It's no secret the medtech industry has embraced artificial intelligence (AI) and machine learning (ML). Pharmaceutical companies are leaping into the AI/ML space, too. There have been about 100 partnerships that have been established between pharmaceutical companies and AI vendors, according to a report from clinicaltrialsarena.com citing GlobalData Healthcare data. Earlier today (Wednesday), Merck announced its plan to dive deeper into the space. The pharma powerhouse said it was launching the Merck Digital Sciences Studio (MDSS), which will help early-stage biomedical startups with direct investment, access to powerful Azure Cloud computing, and opportunities to pilot their technologies in collaboration with discovery and clinical scientists at Merck.

Healthcare providers and their patients stand to benefit dramatically from AI technologies, thanks to their ability to leverage data at scale to reveal new insights. But for AI developers to perform the research that will feed the next wave of breakthroughs, they first need the right data and the tools to use it. Powerful new techniques are now available to extract and utilize data from complex objects like medical imaging, but leaders must know where to invest their organizations' resources to fuel this transformation. When a layperson envisions creating an AI model, most of what they picture is concentrated in step four: feeding data into the system and analyzing it to arrive at a breakthrough. But experienced data scientists know the reality is much more mundane--80% of their time is spent on "data wrangling" tasks (the comparatively dull work of steps one, two, and three)--while only 20% is spent on analysis. Many facets of the healthcare industry have yet to adjust to the data demands of AI, particularly when dealing with medical imaging.

Innovative medical devices have changed the healthcare landscape and will continue making dramatic improvements in patient care. Nevertheless, the growth of such devices will inevitably lead to increased litigation over their alleged failures. All companies developing healthcare tech therefore need to consider measures to protect themselves against potential claims. Any litigation that arises from medtech that uses AI – especially AI used as part of a diagnosis or intervention – is likely to be complicated. Medtech often involves a complex chain of actions involving a number of different parties, ranging from medical device manufacturers to programmers to physicians.

Jeff Shuren, director of the FDA's Center for Devices and Radiological Health, on Thursday called out the need for better methodologies for identification and improvement of algorithms prone to mirroring "systemic biases" in the healthcare system and the data used to train artificial intelligence and machine learning-based devices, speaking at an FDA public workshop on the topic. The medical device industry should develop a strategy to enroll racially and ethnically diverse populations in clinical trials. "It's essential that the data used to train [these] devices represent the intended patient population with regards to age, gender, sex, race and ethnicity," Shuren said. The virtual workshop comes nine months after the agency released an action plan for establishing a regulatory approach to AI/ML-based Software as a Medical Device (SaMD). Among the five actions laid out in the plan, FDA intends to foster a patient-centered approach that includes device transparency for users.

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There are several challenges that impede the deployment of AI in healthcare. These include a fragmented data landscape that makes access to or sharing of data difficult, a lack of interoperability, and a shortage of incentives for data sharing. To overcome these challenges, MedTech Europe recommends specific policy actions, also in reference to those drafted by the High-Level Expert Group on AI (HLEG).

Two pixilated vowels, together, represent the hopes and fears of a healthcare industry seeking more intelligent solutions. "AI," artificial intelligence, has been around since 1956 but has made precious few contributions to medical practice so far. Each year, hundreds of plucky new startups hop aboard the AI hype wagon, each promising sophisticated new solutions, from nurse-bots to virtual assistants to AI-powered wearables for the elderly, just to name a few. Most are titillating but not transformative. Nearly all have failed to move the needle on quality outcomes or life expectancy.

It shouldn't really be a surprise that artificial intelligence (AI) gets a special mention in the long-term plan for the NHS, published in March. AI is seen as important for the future of the NHS because it can make healthcare more effective and efficient, leaving staff free to focus on, as the plan puts it, the'complexity of human interactions that technology will never master'. With a growing population, limited resources yet more and more treatments available, the use of intelligent technology will be key to ensuring our healthcare services can keep pace. At Grow MedTech, we see AI as one of the most important digital technologies that will combine with traditional medtech to create the products and technologies of the future. And Yorkshire is a hotbed for the technology, with all of our partner universities offering expertise in the field.

When I joined MD DI nearly a year ago, I would hear smatterings about artificial intelligence and machine learning in medtech. By mid-February every other pitch I started receiving was AI-related. Conferences started delving into the AI topic. I even participated in a webinar hosted by Dave Saunders, CTO and Co-founder of Galen Robotics. It was clear to me that AI wasn't just a buzzword, or some fancy marketing scribble used to catch a journalist's attention.

Armed with a mouse and computer screen instead of a scalpel and operating theatre, cardiologist Benjamin Meder carefully places the electrodes of a pacemaker in a beating, digital heart. Using this "digital twin" that mimics the electrical and physical properties of the cells in patient 7497's heart, Meder runs simulations to see if the pacemaker can keep the congestive heart failure sufferer alive, before he has inserted a knife. The digital heart twin developed by Siemens Healthineers is one example of how medical device makers are using artificial intelligence (AI) to help doctors make more precise diagnoses as medicine enters an increasingly personalised age. The challenge for Siemens Healthineers and rivals such as Philips and GE Healthcare is to keep an edge over tech giants from Alphabet's Google to Alibaba that hope to use big data to grab a slice of healthcare spending. With healthcare budgets under increasing pressure, AI tools such as the digital heart twin could save tens of thousands of dollars by predicting outcomes and avoiding unnecessary surgery.