CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare

Abstract

Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks. However, recent efforts have focused on short-horizon or general-purpose applications (e.g., mobile or desktop interfaces), leaving long-horizon automation for domain-specific systems, particularly in healthcare, largely unexplored. To address this, we introduce CareFlow, a high-quality human-annotated benchmark comprising complex, long-horizon software workflows across medical annotation tools, DICOM viewers, EHR systems, and laboratory information systems. On this benchmark, existing vision-language models (VLMs) perform poorly, struggling with long-horizon reasoning and multi-step interactions in medical contexts. To overcome this, we propose CarePilot, a multi-agent framework based on the actor-critic paradigm. The Actor integrates tool grounding with dual-memory mechanisms (long-term and short-term experience) to predict the next semantic action from the visual interface and system state. The Critic evaluates each action, updates memory based on observed effects, and either executes or provides corrective feedback to refine the workflow. Through iterative agentic simulation, the Actor learns to perform more robust and reasoning-aware predictions during inference. Our experiments show that CarePilot achieves state-of-the-art performance, outperforming strong closed-source and open-source multimodal baselines by approximately 15.26% and 3.38%, respectively, on our benchmark and out-of-distribution dataset.

Figures (7)

• Figure 1: CareFlow is a large-scale benchmark for evaluating multimodal agents on a range of real healthcare software. It enables execution-based evaluation and interactive reasoning across DICOM viewers, image annotation tools, EMR/EHR, and LIS platforms. Each task pairs a natural-language goal with GUI screenshots representing authentic clinical workflows.
• Figure 2: Overview of the CarePilot framework. An Actor–Critic multi-agent architecture governs hierarchical decision-making for long-horizon healthcare workflows. At each step, the Actor observes the current interface and instruction, integrates tool-grounding signals, and its past experience that is stored in short- and long-term memories, and predicts the next semantic action. The Critic evaluates outcomes, provides corrective feedback, and updates both short-term and long-term memory buffers to guide subsequent decisions.
• Figure 3: Distribution of task lengths (number of steps) in the test split of CareFlow.
• Figure 4: Category distribution of tasks in CareFlow across four major healthcare software domains.
• Figure 5: Qualitative visualization of Llama-4 Maverick-17B performing CarePilot’s radiology workflow tasks. The traces highlight typical action–mode confusions such as issuing ZOOM instead of CLICK for tool selection and CLICK in place of SEGMENT or TEXT operations. These incomplete branches illustrate the model’s inconsistent tool arming and gesture execution.