9 tips for implementing AI in medical devices from a Medtronic executive

Patients and healthcare providers remain at the core of successful AI implementations in medtech. [piai/Adobe Stock]

It seems like artificial intelligence (AI) is ubiquitous in the healthcare landscape, but the technology remains nascent in the industry. Technologies ranging from machine learning to natural language processing and beyond promise to help make diagnoses and treatment more precise, efficient, and personalized.

But the allure of AI can sometimes overshadow the central goal of addressing tangible clinical problems.

During his talk at DeviceTalks West, Ha Hong, chief AI officer at Medtronic Endoscopy, underscored the importance of putting patients and healthcare providers at the forefront when incorporating AI into medical devices.

With a plethora of AI tools at our disposal — many of which are increasingly user-friendly — the onus is on us to wield them responsibly. Below, you’ll find …

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Natalizumab and PML: The complex dance of benefit and risk for MS

MRI scan of a PML patient displaying prominent brain lesions (indicated by white signal). [Image credit: Daniel S. Reich, National Institute of Neurological Disorders and Stroke (NINDS)]

Biogen’s Tysabri (natalizumab), the first humanized monoclonal antibody for multiple sclerosis (MS), sparked optimism among MS patients following its FDA approval in 2004. The drug offered significant benefits, reducing relapses for patients resistant to other treatments. This was a significant milestone in the treatment of MS, but the journey of natalizumab and PML soon took a concerning turn.

Within a year, alarming reports surfaced: A number of patients were developing progressive multifocal leukoencephalopathy (PML), a rare and often fatal brain infection. The suspected culprit? Natalizumab’s immunomodulatory effect, which suppressed the immune cells fighting the JC (John Cunningham) virus…

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