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

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

OBJECTIVE This discussion paper conceptualizes cognitive fog as a communication and reasoning problem that can emerge when clinicians, patients, or organizations over-rely on artificial intelligence (AI) tools in health care. DISCUSSION Drawing on a selective, theory-oriented narrative synthesis of literature on AI-enabled clinical decision support, large language models, automation bias, cognitive offloading, patient-facing AI communication, and shared decision-making, this paper defines cognitive fog as a condition in which fluent, rapid, and institutionally embedded AI output blurs the boundary between assistance and authority. It comprises epistemic blurring, metacognitive weakening, and relational displacement. In clinical and patient-facing contexts, inaccurate or biased recommendations may reduce accuracy, while polished language can make generic education appear personal and automated recommendations appear deliberative. Management requires explicit role boundaries, AI literacy, cognitive forcing routines, visible human review, proportionate patient-facing disclosure, and governance that addresses workflow, accountability, equity, and patient understanding. CONCLUSION AI overreliance is not only a technical safety issue; it is also a health communication issue because it can cloud reasoning, weaken accountability, and narrow shared decision-making. Effective mitigation must address both individual barriers, including time pressure, variable AI literacy, and reluctance to challenge automated output, and systemic barriers, including weak governance, opaque tools, misaligned incentives, and inadequate monitoring.

Generative AI in the Care of Older Adults: A Position Statement From the American Geriatrics Society.

BACKGROUND Generative artificial intelligence (GenAI), particularly large language models (LLMs), is being integrated into healthcare documentation, decision support, patient education, administrative workflows, and emerging agentic systems capable of initiating clinical and operational actions. While GenAI may reduce clinician burden and support person-centered care, it also introduces risks such as misinformation, algorithmic bias, privacy harms, errors of omission, and automation over-reliance. These risks may be amplified for older adults because core geriatrics care tasks, such as goals-of-care discussions, capacity-sensitive consent, polypharmacy and deprescribing, and functional and cognitive assessment in the setting of multimorbidity, may be underrepresented in training data and are high-stakes in practice. METHODS The American Geriatrics Society (AGS) convened an interdisciplinary working and writing group, reviewed relevant literature and reports, incorporated input from multiple AGS committees, and completed review and approval through AGS committee processes in March 2026. RESULTS This AGS position statement translates core geriatrics principles, person-centered care, equity, shared decision-making, and promoting independence into actionable recommendations for clinicians, health systems, developers, policymakers, older adults, and care partners. Recommendations address ethical use, clinical integration and oversight, transparency and documentation, privacy protections, governance across the AI lifecycle, including pre, during, and post-deployment monitoring and accountability, and priority geriatrics use cases. CONCLUSION GenAI should augment, not replace, clinical judgment and relational care. Responsible use in geriatrics requires transparency, clinician-in-the-loop oversight, validation in older adult populations using age-relevant outcomes, and governance safeguards to protect dignity, safety, and equity.

From Algorithm to Bedside: A Clinician's Framework for AI in Practice

Abstract Artificial intelligence (AI) is entering clinical practice faster than the evidence base supporting it. Clinicians, who remain the licensed decision-makers at the bedside, increasingly find themselves as end-users of tools whose strengths, failure modes, and external validity they have had no opportunity to assess. This article offers a practical framework that does not require coding or mathematical literacy. We outline how AI is built, validated, deployed, and monitored, and where each phase typically goes wrong. We propose seven questions that clinicians can run through to evaluate any clinical AI tool in the time it takes to read an abstract, alongside a traffic-light schema for matching oversight to risk and a short list of demands clinicians should make of vendors and institutions. We then examine the deeper questions of equity, accountability, and the therapeutic relationship that AI is now forcing into view. AI literacy belongs alongside biostatistics and evidence-based medicine as a core clinical competency.

Focal, intervention-specific, and titrated: Which artificial intelligence tools are FIT for mental health care?

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

A dual-view approach that connects clinical practice with computational methods is presented, establishing a five-level competency scheme following Miller’s Pyramid and linking deductive, inductive, and abductive reasoning patterns to common medical goals and tasks.

The Use of Artificial Intelligence Systems in Mental Health Treatment: A Thematic Review

A list of eight best practices was created to assist developers with designing AI systems in a way that would reduce the overall risk of harm for users attempting to use their AI for mental health cases.