Jul 2026· IEEE Reviews in Biomedical Engineering· Vol PP, pp. 1-20· 1 citation
Medicine
TL;DR
This survey provides a structured synthesis of how recent work connects foundation models to governable biomedical agentic systems and distills the recurring challenges and directions identified in the literature for reliable, accountable deployment.
Abstract
Biomedical AI is increasingly shaped by policy-bound, multi-step clinical workflows and non-stationary, multimodal data and tools. In this setting, the field is moving beyond static predictors toward agentic systems, enabled by foundation models that maintain task-relevant state and operate through a closed perceive$\rightarrow$plan$\rightarrow$act$\rightarrow$observe loop under explicit oversight. However, the field lacks a coherent account that defines biomedical agency, relates foundational model capabilities to agent behaviors, and traces the pathway from pretraining to domain-adapted, deployable systems. This survey offers such an account by synthesizing operational boundaries of agency and framing six core components (memory, planning, reflection, tool use, dialogue, and collaboration) as foundational agent-enabling capabilities that drive the transition from isolated pipelines to fully realized agents. This survey situates these perspectives along the model-building pathway, from pretraining through post-training adaptation to the orchestration mechanisms that operationalize agents. We highlight safety and governance considerations for high-stakes settings, emphasizing the fidelity of process and reasoning, uncertainty and abstention, privacy and provenance, and human oversight. Taken together, this survey provides a structured synthesis of how recent work connects foundation models to governable biomedical agentic systems and distills the recurring challenges and directions identified in the literature for reliable, accountable deployment.
In the healthcare and clinical domain, artificial intelligence (AI) is evolving from earlier models that primarily predicted outcomes or generated content toward agentic AI systems that demonstrate the capability to make decisions and complete tasks autonomously. Previous research on AI has contributed significantly to disease identification, deep learning applications, large language models (LLMs), and generative AI. These systems primarily function as assistive tools, as they generate text or predictions without directly interacting with clinical infrastructures. Therefore, recent research trends are increasingly oriented toward agentic AI systems that extend beyond traditional predictive and generative model performance. This manuscript provides a detailed review of the current state of agentic AI, starting with the evolution of AI and the concept of a medical agent. A medical agent refers to an intelligent AI system designed to assist in clinical or administrative tasks by analyzing data, supporting decision making, and interacting with healthcare environments. Its underlying agentic AI architecture integrates planning, memory, reasoning, and environmental interaction to enable autonomous tool use, multi-agent collaboration, and continuous perception decision action loops across diverse healthcare applications and clinical workflows. The review further examines safety mechanisms, including human-in-the-loop oversight, self-verification strategies, and regulatory alignment frameworks, which are designed to ensure reliability, accountability, compliance, and safe deployment in regulated healthcare environments. Our findings indicate that a large number of AI agents have been introduced in various manuscripts for healthcare applications; however, fully autonomous systems remain challenging to achieve, as AI still faces several limitations related to reliability, interpretability, data dependency, and integration within complex clinical workflows. In response to these challenges, this survey shifts the focus from task-specific model performance to system-level autonomy and workflow orchestration, providing a structured foundation for understanding the design, deployment, governance, and limitations of agentic AI systems in modern healthcare ecosystems.
A scoping review with systematic evidence mapping across five electronic sources, screened 1,649 exportable records, and provisionally included 557 unique studies that met predefined criteria for goal-directed task execution, tool use, interaction with external resources, feedback-based refinement, or multi-agent collaboration.
Zheng Tong, Yang Liu, Wan-Shu Fan et al.· arXiv.org· 0 citations
Background Cardiovascular medicine faces persistent implementation gaps driven by workforce shortages, fragmented data systems, and the cognitive burden of complex clinical decision-making—structural constraints that limit the delivery of guideline-directed care. Artificial intelligence (AI) is transitioning from isolated predictive models toward autonomous agent systems capable of perceiving, reasoning, and acting in clinical environments, offering a potential pathway to address these challenges. Objective This review synthesizes current evidence on AI agent systems in cardiovascular medicine across four interconnected dimensions: technical paradigms enabling agentic functionality (multi-agent systems, digital twins, multimodal integration), emerging clinical applications across the cardiovascular continuum, and governance frameworks essential for responsible translation. Methods We conducted a narrative review of peer-reviewed literature published between January 2020 and March 2026, drawing from PubMed, Web of Science, IEEE Xplore, and Scopus databases. Search terms included combinations of “artificial intelligence”, “AI agents”, “autonomous agents”, “multi-agent systems”, “large language models”, “cardiovascular diseases”, “heart failure”, “digital twins”, and “clinical decision support”. Emphasis was placed on high-quality original research, systematic reviews, and position papers from major cardiovascular societies, with particular attention to developments from 2024 to 2026. Results Agentic AI systems should function as augmented intelligence—enhancing rather than replacing clinical judgment—to close implementation gaps in cardiovascular care. Multi-agent architectures, digital cardiovascular twins, and multimodal integration are emerging as core technical paradigms. The ClinNoteAgents system demonstrates high extraction fidelity (conditional accuracy ≥90%) for clinical variables while achieving 60%–90% text reduction. Digital twin applications span therapy planning, risk prediction, and monitoring, with 69% relying on mechanistic models and 76% utilizing imaging data for personalization. Heart failure has emerged as a paradigmatic use case, driven by structural workforce gaps and the ARPA-H ADVOCATE initiative launched in January 2026. The C.A.R.D.I.O. framework (Clinical validation, Auditability, Risk stratification, Data privacy, Integration, Ongoing vigilance) and the CURACO framework (Clinical safety, Understanding, Research-informed care, Authentic patient-centred approaches, Conscientious ethics, Optimised technology) provide governance structures for responsible deployment. A rapid systematic review of 13 studies including 22,641 participants found that 85% of AI interventions improved cardiovascular outcomes, with mortality reductions of 0.8%–12% and major adverse cardiovascular event reductions of 4%–12%. Conclusion Cardiovascular medicine stands at an inflection point. The transition from assistance to autonomy requires rigorous fit-for-purpose evaluation, transparent interpretability mechanisms, and robust governance frameworks. Agentic AI systems should function as augmented intelligence—enhancing rather than replacing clinical judgment—to close implementation gaps in cardiovascular care.
Ye Chen, Xiaoqun Qin, Shouping Chen· Frontiers in Cardiovascular...· 0 citations
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.
Raşit Dinç, N. Ardic· JMIR Medical Informatics· 1 citation
DoctorAgents is proposed, an agentic AI framework that autonomously constructs and optimizes end-to-end ML pipelines through specialized large language model (LLM) agents for generation, validation, and refinement.
Ruilin Wang, Bo-Hong Wang, Elizabeth Kourbatski et al.· 1 citation
Medicine is a predominantly physical profession, yet most medical artificial intelligence (AI) remains screen bound. Physical artificial intelligence (PAI) extends AI's capabilities and can directly address many of the healthcare system challenges and unmet needs such as workforce shortages, unsustainable healthcare costs, heightened expectations, aging population and need for pandemic preparedness. PAI relies on systems that autonomously perceive, decide, and actuate in real-time by synthesizing diverse environmental data from multiple sensory sources. These data undergo rapid processing and interpretation, facilitating immediate decision-making and responsive physical actions, including movement, object manipulation, and direct human interaction. This paper first identifies critical healthcare system needs that robotic agents can effectively address. It further examines core actions of PAI systems, emphasizing perceptual pathways, clinical decision-making processes, and human-robot interactions that translate sensory inputs into tailored, patient-specific responses. We explore essential data utilization aspects, including edge-device advances, PAI datasets, digital twins, and edge-to-cloud infrastructures that support real-time inference and are crucial for reducing barriers to PAI implementation. Finally, we analyze the cultural factors accelerating the adoption of PAI in healthcare and review PAI advancements in other sectors. We argue that the convergence of pressing healthcare demands, technological advancements, and cultural readiness signals a tipping point for PAI in medicine.
Yonatan Prat, R. Francos, M. Shoham et al.· Frontiers in Digital Health· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
