Journal article
Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation
Current transplantation reports, Vol.8(3), pp.235-240
2021
DOI: 10.1007/s40472-021-00336-z
PMCID: PMC8317681
PMID: 34341714
Abstract
Purpose of Review
Artificial intelligence (AI), machine learning, and technology-enabled remote patient care have evolved rapidly and have now been incorporated into many aspects of medical care. Transplantation is fortunate to have large data sets upon which machine learning algorithms can be constructed. AI are now available to improve pretransplant management, donor selection, and post-operative management of transplant patients.
Recent Findings
Changes in patient and donor characteristics warrant new approaches to listing and organ acceptance practices. Machine learning has been employed to optimize donor selection to identify patients likely to benefit from transplantation of higher risk organs, increasing organ discard and reducing waitlist mortality. These models have greater precisions and predictive ability than currently employed metrics including the Kidney Donor Profile Index and the expected posttransplant survival models. After transplant, AI tools have been developed to optimize immunosuppression management, track patients adherence, and assess graft survival.
Summary
AI and technology-enabled management tools are now available throughout the transplant journey. Unfortunately, those are frequently not available at the point of decision (patient listing, organ acceptance, posttransplant clinic), limiting utilization. Incorporation of these tools into the EMR, the Donor Net® organ offer system, and mobile devices is vital to ensure widespread adoption.
Details
- Title: Subtitle
- Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation
- Creators
- Issac R. Schwantes - Oregon Health & Science UniversityDavid A. Axelrod - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Current transplantation reports, Vol.8(3), pp.235-240
- Publisher
- Springer International Publishing
- DOI
- 10.1007/s40472-021-00336-z
- PMID
- 34341714
- PMCID
- PMC8317681
- ISSN
- 2196-3029
- eISSN
- 2196-3029
- Language
- English
- Date published
- 2021
- Academic Unit
- Surgery
- Record Identifier
- 9984321863402771
Metrics
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