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.
Highlights
The new frontiers of research made possible by artificial intelligence (AI) algorithms based on machine learning (ML) and deep learning (DL) give the technological possibility of using aggregated healthcare data to produce models that enable a true precision approach to medicine [3,4,5,6,7,8]
During the first months of 2020, chest computedtomography scans showed the extent of lung damage caused by COVID-19; efforts were established around the world to facilitate data sharing, model training, and scan assessment [10,15]
Push Doctor, a company in the United Kingdom (U.K.) that has partnered with the National Health System (NHS), claimed in March that usage of their product had increased by 70% [19]
Summary
In the last few years, governments have started to promote data sharing [36]. For instance, anonymized benchmarking datasets with annotated diagnoses have been created to provide reference standards [37,38]. Existing examples of data-sharing efforts include biobanks and international consortia for medical imaging databases, such as the Cancer Imaging Archive (TCIA) [39], the Visual Concept Extraction Challenge in Radiology Project [40], the Cardiac Atlas Project [41], the U.K. Biobank [42], and the Kaggle Data Science Bowl [25], the latter of which represents a valuable step in the direction of an openaccess database of anonymized medical images coupled with histology, clinical history, and genomic signatures. Biobank [42], and the Kaggle Data Science Bowl [25], the latter of which represents a valuable step in the direction of an openaccess database of anonymized medical images coupled with histology, clinical history, and genomic signatures Despite those hopeful examples, the amount of data sharing required for widespread adoption of AI technologies across different health systems demands still more efforts.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
