FDA’s Struggling AI Tool Raises Concerns for Medical Device Approval

As artificial intelligence (AI) continues to influence industries from transportation to finance, healthcare regulators have also begun integrating AI into their workflows. One prominent example is the U.S. Food and Drug Administration’s (FDA) attempt to implement an AI-based tool to streamline the review of medical device applications. However, recent reports suggest the tool is underperforming—even struggling with basic tasks—sparking concerns about delays, inefficiencies, and broader implications for medical innovation.

The Promise of AI in Medical Device Review

In theory, AI can revolutionize the way government agencies manage complex tasks. The FDA’s AI tool was introduced with the aim of expediting the review process for the vast number of applications submitted for medical devices—ranging from imaging machines to diagnostic tools. By automating repetitive tasks and flagging potential issues, the agency hoped to cut down on review times, increase approval transparency, and better allocate human resources.

The challenge is enormous: according to the FDA, there are thousands of medical devices submitted for review each year, many of which require rigorous evaluation under tight timelines. The AI system, introduced in late 2023, was designed to scan and classify these applications, prioritize urgent cases, and assist human reviewers in identifying missing information or inconsistencies.

Where the AI System Falls Short

Despite the initial promise, internal testing and whistleblower reports have revealed the system struggles with basic recognition tasks. According to NBC News, FDA staff describe the AI as unable to distinguish between medical device types, often misclassifying applications and creating confusion rather than clarity. In one documented instance, a device intended for diabetes monitoring was grouped under orthopedic implants.

Such errors are more than just technical hiccups—they can lead to misprioritized reviews, unnecessary delays, and, in worst-case scenarios, unsafe approvals. A review delay can cost device manufacturers millions of dollars, while hospitals and clinics face longer waits for new technology that could improve patient care.

Beyond misclassification, the tool reportedly lacks the ability to process unstructured text, which makes up a significant portion of regulatory submissions. Instead of assisting reviewers, the tool often flags innocuous content as problematic, further burdening staff who must double-check AI assessments.

Human-AI Collaboration vs. Replacement

The FDA has emphasized that the AI tool was never meant to replace human judgment but rather to support it. Still, the reliance on flawed outputs raises a fundamental concern: if reviewers begin to defer to AI suggestions out of habit or under pressure, critical mistakes might slip through the cracks.

This is especially risky in high-impact areas like Class III medical devices—those that sustain or support life, such as pacemakers and infusion pumps. In these cases, any misstep in the approval process can have direct consequences on patient safety and public trust.

According to an analysis from MedTech Dive, the FDA has already had to pull back certain functionalities of the tool after internal audits found inconsistencies in how recommendations were generated. The report also notes a growing frustration among FDA reviewers, who feel the tool adds to their workload rather than easing it.

Impact on Medical Device Manufacturers

The ripple effects of the AI tool’s shortcomings extend to medical device companies. A slower, more error-prone review process increases time to market, which can delay revenue and investment opportunities. For smaller companies and startups, this could mean the difference between survival and bankruptcy.

Additionally, some companies report being asked to resubmit documentation multiple times due to misreadings or errors triggered by the AI’s automated checks. This not only frustrates applicants but also clogs the FDA’s already congested workflow.

One mid-size device manufacturer, speaking anonymously to NBC News, noted that their application for a non-invasive monitoring device has been delayed for over six months due to AI-generated flags that human reviewers eventually deemed irrelevant.

Regulatory Transparency and Accountability

One of the core concerns raised by watchdog groups and healthcare advocates is the lack of transparency surrounding the AI’s implementation. The FDA has provided minimal public documentation about how the tool works, what data it was trained on, or how it weighs risk factors in its decision-making process.

This black-box nature of the AI undermines trust in the regulatory process. When lives and public health are at stake, opaque algorithms are not just problematic—they can be dangerous. Experts are calling for the FDA to release a full audit of the tool’s development and a detailed roadmap for improvements.

In the broader AI community, this situation is viewed as a cautionary tale about over-relying on underdeveloped technology in critical sectors. According to a recent Brookings Institution report, public agencies deploying AI tools must establish clear accountability frameworks, fail-safes, and user feedback loops to prevent operational breakdowns.

What Healthcare Providers Should Know

For hospitals, clinics, and practitioners, the FDA’s AI troubles are not just regulatory news—they have real-world consequences. Extended approval delays can limit access to cutting-edge diagnostic tools or therapeutic devices, potentially compromising care delivery. Providers should:

• Stay Informed: Follow FDA updates and industry news to understand which devices are caught in approval backlogs.
• Explore Alternatives: Consider refurbished or previously cleared models while awaiting newer technologies.
• Engage with Vendors: Ask manufacturers about expected timelines and whether their applications have been affected by regulatory delays.
• Advocate: Join coalitions or professional associations pressing for more transparency and oversight in AI-driven reviews.

Opportunities in Refurbished Equipment

As approval lags grow longer, more healthcare organizations are turning to refurbished equipment as a reliable stopgap. These devices are restored to original specifications and often available without the regulatory hold-ups new devices face. With proper certification, refurbished equipment can provide the same functionality and safety as new products, while also reducing capital expenditure.

In this environment of uncertainty, refurbished equipment is increasingly being viewed not as a fallback option but as a strategic resource. It enables facilities to maintain high standards of care without becoming hostage to regulatory gridlock or rising device costs.

The Road Ahead

The FDA’s AI review tool highlights both the promise and peril of integrating artificial intelligence into public sector workflows. While the idea of faster, smarter, automated reviews is compelling, the current reality demonstrates how quickly things can go wrong without robust design, oversight, and transparency.

For healthcare providers, the takeaway is clear: stay flexible, seek alternatives like refurbished equipment, and remain engaged with regulatory developments. The success or failure of AI tools in agencies like the FDA will shape how quickly and safely innovations reach the patients who need them most.

Conclusion

The ongoing issues with the FDA’s AI tool underscore the challenges of modernizing complex regulatory systems. As device manufacturers face longer approval timelines and healthcare providers struggle with delayed access to innovations, the role of refurbished equipment becomes even more critical. Until AI tools are proven to be accurate, transparent, and helpful, relying on established and certified solutions remains the smartest move for forward-thinking medical institutions.

To explore high-quality refurbished medical equipment that can help your facility stay ahead despite regulatory uncertainty, visit www.med.equipment.