| Slow adoption
Across healthcare, AI-enabled diagnostic solutions are flooding the market. From cardiology and imaging to oncology and primary care, clinicians are being asked to evaluate algorithms that promise earlier detection, better accuracy, and more efficient care. Many of these tools are technically impressive. Some are FDA-cleared. A few even come with early clinical data. Yet only a relatively small number have become part of routine clinical practice. What's the hold up?
Despite the volume of innovation, many AI-enabled diagnostics struggle to move beyond limited pilots, highlighting the importance of clinical validation, workflow fit, and effective execution, meaning how a tool is implemented, supported, governed, and sustained in everyday clinical operations. In practice, the same issues come up again and again: clinical validation, ease of use, workflow integration, and system-level value. Trust is key
What matters just as much is whether AI is used deliberately to improve care, not simply to reduce risk. Tools that earn trust demonstrate real clinical value, fit naturally into existing workflows, and support better decisions over time. Skepticism toward AI is understandable, particularly as the field continues to mature and distinguish tools that withstand rigorous clinical testing from those driven primarily by novelty. If AI is going to earn a lasting role in diagnostics, it must be held to the same standards as any serious medical advance: demonstrate real-world clinical performance at scale, ideally supported by randomized clinical trials and longitudinal evidence tied to meaningful patient outcomes. AI that succeeds will be grounded in strong clinical validation, introduced through workflows clinicians already use, supported by fit-for-purpose oversight structures and implementation, and ultimately shown to improve patient outcomes. — By MedCity Influencer Simos Kedikoglou | 
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