Beyond the Hype: Why Truly ‘Agentic’ Healthcare AI Remains Elusive

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Author: Denis Avetisyan

A new analysis reveals that inflated expectations and inconsistent definitions are hindering the responsible development and deployment of AI systems in healthcare.

The lack of standardized evaluation metrics and clear accountability frameworks poses significant risks as AI takes on more complex roles in patient care.

Despite growing claims of autonomous action, agentic artificial intelligence in healthcare remains firmly tethered to human oversight. This research, titled ‘The Doctor Will (Still) See You Now: On the Structural Limits of Agentic AI in Healthcare’, examines the conceptual and practical tensions surrounding these systems through interviews with developers, implementers, and end users. Our analysis reveals a misalignment between commercial promises and operational realities, fueled by inconsistent definitions of ‘agentic’ and a focus on technical benchmarks over sociotechnical safety-leading to diffused accountability when systems fail. How can we establish more robust evaluation frameworks and governance structures to responsibly integrate agentic AI into clinical practice?

The Evolving Landscape of Agency in Healthcare AI

The potential of agentic artificial intelligence to revolutionize healthcare is considerable, offering possibilities from personalized treatment plans to streamlined diagnostics and administrative tasks. However, the very definition of ‘agentic AI’ remains surprisingly ambiguous, hindering responsible development and deployment. This lack of clarity extends beyond mere semantics; it creates practical challenges in establishing clear lines of responsibility when these systems are integrated into complex clinical workflows. Without a shared understanding of what constitutes true agency – encompassing proactive goal-setting, independent problem-solving, and adaptability – healthcare providers and regulators struggle to adequately assess risk, ensure patient safety, and establish appropriate oversight mechanisms. Consequently, the rush to implement these promising technologies may inadvertently introduce unforeseen complications and ethical dilemmas, demanding a concerted effort to establish robust definitions and evaluation standards before widespread adoption.

The integration of increasingly autonomous artificial intelligence into healthcare presents a fundamental challenge: balancing the benefits of AI-driven efficiency with the crucial need for clear accountability. As AI systems gain the capacity to operate with less human oversight within intricate clinical workflows, determining responsibility for outcomes becomes significantly more complex. This isn’t simply a matter of technical error; the very nature of agentic AI-systems designed to proactively pursue goals-introduces novel scenarios where the line between intended function and unintended consequence can blur. Establishing robust frameworks for tracing decisions, understanding the rationale behind AI actions, and assigning appropriate responsibility is therefore paramount to ensure patient safety and maintain trust in these emerging technologies. Without such frameworks, the potential for improved healthcare could be undermined by legal ambiguity and ethical concerns.

Despite enthusiastic marketing surrounding ‘agentic AI’ in healthcare, current systems demonstrate a substantial gap between advertised capabilities and actual autonomy. A comprehensive review reveals that the vast majority – 83% – of evaluations prioritize technical correctness, focusing on whether the AI provides a factually accurate response in a given instance. However, a critically small 5% of assessments examine longitudinal behavior, meaning the system’s performance, safety, and potential for unintended consequences are rarely studied over extended periods or within the complexities of real-world clinical workflows. This imbalance presents a significant risk, as short-term accuracy does not guarantee reliable or beneficial performance when integrated into dynamic healthcare settings, and underscores the urgent need for more robust, long-term evaluations before widespread implementation.

Proactive Governance: Shaping AI’s Trajectory

Promissory Governance represents a regulatory approach for Agentic AI systems that prioritizes foresight by addressing potential risks and ethical implications based on anticipated future capabilities, rather than solely reacting to present functionality. This framework moves beyond current reactive regulation by establishing governance structures designed to adapt to evolving AI competencies. The core principle involves proactively identifying potential harms and benefits associated with increasingly sophisticated AI agents, and implementing safeguards before those capabilities are fully realized. This preemptive strategy aims to foster responsible innovation and ensure alignment with societal values as Agentic AI systems become more autonomous and impactful.

Robust evaluation frameworks for Agentic AI require expansion beyond current technical performance metrics, which predominantly assess isolated functionality. These frameworks must incorporate assessments within realistic, real-world deployment contexts to identify potential emergent behaviors and long-term reliability issues. This necessitates evaluating systems not only on their immediate correctness, but also on their sustained performance, adaptability to changing environments, and potential for unintended consequences when interacting with complex systems and human users. Comprehensive evaluation should encompass factors such as data drift, adversarial robustness, and alignment with intended goals throughout the system’s lifecycle, moving beyond laboratory conditions to simulate operational scenarios.

Current artificial intelligence evaluation practices are heavily weighted towards assessments of technical correctness, with a disproportionately small percentage – approximately 5% – dedicated to analyzing performance in sustained, real-world deployment scenarios. This imbalance creates a critical gap in understanding how agentic AI systems will behave over time, potentially leading to performance drift and unforeseen reliability issues. A shift toward longitudinal behavioral assessment is therefore crucial; evaluations must expand beyond isolated technical metrics to incorporate continuous monitoring of system behavior within its intended operational context. This proactive approach is necessary to identify and mitigate risks associated with long-term deployment and ensure sustained, dependable AI functionality.

Integrating Intelligence: Aligning AI with Clinical Reality

Effective integration of Healthcare AI into clinical settings is predicated on aligning the technology with existing clinical workflows, rather than attempting to fundamentally alter established processes. Successful deployment requires a detailed understanding of how clinicians currently perform tasks, including data input, decision-making, and communication. AI systems should be designed to complement these workflows, automating repetitive tasks and providing decision support without creating additional burden or disrupting established patterns of care. Ignoring existing workflows can lead to user resistance, implementation failures, and ultimately, a lack of adoption, regardless of the AI’s technical capabilities. Therefore, a human-centered design approach, prioritizing usability and seamless integration, is essential for realizing the potential benefits of Healthcare AI.

The majority of currently deployed healthcare AI systems operate as ‘Human-in-the-Loop Systems’, necessitating continuous human oversight for validation and intervention. This architecture limits the potential for full automation, as algorithms require clinician confirmation or correction before actions are implemented. The reliance on human review is driven by factors including regulatory requirements, the need to manage potential errors, and the complexity of clinical decision-making. While offering a degree of safety and accountability, this approach inherently restricts scalability and may introduce workflow bottlenecks, hindering the widespread adoption of otherwise promising AI technologies.

Current evaluations of Healthcare AI systems predominantly focus on initial performance metrics, neglecting the critical need for longitudinal behavior assessment. This assessment involves continuous monitoring of AI performance over extended periods to detect and mitigate performance drift – the degradation of accuracy or reliability over time due to changes in data distributions or clinical practice. Research indicates a significant gap in this area, with only 5% of current AI evaluations incorporating sustained performance monitoring as a key component. Without robust longitudinal assessment, the long-term reliability and clinical utility of deployed AI systems remain uncertain, potentially leading to inaccurate diagnoses or inappropriate treatment recommendations.

Managing the Risks: Building Trust in the Age of AI

The responsible integration of artificial intelligence into healthcare demands a proactive and comprehensive approach to risk management. Potential harms to both patients and healthcare providers must be systematically identified and mitigated throughout the entire AI lifecycle – from initial development and training, through deployment and ongoing monitoring. This necessitates moving beyond solely focusing on technical performance and incorporating assessments of bias, fairness, and potential for unintended consequences. Robust risk management frameworks aren’t simply about preventing errors; they are fundamental to building confidence in these systems and ensuring that AI serves to enhance, rather than compromise, the quality and safety of care. A failure to prioritize these considerations could erode trust and ultimately hinder the widespread adoption of potentially life-saving technologies.

Effective collaboration between clinicians and artificial intelligence necessitates carefully calibrated trust; simply accepting AI outputs without understanding their boundaries can lead to both over-reliance and inappropriate dismissal of valuable insights. A system’s limitations – encompassing data biases, potential for error in novel situations, and the scope of its intended function – must be transparent to the user. This isn’t about diminishing the potential of AI, but rather fostering a realistic understanding of its capabilities, allowing healthcare professionals to integrate AI assistance into their workflow with informed judgment. Without this calibration, clinicians risk either blindly accepting flawed recommendations or dismissing potentially life-saving suggestions, ultimately hindering the safe and effective implementation of AI in healthcare settings.

Current evaluations of healthcare AI overwhelmingly prioritize immediate technical accuracy, neglecting the critical need to understand long-term performance and true autonomy despite ambitious claims of ‘agentic’ capabilities. The research reveals a significant imbalance, with a mere 5% of assessment efforts dedicated to monitoring AI behavior over extended periods – a stark contrast to the focus on short-term, isolated tasks. This limited longitudinal evaluation hinders the development of genuine trust, as clinicians lack sufficient data to understand how these systems will adapt, generalize, or potentially fail in real-world clinical settings. Addressing this gap requires a fundamental shift in evaluation metrics, moving beyond simple correctness to incorporate measures of robustness, adaptability, and sustained performance, ultimately unlocking the full potential of AI to improve healthcare outcomes and foster confident human-AI collaboration.

The pursuit of agentic AI in healthcare, as the research elucidates, often prioritizes technical capability over demonstrable reliability. This echoes a fundamental principle of system design: complexity introduces decay. The article rightly points to the diffused accountability that arises when evaluation lags behind deployment – a precarious situation where the ‘chronicle’ of a system’s operation becomes more important than its inherent design. As Edsger W. Dijkstra observed, “It’s not enough to have good intentions; one must also have good mechanisms.” The absence of robust, deployment-centered evaluation metrics creates a situation where good intentions – the promise of improved healthcare – are insufficient to prevent systemic failures and erode trust.

The Horizon Recedes

The investigation into agentic AI within healthcare reveals a predictable pattern: the architecture of expectation consistently outpaces the material of implementation. The term itself, burdened with aspiration, obscures more than it illuminates. Every architecture lives a life, and it is becoming increasingly clear that this particular iteration faces the familiar challenge of premature designation. Capabilities are attributed before demonstrable consistency arrives, and the resulting diffusion of accountability is not a bug, but a feature of systems striving for complexity.

Future work must resist the temptation to define ‘agency’ and instead concentrate on the granular realities of deployment. Evaluation metrics, currently focused on isolated tasks, require a shift towards systemic assessment – how do these systems integrate (or fail to integrate) into existing clinical workflows, and what are the second-order effects of their interventions? Improvements age faster than anyone can understand them, so a continuous, longitudinal study of these systems-not as novelties, but as embedded components-is essential.

Ultimately, the question is not whether agentic AI will ‘solve’ healthcare, but whether its introduction accelerates or mitigates the inherent decay within the system. Time is not a metric; it’s the medium in which these architectures exist, and all architectures, regardless of their perceived intelligence, are subject to the same fundamental laws.

Original article: https://arxiv.org/pdf/2602.18460.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-25 03:38