Application of Artificial Intelligence in Healthcare: The Need for More Interpretable Artificial Intelligence
DOI:
https://doi.org/10.20344/amp.20469Keywords:
Artificial Intelligence, Delivery of Health Care, Machine LearningAbstract
N/a.
Downloads
References
Matthew Helm J, Swiergosz MA, Haeberle HM, Karnuta JL, Schaffer JE, Krebs V, et al. Machine learning and artificial intelligence: definitions, applications, and future directions. Curr Rev Musculoskelet Med. 2020;13:69-76. DOI: https://doi.org/10.1007/s12178-020-09600-8
Rasheed K, Qayyum A, Ghaly, M, Al-Fuqaha A, Razi, A, Qadir J. Explainable, trustworthy, and ethical machine learning for healthcare: a survey. Comput Biol Med. 2022;149:106043. DOI: https://doi.org/10.1016/j.compbiomed.2022.106043
Müller AC, Guido S. Introduction to machine learning with Python. 4th ed. Paris: O’Reilly Editions; 2018.
Belle V, Papantonis I. Principles and practice of explainable machine learning. Front Big Data. 2021;4:688969. DOI: https://doi.org/10.3389/fdata.2021.688969
Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N. Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD Sydney: International Conference on Knowledge Discovery and Data Mining; 2015. DOI: https://doi.org/10.1145/2783258.2788613
European Comission: EUR-Lex 2021. Proposal for a regulation of the European Parliament and of the Council laying down harmonized rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union Legislative Acts. [cited 2023 Jul 10]. Available from: https://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=celex%3A52021PC0206.
European Medicines Agency. Reflection paper on the use of artificial intelligence in the lifecycle of medicines. [cited 2023 Jul 22]. Available from: https://www.ema.europa.eu/en/news/reflection-paper-use-artificial-intelligence-lifecycle-medicine.
Hunter DJ, Holmes C. Where medical statistics meets artificial intelligence. N Engl J Med. 2023;389:1211-9. DOI: https://doi.org/10.1056/NEJMra2212850
Ramamoorthy D, Severson K, Ghosh S, Sachs K, Als A, Glass JD, et al. Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data. Nat Comput Sci. 2022;2:605-16. DOI: https://doi.org/10.1101/2021.05.13.21254848
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Acta Médica Portuguesa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the articles published in the AMP are open access and comply with the requirements of funding agencies or academic institutions. The AMP is governed by the terms of the Creative Commons ‘Attribution – Non-Commercial Use - (CC-BY-NC)’ license, regarding the use by third parties.
It is the author’s responsibility to obtain approval for the reproduction of figures, tables, etc. from other publications.
Upon acceptance of an article for publication, the authors will be asked to complete the ICMJE “Copyright Liability and Copyright Sharing Statement “(http://www.actamedicaportuguesa.com/info/AMP-NormasPublicacao.pdf) and the “Declaration of Potential Conflicts of Interest” (http:// www.icmje.org/conflicts-of-interest). An e-mail will be sent to the corresponding author to acknowledge receipt of the manuscript.
After publication, the authors are authorised to make their articles available in repositories of their institutions of origin, as long as they always mention where they were published and according to the Creative Commons license.