OBJECTIVE: This survey aims to systematically map the rapidly evolving landscape of AI agents in healthcare. It addresses the critical need to adapt general-purpose agentic frameworks characterized by autonomy, planning, and tool use to the high-stakes, safety-critical constraints of medical decision-making and patient care. METHODS: We conducted a comprehensive review of over 200 recent studies, synthesizing literature from major academic databases. We developed a holistic taxonomy that traces the full lifecycle of healthcare agents, analyzing perception modalities, core technical architectures, and evaluation protocols specific to autonomous systems. RESULTS: The review presents a quantitative landscape analysis showing exponential growth in the field. We structure the domain into three pillars: (1) Perception of multi-modal clinical data (e.g., EHR, imaging, genomics); (2) Agent Capabilities, including tool use, reasoning, memory, and multi-agent collaboration; and (3) an Application Ecosystem organized by stakeholder roles (clinicians, patients, researchers, and administrators). Additionally, we categorize evaluation frameworks, and discuss the deployment readiness of current systems across technical, evidentiary, and governance dimensions. Finally, we identify challenges for advancing healthcare agents from controlled evaluation toward real-world clinical integration. A continuously updated repository of related papers is available at https://github.com/AgenticHealthAI/Awesome-AI-Agents-for-Healthcare. CONCLUSION: AI agents offer significant potential to enhance healthcare through autonomous reasoning and workflow integration. However, current research remains largely concentrated in benchmark and controlled evaluation settings, and the translation into clinical practice will require advances in reliability, privacy protection, governance, and operational integration.

Recent efforts in medical image computing have focused on improving fairness by balancing it with accuracy within a single, unified model. However, this often creates a trade-off: gains for underrepresented groups can come at the expense of reduced accuracy for groups that were previously well-served. In high-stakes clinical contexts, even minor drops in accuracy can lead to serious consequences, making such trade-offs highly contentious. Rather than accepting this compromise, we reframe the fairness objective in this paper as maximizing diagnostic accuracy for each patient group by leveraging additional computational resources to train group-specific models. To achieve this goal, we introduce SPARE, a novel data reweighting algorithm designed to optimize performance for a given group. SPARE evaluates the value of each training sample using two key factors: utility, which reflects the sample's contribution to refining the model's decision boundary, and group similarity, which captures its relevance to the target group. By assigning greater weight to samples that score highly on both metrics, SPARE rebalances the training process-particularly leveraging the value of out-of-group data-to improve group-specific accuracy while avoiding the traditional fairness-accuracy trade-off. Experiments on two skin disease datasets demonstrate that SPARE significantly improves group-specific performance while maintaining comparable fairness metrics, highlighting its promise as a more practical fairness paradigm for improving clinical reliability.