Legal and Regulatory Framework for AI Solutions in Healthcare in EU, US, China, and Russia: New Scenarios after a Pandemic

Abstract

The COVID-19 crisis has exposed some of the most pressing challenges affecting healthcare and highlighted the benefits that robust integration of digital and AI technologies in the healthcare setting may bring. Although medical solutions based on AI are growing rapidly, regulatory issues and policy initiatives including ownership and control of data, data sharing, privacy protection, telemedicine, and accountability need to be carefully and continually addressed as AI research requires robust and ethical guidelines, demanding an update of the legal and regulatory framework all over the world. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. A global effort is needed for an open, mature conversation about the best possible way to guard against and mitigate possible harms to realize the potential of AI across health systems in a respectful and ethical way. This conversation must include national and international policymakers, physicians, digital health and machine learning leaders from industry and academia. If this is done properly and in a timely fashion, the potential of AI in healthcare will be realized.

Women’s perceptions and attitudes to the use of AI in breast cancer screening: a survey in a cancer referral centre

“…22 The pending clinical introduction of AI and the unanswered accountability questions underline the urgent need for policymaker to develop legal frameworks for the use of AI in screening mammography. 30,31 Although the percentage of variance explained by mca1 is quite low (33.5%), we were able to identify possible groups of patients with different knowledge and perception of AI. The group of patients who mostly considered AI in a positive way was made by young females (40-49 years) with high school educational level at their first screening.…”

The Good, the Bad, and the Invisible with Its Opportunity Costs: Introduction to the ‘J’ Special Issue on “the Impact of Artificial Intelligence on Law”

“…Therefore, we can expect not only different national strategies, if any, concerning the 'major threat' of the underuse of AI, but such strategies will necessarily be related to the specificities of each field, that is, the underuse of AI in the health sector, in environmental law, for agriculture, in public transport systems, etc. [43][44][45][46].…”

Mammography in 2022, from Computer-Aided Detection to Artificial Intelligence Applications

“…The ownership of data, particularly the personal data concerning health information, is another part of the discussion on the application of different ownership rules to original, de-identified, anonymized, and processed data. However, the current anonymization or de-identification techniques are still substandard and there are no available certifications for tools and methods for anonymization, as far as we know [41].…”

“…In a recent paper, Chen et al [42] explain that as payment systems in healthcare progressively evolve towards more mature value-based payment models where measuring improvement in quality becomes in-creasingly important at decreased costs, AI is also likely to become a valuable and indispensable tool for radiologists and healthcare systems. In many European countries, the reimbursement system in healthcare it tends to make it more economically advantageous to provide a greater number of medical exams/procedure-whatever the quality of the same-rather than delivering fewer exams/procedures of guaranteed high quality [41][42][43][44][45]. Accordingly, a shift towards value-based healthcare systems and quality-based reimbursement, might increase the perceived importance of quality and thus the value of using AI solutions.…”

“…For instance, the human professional may trust the AI device over his own judgment. This is a delicate and difficult scenario and any clinicians should be left to face such choices alone; it is essential that both healthcare providers and associations of clinicians take an active step towards their employees and members, respectively, by proposing the appropriate instruments including protocols, guidelines, and training programs, that can support clinicians to truly understand of the AI algorithms they are using [41].…”