective hazard analysis can identify in advance.
Both the responsible-AI framework (Alelyani, 2025) and the CAOS stewardship model (Lee, 2025) recommend systematic risk assessment and continuous monitoring to detect drift, inequities, and operational breakdowns. Applied to the care pathway, this yields a concrete “design-for-failure” checklist:
- Enumerate predictable failure scenarios—unreachable patients, clinician departure or turnover, out-of-network referrals, ambiguous recommendations, and conflicting guidelines.
- Define escalation pathways—for example, auto-routing a case to a patient-navigation team after a set number of days without documented action.
- Name a system owner for stalled cases—a role explicitly responsible for resolving cases that fall out of the normal flow and for closing the loop.
The unifying principle is that a “correct” prediction should never depend on everything going right. Accountable design assumes that inboxes will be missed, staff will leave, and patients will be hard to reach—and builds the backup pathways that keep those normal frictions from becoming clinical harm.
Connect AI Governance to Operations
None of the above is achievable if AI governance remains siloed within IT, data science, or compliance. Governance bodies constituted only of technical and legal functions can evaluate a model. However, they cannot assign operational ownership, redesign a workflow, or allocate the staffing required to close the loop. Effective governance must therefore be wired directly into clinical operations.
In practice, this means AI oversight bodies should include representatives from clinical services, scheduling, patient navigation, health equity, and patient experience alongside informatics and data science—and, crucially, should own the full pathway from model deployment to care completion, including the resource and workflow decisions that ownership entails. Cultivating a culture in which clinicians and administrators genuinely feel responsible for an AI tool’s downstream effects—rather than regarding the algorithm as an external authority—is part of the same shift. Governance, on this view, is not a gate the model passes through once; it is the standing organizational structure that keeps prediction tied to completed care.
Accountable AI as Accountable Care
Pulling these threads together yields a definition. An accountable AI system in healthcare is (a) technically robust, appropriately calibrated, and transparent; (b) embedded in workflows with clearly assigned ownership for each step from detection to completed care; (c) continuously monitored for outcomes and equity impacts, with mechanisms to respond when the pathway fails or disparities appear; and (d) governed by structures that tie AI oversight directly to clinical operations and patient experience.
That definition reframes the central question. The measure of a clinical AI system is not whether it produces the right answer. It is whether the organization around it ensures that the answer reliably results in appropriate, completed care—for every patient, not just the easy ones. Accuracy is where accountability begins; completed, equitable care is where it has to end. The most accountable AI, in the end, is inseparable from accountable care.
Read more about AI Accountability in our recent article published in Healthcare IT News: AI accountability is now healthcare’s next big challenge
References
Alelyani, T. (2025). A validated framework for responsible AI in healthcare autonomous systems. Scientific Reports, 15, Article 25266. https://doi.org/10.1038/s41598-025-25266-z
Donia, J. (2025). Algorithmic accountabilities and health systems: A review and sociomaterial approach. Big Data & Society, 12(2). https://doi.org/10.1177/20539517251334099
Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: Accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. https://doi.org/10.2471/BLT.19.237487
Lee, A. G. (2025). Navigating healthcare AI governance: The Comprehensive Algorithmic Oversight and Stewardship (CAOS) Framework for risk and equity. Health Care Analysis. Advance online publication. https://doi.org/10.1007/s10728-025-00537-y
