The healthcare AI landscape is increasingly shifting from systems that simply analyse information or generate responses towards agentic AI capable of planning, executing and coordinating complex workflows. Rather than acting as passive assistants, AI agents are beginning to take on defined tasks across clinical care, medical device operations, drug development and healthcare administration.
A leading example is Faro AI, which recently raised $37.3 million in Series B funding to expand its agentic AI capabilities and infrastructure for clinical development. The company plans to work with pharmaceutical and biotechnology companies to deploy AI agents throughout the drug development process, from first-in-human studies through to regulatory approval. The ambition is not simply to provide researchers with another AI tool, but to automate parts of the underlying clinical development workflow.
This represents an important evolution in the role of AI in healthcare. Traditional healthcare AI has largely focused on analysing data, identifying patterns, generating summaries or supporting individual decisions. Agentic AI goes a step further by combining these capabilities with action-oriented workflows. An AI agent can potentially interpret information, determine the next appropriate step, interact with digital systems and execute predefined tasks with limited human intervention.
The same development is visible across other areas of healthcare. Hike Medical, which recently raised $22.5 million, uses AI agents to process referrals and insurance approvals for orthotics and prosthetics providers. Its technology connects administrative processes, clinical workflows and device manufacturing, illustrating how agentic AI can operate across multiple stages of a healthcare value chain rather than within a single software application.
Meanwhile, Metriport raised $26 million to expand its healthcare data infrastructure and its AI and agentic capabilities. Its platform integrates fragmented patient records and transforms them into structured, accessible data, providing an important foundation for AI agents to operate effectively. Without reliable and interoperable data, autonomous AI workflows remain limited.
This highlights a broader trend: agentic AI is becoming increasingly dependent on the infrastructure surrounding it. Data interoperability, electronic health records, clinical validation, regulatory compliance and secure system integration are becoming just as important as the underlying AI models.
The potential impact is significant. In drug development, AI agents could reduce the administrative workload associated with clinical trials and regulatory processes. In clinical care, they could coordinate information and follow-up actions across different systems. In medical device care, they could automate referrals, insurance processes and production workflows. Across these applications, the objective is increasingly to move from “AI that tells you what to do” to “AI that can help get it done.”
However, the expansion of agentic AI also introduces new challenges. Healthcare decisions require high levels of accuracy, transparency and accountability, particularly when AI systems begin taking actions rather than merely generating recommendations. Clear boundaries between autonomous execution and human oversight will therefore become essential.
The growing investment in companies such as Faro AI, Hike Medical and Metriport suggests that agentic AI is moving beyond experimentation and into practical healthcare workflows. The next phase of healthcare AI may therefore be defined not by increasingly capable chatbots, but by AI agents that can coordinate processes, interact with healthcare infrastructure and execute real-world tasks across the care continuum.
source: mobihealthnews.com
