Remote monitoring is increasingly used in heart failure care, but most programs rely on uniform models that insufficiently reflect the heterogeneity of older adults, particularly with respect to frailty, cognitive impairment, functional dependency, and caregiver availability. This viewpoint argues that frailty should be considered a central determinant of remote monitoring design in older adults with heart failure, rather than a secondary modifier of conventional digital health pathways. Drawing on evidence from heart failure telemonitoring, geriatric medicine, and real-world cardiogeriatric experience, we propose a frailty-adaptive framework structured around four clinical trajectories: robust, prefrail, frail, and palliative. Each trajectory is associated with distinct monitoring objectives, workflow adaptations, and response pathways. Robust patients may benefit primarily from optimization, self-management support, and guideline-directed therapy titration; prefrail patients from early detection of deterioration and functional decline; frail patients from proxy-supported reporting, nurse-led triage, and rapid-access cardiogeriatric reassessment; and palliative patients from simplified symptom-guided monitoring focused on comfort, hospitalization avoidance, and caregiver support. This framework reframes remote monitoring as a stratified clinical process rather than a purely technological intervention. By aligning digital strategies with frailty status, functional capacity, and care goals, frailty-adaptive remote monitoring may improve clinical relevance, promote digital health equity, and support more sustainable models of care for older adults with heart failure.

Artificial intelligence (AI) in health care is increasingly defined not by static algorithms but by adaptive intelligence-systems that evolve over time through interactions with data, clinicians, and clinical environments. This adaptive capacity creates a structural mismatch with regulatory frameworks built for technologies whose behavior remains static. As AI models drift, recalibrate, or degrade in real-world contexts, they dissolve the linear boundaries between design, deployment, and clinical interpretation. These temporal, epistemic, and organizational frictions expose responsibility gaps that cannot be resolved through incremental modifications to legacy oversight structures. Regulators across major jurisdictions are beginning to respond to these challenges, though with differing orientations. The United States advances mechanisms for predictable adaptation, including Predetermined Change Control Plans, real-world evidence frameworks, and life cycle-oriented quality management reforms. The European Union emphasizes precautionary, rights-based governance through the European Union Artificial Intelligence Act (AI Act) and modernized liability rules. South Korea, operating within a hyperconnected digital health ecosystem, has introduced the Digital Medical Products Act (DMPA), one of the world's first comprehensive statutory frameworks for learning medical AI. Despite philosophical differences, these regulatory trajectories converge on a shared insight: learning AI systems cannot be governed by static rules or episodic evaluation. This viewpoint proposes Co-Lifecycle Governance as a conceptual framework to synchronize regulatory oversight with adaptive intelligence. Rather than treating oversight as a discrete event, Co-Lifecycle Governance frames regulation as a continuous, synchronized process grounded in 4 pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability. Each pillar functions as a structural antidote to the responsibility frictions that arise when AI systems evolve faster than expectations surrounding them. Together, these pillars provide a governance grammar capable of supporting safe, iterative model improvement while maintaining system-level trust. Drawing from the strengths of US predictability, European Union accountability, and Korean scalability, this paper outlines a hybrid convergence pathway that synthesizes predictability, accountability, and operational feasibility. Learning AI will not wait for governance to catch up; oversight must evolve in lockstep with adaptive intelligence. Co-Lifecycle Governance offers a foundation for regulatory systems that not only regulate learning AI but also learn with it-at the speed at which adaptive intelligence actually changes.