______________________________________________________________________________________________ AI in Diagnostics
capacity-limited settings. By letting patients test themselves in familiar environments with minimal setup, these tools cut down barriers like travel, lost work hours, and long waits for specialists and diagnostic tests,” the Professor notes.
She points out the benefit isn’ t limited to one group.“ It matters especially for patients whose symptoms don’ t immediately lead to specialist referral, like women and younger adults. Over time, these tools could offer similar benefits in pediatric care as evidence and regulations catch up. The benefit really extends across the whole population.”
She makes it clear that the approach doesn’ t sideline specialists in care delivery.“ At scale, these solutions expand diagnostic capacity without overwhelming specialist services. Specialist expertise still matters for complex cases and treatment planning, but it’ s not needed for every diagnostic step,” the Professor observes. She also notes policy barriers.“ This model won’ t work if reimbursement structures stay misaligned,” she warns.
Finally, she stresses the importance of clinical safeguards.“ We need guardrails. Not all home diagnostic devices are clinically fit for purpose, and a modern diagnostic framework shouldn’ t accept stated intended use as enough,” the Professor notes.
She cautions against uneven performance.“ Devices with low predictive value, or those based on physiological measures that don’ t work well across all populations, could actually make underdiagnosis worse,” she explains.
She closes with a call for evidence-based policy.“ Reimbursement models should draw a clear line between technologies that meet robust clinical standards- statistically equal to cardiorespiratory polygraphy- and those that don’ t. We shouldn’ t support widespread use of less accurate tools,” the Professor concludes. ■
Esther Rodriguez Villegas www. acurable. com
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