Regulatory
AI-based devices should follow an ‘ethics by design’ approach to assure people that they are safe to use.
According to Baird, many existing processes could be adapted and re-used to safeguard data – and product – quality. “Everyone knows the saying ‘garbage in, garbage out’ and the quality of the data used to train and test AI/ML systems has a direct impact on product performance,” he says, adding that it should be possible to look at the key success factors in supplier quality and see how the principles behind them could be applied to data quality. Quality is crucial to building patient and carer confidence – and boosting uptake. “If AI works, most people will likely want to use it,” says Gerke. “It will therefore be important that AI-based devices are responsibly developed with an ‘ethics by design’ approach and that they are safe and effective to use, improve patient outcomes and fit into hospital workflows.” Baird believes ML systems will need a “proactive” approach when it comes to monitoring product performance over time, and that there could be lessons to be learned from other sectors. “We need to know if performance suddenly changes. In cybersecurity, there is a very proactive approach to monitoring products for new threats, perhaps we can use their general approach to things to help in the development of ML post-market monitoring.” Another element of introducing AI/ML-based devices that depends on quality and reliability is patient buy-in. Patients have historically been amenable to adopting everything from artificial implants to pumps that manage insulin flow – providing the technology is proven and safe. “The amount of buy-in needed [from patients] will vary by the risk and benefit of the application,” says Baird. “For low-risk AI, the amount of trust needed is much lower than for higher risk applications. For some of the low-risk items, I think we are already there – patients might not even realise there is AI in [certain] applications.” He also notes the crucial role of the healthcare professional in securing patient buy-in. “Many patients will trust their physician – if the physician believes in the product, then the patient will
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too. Some patients will, of course, have their own opinions and building confidence with them will also be important to the long-range success of this technology.” One of the key challenges to securing patient buy- in will be public messaging and education. There is a danger people will read about AI failures and generalise that to all applications. “For example, a crash involving an autonomous vehicle will affect the trust in AI across all applications,” says Baird. “People won’t care that the problem was a software issue for a certain model car running on a certain revision of the software; people will think ‘we can’t trust any of this technology in any application’.”
Legal liability
In terms of other challenges, Baird says work needs to be done on the legal side. “People have questions about legal liability – if the user decides to take action due to what the AI is indicating, and if it ends up being wrong, who is liable? Is it the application’s fault? Or the user for not knowing better?” Gerke, who served as Research Fellow on Harvard Law School’s Project on Precision Medicine, Artificial Intelligence and the Law, also picks up on this point. “To promote the implementation of AI in healthcare, it will be crucial to properly balance liability risks among stakeholders,” she says. “Insurance can play an important role in this regard.” For Baird, the potential of the technology far outweighs any challenges – be they regulatory or otherwise. He cites the response from a nurse after he asked what had changed in the sector over the last decade. “Her reply was that she was spending too much time in administrative tasks at her workstation rather than actually taking care of patients,” he says. “She had become a caregiver to take care of people, yet she was spending all her time taking care of machines.” This, he explains, is where AI-driven devices will come into their own. “The ML system can take care of administrative tasks [and] can help improve efficiencies – the machine can be good at doing what machines do, freeing up time for caregivers to give care.” ●
Medical Device Developments /
www.nsmedicaldevices.com
LeoWolfert/
Shutterstock.com
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