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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Investments in AI and ML help PV teams transform safety case processing

[NicoElNino/Adobe Stock]

Thanks to automation in pharmacovigilance, the next generation of safety is here — and with it, there is an immense opportunity for firms that can change how they work and maximize the opportunity automation creates.

Leading safety teams are seeing up to 80% efficiency gains on key workflows by investing in touchless case processing, automating pharmacovigilance processes across intake and assessment, review, full data entry, medical review, quality review and submission.

A new opportunity in AI and ML

The life sciences industry is rapidly growing in scale and complexity, with organizations generating increasingly large volumes of safety data. The pharmaceutical industry, healthcare providers and consumers have reported more than 18.6 million adverse events to the U.S. Food and Drug Administration (FDA) in the last 10 years, 216% more than the prior 10 years.

Safety case da…

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Why AI alone won’t resolve drug discovery challenges

A digital representation of a protein structure. [Image courtesy of NIH]

Big Pharma and researchers are sharpening their focus on AI to speed drug discovery. But the path to fully AI-driven drug discovery faces substantial hurdles, according to Adityo Prakash, CEO of Verseon. “When it comes to drug discovery, AI has a data problem with which the pharmaceutical industry has not yet come to terms,” he said.

Prakash explained there is simply “not enough data” to rely on AI as the primary means of small molecule drug discovery. He discussed these challenges further in an article in American Pharmaceutical Review titled “Exploring New Chemical Space for the Treatments of Tomorrow.”

The limitations and challenges of AI-first methods

Even with high-throughput screening to automate the process of testing pre-synthesized drug candidates against disease-associated target proteins, the pharma…

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An intelligent approach to data cleaning 

Image courtesy of Pexels

The collection of good quality data from clinical trials is essential to data analysis to produce robust results that meet the precise requirements for regulatory need. Data from clinical trials are increasingly complex, related to involved protocols, the geography of trial sites, increasing data streams and technological advances. Therefore, studies must be set up to be efficient, offer support and training to trial sites and ensure that the right data are collected correctly.

Data Management teams have historically reviewed data once source document verification (SDV) has taken place by the Clinical Monitors on an ongoing basis. Data issues can be actioned early in the trial, and corrective action put in place. This activity has been a very manual process, thorough and time-consuming and has left less time to focus on insightful data analysis.

Collaboration: Data science and da…
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