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Cited by 67Artificial Intelligence in Healthcare and EducationTransplantation: Methods and OutcomesOrgan Donation and Transplantation

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation

Open access
Peloso, Andrea, Moeckli, Beat, Delaune, Vaihere, Oldani, Graziano, Andres, Axel, Compagnon, Philippe·2022·Transplant International
DOI 10.3389/ti.2022.10640PMC PMC9290190
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Abstract

Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.

AI snapshot

Written from the paper’s full text.

TL;DR

This narrative review surveys AI applications in solid organ transplantation across organ allocation/donor-recipient matching, transplant oncology, immunosuppression, and transplant pathology. It argues AI has potential to improve graft and patient survival but remains mostly under development, with many algorithms still needing external validation.

Key findings

  • AI-based allocation and matching models showed improved predictions over standard scores: OPOM used 1,618,966 observations and reduced mortality on average by 417.96 deaths for 6,139 liver transplantations; MADR-E AI-ANNs predicted graft survival (AUC 0.81) and graft loss (AUC 0.82); Bae et al.’s random survival forest achieved a C-statistic of 0.637 versus 0.6 for KDRI.
  • In transplant oncology, AI models outperformed traditional criteria: MORAL pre- and post-transplant scores had C-statistics of 0.82 and 0.86 versus 0.63 for Milan criteria, and 0.91 when combined; Metroticket 2.0 showed external validation accuracy of 0.721 (95% CI 0.648–0.793) for 5-year survival; a Toronto CoxNet calculator outperformed AFP by 0.118, MORAL by 0.130, and HALT-HCC by 0.102.
  • For immunosuppression, machine learning predicted tacrolimus stable dose better than logistic regression, though with a relatively small difference; a three-gene urinary signature predicted acute kidney rejection with AUC 0.85 internally and 0.74 externally.
  • In transplant pathology, deep learning classified kidney biopsy components across centers but struggled with sclerotic glomeruli and atrophic tubuli; machine learning also quantified liver steatosis in frozen sections and was highly accurate compared with an expert pathologist.

Methods

  • Narrative review identifying four key areas: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology.
  • No primary data collection; the paper synthesizes published AI, machine learning, and deep learning studies, including registry datasets and clinical cohorts cited in the text.
  • Main analysis is a qualitative discussion of cited model performance metrics and potential clinical implementations; no systematic search strategy or meta-analysis is described.

Limitations

  • Stated in the paper: several algorithms were developed in a single institution and still need external validation; in some cases AI cannot provide significant improvements over current models; the best strategy for implementing AI into daily clinical practice is unclear; many physicians remain unconvinced AI can play a weighty role in medicine.
  • Stated for cited work: Yasodhara et al. had a retrospective design, missing comorbidity data, unclear immunosuppression information, and unusually few patients with steatohepatitis; OPOM and the Toronto HCC-recurrence calculator still require external validation.
  • Evident from the text: as a narrative review, it lacks a systematic search strategy, inclusion criteria, risk-of-bias assessment, and quantitative pooling, so conclusions rely on selected examples rather than a comprehensive evidence synthesis.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/ti.2022.10640

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 66 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    The allocation process depends on the timeframe during which the organ remains viable once harvested, which ranges from a few to 36 h, depending on the organ.

    “Additionally, the allocation (and donor-recipient matching) process depends on the timeframe during which the organ remains viable once harvested, which ranges from a few to 36 h, depending on the organ (5).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [5] Messner, 2019, Emerging Technologies in Organ Preservation, Tissue Engineering and Regenerative Medicine: A Blessing or Curse for Transplantation?, Transpl Int source

      Not checked in the free run.

  • Premisecitation

    OPOM predicts the probability of a patient's 3-month mortality or waitlist removal given their characteristics.

    “This model, named Optimal Prediction of Mortality (OPOM), predicts the probability of a patient’s 3-month mortality or waitlist removal given their characteristics.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [8] Bertsimas, 2019, Development and Validation of an Optimized Prediction of Mortality for Candidates Awaiting Liver Transplantation, Am J Transpl source

      Not checked in the free run.

  • Premisecitation

    Using the Standard Transplant Analysis and Research dataset, OPOM provided more accurate and objective predictions than MELD.

    “Using the Standard Transplant Analysis and Research dataset (1618966 observations), OPOM provided more accurate and objective predictions than MELD.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [8] Bertsimas, 2019, Development and Validation of an Optimized Prediction of Mortality for Candidates Awaiting Liver Transplantation, Am J Transpl source

      Not checked in the free run.

  • Premisecitation

    Bertsimas and co-workers proposed a machine learning-based model for alternative liver allocation, named OPOM.

    “In 2019, Bertsimas and co-workers proposed a machine learning-based model for alternative liver allocation (8).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Bertsimas, 2019, Development and Validation of an Optimized Prediction of Mortality for Candidates Awaiting Liver Transplantation, Am J Transpl source

      Not checked in the free run.

  • Premisecitation

    The Child-Pugh classification, MELD, KAS and LAS are the most important algorithms currently used.

    “The Child-Pugh classification, the Model of End Stage Liver Disease (MELD), the Kidney Allocation System (KAS) and the Lung Allocation System (LAS) are the most important algorithms currently used (6).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] UNOS Allocation Calculators (2022)

      Not checked in the free run.

  • Classified as probably not a substantial claim. 12
  • May combine two claims. 22

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    AI has the potential to improve graft and patient survival in solid organ transplantation.

    “We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The allocation systems used by Eurotransplant and UNOS are intended as objective and transparent procedures to make the best possible match.

    “The allocation systems used by Eurotransplant in Europe, and the United Network for Organ Sharing (UNOS) in the US, are intended as objective and transparent procedures to make the best possible match (3, 4).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [3] UNOS Organ Procurement and Allocation Policies (2022)

      Not checked in the free run.

    • cites [4] Eurotransplant Allocation System (2022)

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [7] Briceño, 2020, Artificial Intelligence and Organ Transplantation: Challenges and Expectations, Curr Opin Organ Transpl source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationnone given

    Four key areas of transplantation could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology.

    “Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    External validation of OPOM still needs to be performed.

    “External validation still needs to be performed.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Briceño et al. designed a 3-month graft mortality prediction model and performed binary analysis for donor-recipient matching via AI-ANNs.

    “In their work, Briceño et al. designed a 3-month graft mortality prediction model based on 64 donor and recipient characteristics and performed a binary analysis (graft survival/loss) for donor-recipient matching via AI-ANNs.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12] Briceño, 2014, Use of Artificial Intelligence as an Innovative Donor-Recipient Matching Model for Liver Transplantation: Results from a Multicenter Spanish Study, J Hepatol source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Neutrophil-lymphocyte ratio ≤5, AFP > 200 ng/ml and tumor size >3 cm are pre-transplantation predictive factors of decreased recurrence-free survival.

    “Specifically, neutrophil-lymphocyte ratio ≤5, alpha-fetoprotein (AFP) > 200 ng/ml and tumor size >3 cm have been classified as pre-transplantation predictive factors of decreased recurrence-free survival.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [21] Halazun, 2017, Recurrence after Liver Transplantation for Hepatocellular Carcinoma: A New MORAL to the story, Ann Surg source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The Bae et al. evaluation yielded a C-statistic of 0.637 for the RSF algorithm, which is slightly higher than the KDRI model's 0.6.

    “The result of the evaluation yielded a C-statistic of 0.637 for the RSF algorithm, which is slightly higher than the Kidney Donor Risk Index (KDRI) model’s 0.6.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [17] Bae, 2019, Who Can Tolerate a Marginal Kidney? Predicting Survival after Deceased Donor Kidney Transplant by Donor-Recipient Combination, Am J Transpl source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Both MORAL scores demonstrated predictive superiority (C-statistic of 0.82 and 0.86, respectively) compared to Milan criteria (C-statistic of 0.63) for forecasting tumor recurrence.

    “Both scored (pre- and post-transplant) demonstrated predictive superiority (C-statistic of 0.82 and 0.86, respectively) when compared to Milan criteria for forecasting tumor recurrence (C-statistic of 0.63).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [21] Halazun, 2017, Recurrence after Liver Transplantation for Hepatocellular Carcinoma: A New MORAL to the story, Ann Surg source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    HCC grade 4, tumor size >3 cm, >3 tumor lesions, and vascular invasion are post-transplantation negative predictive factors.

    “Likewise, HCC grade 4, tumor size >3 cm, > 3 tumor lesions, and vascular invasion have been identified as post-transplantation negative predictive factors.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [21] Halazun, 2017, Recurrence after Liver Transplantation for Hepatocellular Carcinoma: A New MORAL to the story, Ann Surg source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The CoxNet-based algorithm outperformed AFP by 0.118, MORAL by 0.130 and HALT-HCC by 0.102.

    “The CoxNet-based algorithm outperformed AFP by 0.118, MORAL by 0.130 and HALT-HCC by 0.102.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23] Ivanics, 2021, The Toronto Post Liver Transplant Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach, Liver Transpl source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Hermsen et al. developed and successfully implemented a deep learning algorithm (a convolutional neural network) that divided kidney biopsies from different centers into their anatomical components.

    “In a recent study, Hermsen et al., successfully implemented a deep learning algorithm to divide kidney biopsies from different centers into their anatomical components (46).”
    • Also stated in: § AI in Transplant Pathology · plain
      “The authors developed a convolutional neural network that classified each anatomical component.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [46] Hermsen, 2019, Deep Learning-Based Histopathologic Assessment of Kidney Tissue, J Am Soc Nephrol source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    AI can help tap into the potential of underutilized donor and recipient information to fine-tune immunosuppression, and optimize graft and patient survival.

    “AI can help us tap into this potential to fine-tune immunosuppression, and optimize graft and patient survival.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    A three-gene expression signature obtained an AUC of 0.85 for the discrimination between acute rejection and no rejection in the authors' own cohort, and an AUC of 0.74 upon external validation.

    “The authors obtained an AUC of 0.85 with a three-gene expression signature for the discrimination between acute rejection and no rejection in their own cohort, and an AUC of 0.74 upon external validation.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] Suthanthiran, 2013, Urinary-cell mRNA Profile and Acute Cellular Rejection in Kidney Allografts, N Engl J Med source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The Hermsen et al. algorithm performed well in identifying healthy glomeruli but struggled to identify more challenging structures such as sclerotic glomeruli or atrophic tubuli.

    “While the algorithm performed well in identifying healthy glomeruli, it struggled to identify more challenging structures such as sclerotic glomeruli or atrophic tubuli.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [46] Hermsen, 2019, Deep Learning-Based Histopathologic Assessment of Kidney Tissue, J Am Soc Nephrol source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The Perez-Sanz et al. algorithm, available as an open-source interactive web platform, proved highly accurate in comparison with the assessment of an expert pathologist.

    “Their algorithm, available as an open-source interactive web platform (50), proved highly accurate in comparison with the assessment of an expert pathologist.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [50] Pérez-Sanz, 2021, Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation, Sensors (Basel) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The Hermsen et al. study provides important groundwork and paves the way for further image analysis of kidney transplant biopsies.

    “Nevertheless, this study provides important groundwork and paves the way for further image analysis of kidney transplant biopsies.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    This steatosis-quantification tool could be extremely valuable for decision-making in remote procurement locations, where an expert pathologist is not readily available.

    “This tool could be extremely valuable for the decision-making in remote procurement locations, where an expert pathologist is not readily available.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationcitation

    The challenges ahead for bringing AI to transplant pathology are implementing digital workflows to routinely scan histological slides and collaborating between centers to establish image databases and bring existing AI tools to transplant pathology.

    “Challenges that lie ahead are the implementation of digital workflows to routinely scan histological slides, and collaboration between centers to establish image databases and bring the existing AI tools to transplant pathology (57).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [57] Loupy, 2020, The Banff 2019 Kidney Meeting Report (I): Updates on and Clarification of Criteria for T Cell- and Antibody‐mediated Rejection, Am J Transpl source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Radiology is one step ahead and shows what is possible with the emerging field of radiomics-the extraction of data from radiograms to diagnose cancer, predict outcomes or guide therapy.

    “Radiology is one step ahead and shows what is possible with the emerging field of radiomics—the extraction of data from radiograms to diagnose cancer, predict outcomes or guide therapy (54–56).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [54] Grosu, 2021, Machine Learning-Based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-Of-Concept Study, Radiology source

      Not checked in the free run.

    • cites [55] Ligero, 2021, A CT-based Radiomics Signature is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors, Radiology source

      Not checked in the free run.

    • cites [56] Skawran, 2021, Can Magnetic Resonance Imaging Radiomics of the Pancreas Predict Postoperative Pancreatic Fistula?, Eur J Radiol source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The same Hermsen et al. algorithm worked on histological samples from different centers, thereby addressing the issue of reproducibility.

    “Most importantly, the authors proved that the same algorithm worked on histological samples from different centers, thereby addressing the issue of reproducibility.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [46] Hermsen, 2019, Deep Learning-Based Histopathologic Assessment of Kidney Tissue, J Am Soc Nephrol source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Automated image analysis, feature recognition, data extraction and deep learning models are everyday reality for the tech giants but have only partially reached precision pathology.

    “Automated image analysis, feature recognition, data extraction and deep learning models are everyday reality for the tech giants but have only partially reached precision pathology (51–53).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [51] Yang, 2019, Applying Deep Neural Network Analysis to High-Content Image-Based Assays, SLAS Discov source

      Not checked in the free run.

    • cites [52] Wulczyn, 2020, Deep Learning-Based Survival Prediction for Multiple Cancer Types Using Histopathology Images, PLoS One source

      Not checked in the free run.

    • cites [53] Noel, 2018, Peri-personal Space as a Prior in Coupling Visual and Proprioceptive Signals, Sci Rep source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    A more comprehensive AI-based decision model could enlarge the pool of transplantable organs and improve patient outcomes.

    “On the one hand, this could bring new insights to potentially enlarge the pool of transplantable organs and, on the other, improve patient outcomes.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationnone given

    The true potential of AI in healthcare has yet to be fully exploited, and its application in solid organ transplantation is mostly under development.

    “The true potential of AI in healthcare has yet to be fully exploited and its application in solid organ transplantation is mostly under development.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationnone given

    Implementing AI into daily clinical practice is an ongoing challenge and the best strategy forward is unclear.

    “Implementing AI into daily clinical practice is an ongoing challenge and the best strategy forward is unclear.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    AI is on a trajectory of exponential growth and has the potential to improve how we experience our lives and to extend life itself.

    “AI is on a trajectory of exponential growth, and has the potential to improve how we experience our lives and to extend life itself.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    Many other domains of medicine beyond transplantation could benefit from AI, including precision medicine, AI-assisted computer vision, augmented and virtual reality, and AI-assisted integration and collection of patients' records.

    “Moreover, while this manuscript focuses on the use of AI in transplantation, many other domains could benefit from it.”
    • Also stated in: § Conclusion: AI Current Pitfalls and Future Promises · plain
      “Precision medicine (genetic-based solutions, drug discovery and development) (61), AI-assisted computer vision (62), augmented and virtual reality (63) and the AI-assisted integration and collection of patients’ records (64, 65) are just few examples of how AI can be applied to medicine.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [61] Bohr, 2020, The Rise of Artificial Intelligence in Healthcare Applications, Artif Intell Healthc source

      Not checked in the free run.

    • cites [62] Gurgitano, 2021, Interventional Radiology Ex-Machina: Impact of Artificial Intelligence on Practice, Radiol Med source

      Not checked in the free run.

    • cites [63] Wendler, 2021, How Molecular Imaging Will Enable Robotic Precision Surgery: The Role of Artificial Intelligence, Augmented Reality, and Navigation, Eur J Nucl Med Mol Imaging source

      Not checked in the free run.

27 of 27

  • The paper relies on this without support.

    Pre- and post-transplantation patient care requires complex decision-making.

    “The pre- and post-transplantation patient care requires complex decision-making.”

  • The paper relies on this without support.

    AI is still in its infancy and we lack validated algorithms that could accurately drive organ selection, predict potential rejections or attenuate postoperative complications.

    “AI is still in its infancy and, so far, we lack validated algorithms that could accurately drive organ selection, predict potential rejections or attenuate postoperative complications.”

  • The paper relies on this without support.

    In the last few decades, AI applications have already contributed to lower incidence of rejection, and fine-tuning of the transplantation and organ preservation processes.

    “Nevertheless, in the last few decades, AI applications have already contributed to lower incidence of rejection, and fine-tuning of the transplantation and organ preservation processes.”

  • The paper relies on this without support.

    AI has proven highly efficient in image processing.

    “AI has proven highly efficient in image processing.”

  • The paper relies on this without support.

    Trained pathologists are in short supply.

    “Unfortunately, trained pathologists are in short supply.”

  • The paper relies on this without support.

    Quick and reliable assessment of liver steatosis during procurement still presents a challenge.

    “In liver transplantation, quick and reliable assessment of liver steatosis during procurement still presents a challenge.”
6 of 6

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In organ transplantation, AI could assist in matching donors with recipients efficiently, but ethical nuances such as patient preferences, medical histories, and urgency require human judgment.

    “In organ transplantation, AI could assist in matching donors with recipients efficiently.However, ethical nuances such as patient preferences, medical histories, and urgency require human judgment [124], [125].”

    From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [125]

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Related

Authors
Peloso, A., Moeckli, B., Delaune, V., Oldani, G., Andres, A., & Compagnon, P.
Affiliations
University of Geneva (CH)
Corresponding author
Peloso, Andrea
ORCID
Venue
Transplant International
Publisher
Frontiers Media SA
Published
2022-07-04
Volume
35
Pages
10640-10640
ISSN
  • 0934-0874 (ISSN-L)
  • 1432-2277
DOI
10.3389/ti.2022.10640
PMC
PMC9290190
OpenAlex
W4283792648
Cited by
67
References
61
Citations by year
2026: 15 · 2025: 17 · 2024: 22 · 2023: 12 · 2022: 1
FWCI
0.89
Type
journal article
Language
en
Open access
Licence
Locations
  • Transplant International (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Archive ouverte UNIGE (University of Geneva) (acceptedVersion, cc-by)
  • Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Surgery › Transplantation: Methods and Outcomes
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Organ Donation and Transplantation
Keywords
artificial intelligence, solid organ transplantation, organ allocation algorithms, donor-recipient matching, transplant oncology, immunosuppression, graft survival, digital health
MeSH terms
Algorithms, Artificial Intelligence, Forecasting, Humans, Medical Oncology, Organ Transplantation

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