Augmenting the Transplant Team With Artificial Intelligence : Toward Meaningful AI Use in Solid Organ Transplant

Copyright © 2021 Clement and Maldonado..

Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:12

Enthalten in:

Frontiers in immunology - 12(2021) vom: 01., Seite 694222

Sprache:

Englisch

Beteiligte Personen:

Clement, Jeffrey [VerfasserIn]
Maldonado, Angela Q [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
Decision making
Ethics
Immunosuppression
Journal Article
Machine learning
Natural language processing
Shared decision model
Systematic Review
Transplant

Anmerkungen:

Date Completed 14.12.2021

Date Revised 14.12.2021

published: Electronic-eCollection

Citation Status MEDLINE

doi:

10.3389/fimmu.2021.694222

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM327250151