Cognitive fog in AI-assisted care: Preserving clinical reasoning, patient education, and shared decision-making.

Ask-Audit-Apply: A Framework for Clinical Reasoning with Artificial Intelligence.

Beyond Algorithms: Human-Centered Explainability for Clinicians, Patients, and Caregivers

Two short vignettes and a design guide to help human factors researchers create explanation systems that are practical and trustworthy are presented to show how explainability can become part of the care system instead of being treated as a separate technical feature.

Beyond the Algorithm: Artificial Intelligence, Clinical Decision-Making, and the Moral Ecology of Care in Africa

Artificial intelligence (AI) is increasingly promoted as a tool to enhance clinical decision-making and thus improve the quality of healthcare. While much of the emerging scholarship on AI and healthcare in Africa has focused broadly on opportunities and systemic challenges, what remains underexplored is the specific application of AI to clinical decision-making. This paper contributes to addressing this gap by offering a conceptual and critical analysis of AI-based clinical decision support systems (AI-CDSS) in African contexts. Drawing on philosophical accounts of medical reasoning and relational moral frameworks such as Ubuntu, the paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care. The paper further argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries. Moving beyond the algorithm, it proposes a shift toward relational and context-sensitive AI governance, including the development of relational impact assessments, the redistribution of responsibility across the AI lifecycle, and the co-production of knowledge with local stakeholders. While focusing on African clinical contexts, the analysis offers broader insights for global debates on AI ethics and clinical decision-making.

Medical AI Agents for Clinical Decision Support: Viewpoint Using the Planning, Action, Reflection, and Memory (PARM) Analytical Lens

Abstract Medical AI agents are emerging as a new generation of clinical decision support systems, moving beyond static prediction toward multistep, workflow-oriented assistance. This Viewpoint argues that agentic architectures incorporating planning, action, reflection, and memory (PARM) represent a meaningful evolution beyond traditional rule-based, machine learning, and multimodal clinical decision support systems. Using PARM as an analytical lens, we examine how medical AI agents can support diagnostic reasoning, treatment planning, and longitudinal monitoring while remaining constrained by human oversight. We further discuss the governance mechanisms required for responsible implementation, including bounded autonomy, auditability, verification protocols, postdeployment surveillance, and clear accountability structures. Rather than proposing autonomous modification of clinical judgment, this Viewpoint emphasizes agentic AI as a supervised workflow support paradigm. Safe implementation will require technical safeguards, institutional governance, regulatory clarity, and evaluation approaches that assess end-to-end task reliability, escalation behavior, and performance under deployment shifts.

Cognitive Friction in Clinical Decision Support: A Comparative Study of Judicial and Adjunct Human–AI Interaction Protocols

Artificial intelligence is increasingly used to support clinical decision making, yet concerns remain regarding algorithmic aversion, automation bias and the preservation of meaningful human oversight; while explainable AI aims to improve transparency, less attention has been devoted to the design of human–AI interaction protocols. This study investigates Frictional AI, an interaction paradigm that introduces cognitive friction to encourage critical engagement with AI recommendations. First, semi-structured interviews were conducted with a legal expert and a psychologist and analyzed through thematic analysis to identify legal, ethical, and cognitive requirements for AI-assisted decision support. Second, a user study involving 96 medical residents compared three interaction protocols: a conventional explainable AI-first design (XAI) and two friction-based protocols, namely a judicial protocol based on juxtaposed explanations (Judicial AI, JAI) and an adjunct protocol requiring an initial unsupported decision before AI exposure (AAI). Diagnostic accuracy and confidence, perceived usefulness, completion time, and reliance patterns were evaluated. The interviews highlighted the importance of human-centered explanations, contrastive reasoning, preservation of professional responsibility, and the role of user studies in evaluating human–AI interaction. The quantitative results showed that none of the AI-assisted conditions improved diagnostic accuracy relative to the no-support baseline. However, JAI achieved performance comparable to the baseline, outperforming XAI and AAI, and exhibited the lowest level of over-reliance. Overall findings suggest that the effectiveness of decision-support systems depends not only on model performance and explanation quality but also on interaction design. In conclusion, while preserving diagnostic performance, judicial protocols showed promise in mitigating automation bias and promoting active cognitive engagement in clinical decision support.

Clinical cognition in the age of cardiovascular AI

Artificial intelligence (AI) is rapidly entering cardiovascular medicine through electrocardiography, imaging, wearable monitoring, risk prediction, heart failure management, and clinical decision support. Its value, however, should not be judged only by technical accuracy, speed, or computational sophistication. Cardiovascular care requires clinicians and teams to convert multimodal, longitudinal, incomplete, and context-dependent information into action under uncertainty. This Perspective argues that AI should be understood not as a replacement for clinical intuition, but as a cognitive instrument that reshapes how cardiovascular teams perceive, prioritize, reason, decide, communicate, and learn. Building on dual-process theories of clinical reasoning, the manuscript proposes that AI can support both rapid pattern recognition (System 1) and slower analytic reasoning (System 2), while also creating new vulnerabilities when automation bias, alert fatigue, poor explainability, dataset shift, hidden inequity, or responsibility drift distort judgment. The central standard should therefore move from algorithm-centered performance to accountable intelligence: AI that is accurate, explainable, locally validated, equitable, auditable, monitored across its lifecycle, and embedded within explicit clinical governance.