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Artificial intelligence and organ transplantation: challenges and expectations
Abstract
PURPOSE OF REVIEW: Classifiers based on artificial intelligence have emerged in all areas of medicine. Although very subtle, many decisions in organ transplantation can now be addressed in a more concisely manner with the support of these classifiers. RECENT FINDINGS: Any aspect of organ transplantation (image processing, prediction of results, diagnostic proposals, therapeutic algorithms or precision treatments) consists of a set of input variables and a set of output variables. Artificial intelligence classifiers differ in the way they establish relationships between the input variables, how they select the data groups to train patterns and how they are able to predict the possible options of the output variables. There are hundreds of classifiers to achieve this goal. The most appropriate classifiers to address the different aspects of organ transplantation are Artificial Neural Networks, Decision Tree classifiers, Random Forest, and Naïve Bayes classification models. There are hundreds of examples of the usefulness of artificial intelligence in organ transplantation, especially in image processing, organ allocation, D-R matching, precision pathology, real-time immunosuppression, transplant oncology, and predictive analysis. SUMMARY: In the coming years, clinical transplant experts will increasingly use Deep Learning-based models to support their decisions, specially in those cases where subjectivity is common.
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Current allocation systems cannot prioritize recipients in real time and need constant modifications and addition of exceptions.
“Whilst well integrated into clinical practice, these systems cannot prioritize recipients in real time and need constant modifications (7) and addition of exceptions.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [7]
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Metadata
- Authors
- Briceño, J.
- Affiliations
- Instituto Maimónides de Investigación Biomédica de Córdoba (ES)
- Corresponding author
- Briceño, Javier
- ORCID
- Briceño, Javier
- Venue
- Current Opinion in Organ Transplantation
- Published
- 2020-06-01
- Volume
- 25
- Issue
- 4
- Pages
- 393-398
- ISSN
- 1087-2418 (ISSN-L)
- 1531-7013
- OpenAlex
- W3031288039
- Cited by
- 46
- References
- 32
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 11 · 2022: 11 · 2021: 6 · 2020: 2
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Organ Transplantation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Organ Donation and Transplantation
- Keywords
- artificial intelligence, organ transplantation, machine learning classifiers, artificial neural networks, decision tree classification, random forest, naive Bayes, organ allocation, donor-recipient matching, transplant immunosuppression, transplant oncology, predictive analytics, medical image processing
- MeSH terms
- Artificial Intelligence, Bayes Theorem, Humans, Organ Transplantation, Neural Networks, Computer
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