Sharing Biomedical Data: Strengthening AI Development in Healthcare
| Autor: | Miguel Correia da Silva, Joana Morgado, José Luis Costa, Rita Barros, Venceslau Hespanhol, Antonio José Ledo Alves da Cunha, Eduardo Negrão, Isabel Ramos, Cláudia Freitas, António J. Madureira, Hélder P. Oliveira, Michele M. Pelter, Tania Pereira, Francisco Silva, Beatriz Flor de Lima, Vasco Rosa Dias |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
020205 medical informatics
Leadership and Management Computer science medical imaging massive databases Health Informatics Bioengineering 02 engineering and technology Field (computer science) Domain (software engineering) 03 medical and health sciences 0302 clinical medicine Development (topology) Health Information Management Biomedical data Health care 0202 electrical engineering electronic engineering information engineering 030212 general & internal medicine biomedical data Data limitations business.industry Health Policy AI-based healthcare solutions Learning models Data science shared data Perspective Medicine Generic health relevance Transfer of learning business |
| Zdroj: | Healthcare Healthcare (Basel, Switzerland), vol 9, iss 7 Healthcare, Vol 9, Iss 827, p 827 (2021) |
| ISSN: | 2227-9032 |
| Popis: | Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data. |
| Databáze: | OpenAIRE |
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