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Sharing Biomedical Data: Strengthening AI Development in Healthcare

INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
Department of Physiological Nursing, School of Nursing, University of California, San Francisco, CA 94143, USA
CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal
FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
i3S—Institute for Research and Innovation in Health of the University of Porto, 4200-135 Porto, Portugal
IPATIMUP—Institute of Molecular Pathology and Immunology of the University of Porto, 4200-135 Porto, Portugal
UTAD—University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal
Author to whom correspondence should be addressed.
Academic Editors: Marco P. Soares dos Santos and Daniele Giansanti
Healthcare 2021, 9(7), 827;
Received: 3 May 2021 / Revised: 11 June 2021 / Accepted: 22 June 2021 / Published: 30 June 2021
(This article belongs to the Special Issue Digital Transformation in Healthcare)
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. View Full-Text
Keywords: biomedical data; medical imaging; shared data; massive databases; AI-based healthcare solutions biomedical data; medical imaging; shared data; massive databases; AI-based healthcare solutions
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Figure 1

MDPI and ACS Style

Pereira, T.; Morgado, J.; Silva, F.; Pelter, M.M.; Dias, V.R.; Barros, R.; Freitas, C.; Negrão, E.; Flor de Lima, B.; Correia da Silva, M.; Madureira, A.J.; Ramos, I.; Hespanhol, V.; Costa, J.L.; Cunha, A.; Oliveira, H.P. Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare 2021, 9, 827.

AMA Style

Pereira T, Morgado J, Silva F, Pelter MM, Dias VR, Barros R, Freitas C, Negrão E, Flor de Lima B, Correia da Silva M, Madureira AJ, Ramos I, Hespanhol V, Costa JL, Cunha A, Oliveira HP. Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare. 2021; 9(7):827.

Chicago/Turabian Style

Pereira, Tania, Joana Morgado, Francisco Silva, Michele M. Pelter, Vasco R. Dias, Rita Barros, Cláudia Freitas, Eduardo Negrão, Beatriz Flor de Lima, Miguel Correia da Silva, António J. Madureira, Isabel Ramos, Venceslau Hespanhol, José L. Costa, António Cunha, and Hélder P. Oliveira. 2021. "Sharing Biomedical Data: Strengthening AI Development in Healthcare" Healthcare 9, no. 7: 827.

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