Agentic AI as Embedded Decision Makers in Diagnostic Devices

Agentic artificial intelligence (AI) is the future of medical diagnostic devices.

Today, hundreds of AI diagnostic tools cleared by the U.S. Food and Drug Administration (FDA) are already embedded in radiology, cardiology, stroke detection, ultrasound imaging, and mammography. These systems primarily assist clinicians by accelerating interpretation, improving detection accuracy, and flagging potential abnormalities.

However, their role remains largely advisory. They support decision-making but do not act on it. 

As these systems evolve, agentic AI will eventually execute multi-step clinical tasks with minimal human oversight, such as ordering follow-up tests and lab work, routing cases to specialists, coordinating imaging, and initiating care within defined clinical boundaries.

This shift represents a fundamental transition in medtech from interpretation to execution. Nature.com recently published an article¹ that indicates current healthcare autonomous AI agents have demonstrated “high accuracy in cancer diagnosis, treatment planning, alert generation, coaching, and workflow optimization.” 

However, more in-depth studies are needed. 

Core Benefits and Operational Pain Points

The power of AI and, ultimately, agentic AI, is its ability to resolve the data paradox—more diagnostic data than ever before. Traditional workflows are slowed by procedural inefficiencies and human processing limits, making AI and autonomous technology an essential solution.

The strategic benefits will include agentic devices that: 

• Eliminate care bottlenecks. The vast volume of data, such as imaging, physiological signals, and lab results can lead to cognitive overload and longer diagnostic timelines.
• Make time-sensitive clinical decisions within the defined clinical boundaries. These decisions can accelerate case prioritization, initiate alerts, and create diagnostic reports without manual interventions. 
• Close the diagnostic loop by integrating multimodal inputs, including imaging, labs, and vitals. Over time, this would reduce redundant queries and fragmented workflows by accelerating the shift from detection to action. 

Operational Issues

Despite these advantages, agentic diagnostics adoption faces a significant trust gap, requiring healthcare organizations to continually audit autonomous decisions in regulated, transparent environments. They must review outcomes and the reasoning behind each action taken by the system. 

For the foreseeable future, there will always be a “human-in-the-loop” overseeing the system outputs because agentic AI is still an emerging technology and professional liability issues and medical standards will require it. Combining human insight with AI should result in better patient outcomes.

Apart from this, hardware constraints complicate the installation, as high-reasoning models must be embedded into compact diagnostic devices. While traditional AI systems already require optimization, agentic AI introduces greater demands on memory, power consumption, and real-time performance due to its multi-step reasoning capabilities.

The Cross-Functional Strike Team

These technical and organizational challenges are significant. Many organizations find the barrier to adopting AI primarily comes from its team members. Healthcare staff and clinicians may view autonomous systems as black boxes or worry about losing expert judgment or jobs. 

For the successful adoption of agentic AI, organizations must go beyond IT-centric implementation. They must establish cross-functional teams at the early stages of the project. This approach helps recognize real impacts on clinical workflows, compliance standards, workforce dynamics, and builds understanding of how AI technology can help them. 

Movement Toward Agentic AI Acceptance

With any new technology, there are early adopters and laggards. In the healthcare industry, there are valid reasons for caution. The systems and devices must prove they are as accurate or more accurate than humans, and it appears healthcare is gradually moving toward acceptance. 

In a Deloitte report,² an agentic AI leader from a technology company said the advancements enable “faster and more accurate diagnoses” and allow healthcare professionals “to focus on complex decision-making, elevating the quality of care.” 

Deloitte’s 2026 U.S. Health Care Outlook³ reported that 83% of healthcare executives said gen AI and agentic AI will add value to “diagnostic imaging, pathology, and clinical decision support,” demonstrating growing acceptance that can directly influence patient care decisions. Grand View Research estimates⁴ that the agentic AI healthcare market will reach almost $5 billion by 2030, a massive increase from $538.51 million in 2024.

Strategic Execution: Build, Buy, or Hybrid?

As modern agentic AI capabilities mature, healthcare groups will have to decide how to adopt the new technology, which usually is buy, build, or a hybrid approach: 

• Build: Most organizations often don’t have highly skilled software developers to build an agentic AI system or device, which means they would have to find a trusted AI solution provider with experience in the technology and healthcare sector. This approach usually has higher upfront costs and longer rollout time, but it includes customization to meet their specific needs and lower long-term costs.
• Buy: Buying a device or platform may have lower upfront costs and faster deployment, but it may have recurring subscription fees and lack needed customization.
• Hybrid: Many organizations opt for the hybrid strategy, which combines the benefits of buying third-party devices with customization by their software solution provider.

Regulatory Guardrails & Future Outlook

The success of agentic healthcare will depend on governed autonomy, where intelligent systems operate within defined clinical and regulatory guardrails, complemented by human oversight. The next generation of medical devices will not just generate insights but also deliver accurate decisions.

Based on the capabilities of agentic AI, the next shift in medical diagnostics will be more outcome-driven. As agentic AI proves its accuracy and helpfulness to healthcare professionals, its adoption will continue an upward trend with better patient outcomes defining its success.

References

1. go.nature.com/4mLlFLs
2. bit.ly/mposoftware05261
3. bit.ly/mposoftware05262
4. bit.ly/mposoftware05263

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Anshu Raj is a director of operations at Chetu, where he oversees the AI and Engineering R&D portfolios, including high-tech medical devices. Raj, who holds certifications in PMP, Agile, and NetSuite Foundation, focuses on mid-market companies that want to accelerate innovation through on-demand engineering teams.