---
title: "Artificial Intelligence and Machine Learning in the Transplantation Surgery Care Pathway"
id: "pubmed-42751007"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42751007"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42751007/"
doi: "10.5500/wjt.122433"
published_at: "2026-09-18T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Artificial Intelligence and Machine Learning in the Transplantation Surgery Care Pathway
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42751007
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42751007/)
- **DOI:** [10.5500/wjt.122433](https://doi.org/10.5500%2Fwjt.122433)
- **Published At:** 2026-09-18T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Artificial intelligence (AI) and machine learning (ML) are being applied across the entire **transplantation surgery care pathway**, from preoperative planning to postoperative monitoring. - Preoperative uses include deep learning for anatomical assessment, CT/MR volumetry, and graft weight estimation to support donor selection and planning. - ML methods are used for evaluating functional status, urgency scoring, metabolic and physiological data integration, and targeted prehabilitation to refine candidate selection. - Perioperative ML models predict events such as massive transfusion, intraoperative hemorrhage, and acute kidney injury; explainable outputs can improve interpretability and clinical trust. - Robotic and AI-assisted surgical platforms show at least functional equivalence to conventional methods, with potential reductions in intraoperative complications and faster recovery in high-risk patients. - Postoperatively, ML-driven models enable early prediction of sepsis, pneumonia, and graft dysfunction; longitudinal markers including recipient-to-donor eGFR ratio and imaging/biomarker approaches inform long-term graft surveillance. - Imaging-based AI innovations include **hyperspectral imaging** for real-time graft viability assessment and deep learning for automated histopathological evaluation, improving diagnostic speed, accuracy, and reproducibility. - Multimodal models that integrate electronic health records, intraoperative signals, ultrasound, and histology can provide dynamic, system-level insights into graft function and rejection risk, bridging diagnostic, prognostic, and therapeutic decision-making. - Potential to personalise transplant care and improve outcomes is significant, but translation to clinical practice requires rigorous validation, diverse datasets, and robust ethical and regulatory governance. - The review reports no conflicts of interest among authors.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Department of Surgery and Cancer, Imperial College University, London SW7 2AZ, United Kingdom. kavyesh.vivek2@nhs.net. * 2 Directorate of Renal and Transplant Services, London W12 OHS, United Kingdom. * PMID: **42751007** * PMCID: **PMC13579408** (available on 2026-09-18) * DOI: [ 10.5500/wjt.122433 ](https://doi.org/10.5500/wjt.122433) Item in Clipboard Review # Artificial intelligence and machine learning in transplantation surgery care pathway Kavyesh Vivek et al. World J Transplant. 2026. Show details Display options Display options Format Abstract PubMed PMID World J Transplant Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22World+J+Transplant%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22World+J+Transplant%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42751007/) . 2026 Sep 18;16(3):122433. doi: 10.5500/wjt.122433. ### Authors [Kavyesh Vivek](https://pubmed.ncbi.nlm.nih.gov/?term=Vivek+K&cauthor_id=42751007)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42751007/#short-view-affiliation-1 "Department of Surgery and Cancer, Imperial College University, London SW7 2AZ, United Kingdom. kavyesh.vivek2@nhs.net."), [Vassilios Papalois](https://pubmed.ncbi.nlm.nih.gov/?term=Papalois+V&cauthor_id=42751007)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42751007/#short-view-affiliation-2 "Directorate of Renal and Transplant Services, London W12 OHS, United Kingdom.") ### Affiliations * 1 Department of Surgery and Cancer, Imperial College University, London SW7 2AZ, United Kingdom. kavyesh.vivek2@nhs.net. * 2 Directorate of Renal and Transplant Services, London W12 OHS, United Kingdom. * PMID: **42751007** * PMCID: **PMC13579408** (available on 2026-09-18) * DOI: [ 10.5500/wjt.122433 ](https://doi.org/10.5500/wjt.122433) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract Artificial intelligence (AI) and machine learning (ML) are increasingly applied across the transplantation pathway, offering advances in preoperative planning, perioperative management, and postoperative recovery. In preoperative care, deep learning algorithms improve anatomical assessment, volumetry, and graft weight estimation, while ML-based functional status evaluation and urgency scoring refine candidate selection. Predictive models incorporating metabolic and physiological data further support surgical eligibility and targeted prehabilitation strategies. Perioperatively, ML models outperform conventional approaches in predicting massive transfusion, intraoperative haemorrhage, and acute kidney injury, with explainable outputs enhancing interpretability and clinical trust. Robotic and AI-assisted surgical platforms demonstrate functional equivalence or superiority to conventional methods, reducing intraoperative complications and accelerating recovery, particularly in high-risk cohorts. Postoperatively, ML-driven models enable early prediction of sepsis, pneumonia, and graft dysfunction, while longitudinal markers such as the recipient-to-donor estimated glomerular filtration rate ratio and novel imaging or biomarker-based approaches inform long-term graft monitoring. Optimised perioperative strategies, including analgesic regimens and fluid management, further enhance donor recovery and rehabilitation outcomes. Cross-cutting innovations include imaging-based AI applications such as hyperspectral imaging for real-time graft viability assessment and deep learning for automated histopathological evaluation, which improve diagnostic speed, accuracy, and reproducibility. Multimodal models integrating electronic health records, intraoperative signals, ultrasound, and histology provide dynamic, system-wide insights into graft function and rejection risk, bridging diagnostic, prognostic, and therapeutic decision-making. AI and ML thus hold substantial potential to personalise transplant care and improve outcomes. Their translation into practice, however, requires rigorous validation, dataset diversity, and strong ethical and regulatory governance. **Keywords:** Artificial intelligence; Machine learning; Peri-operative care; Pre-operative planning; Rehabilitation. ©Author(s) 2026. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article. ## Similar articles * [ Personality Theories. ](https://pubmed.ncbi.nlm.nih.gov/42475469/) Gallios JM, Iyer V, Kaylor LE.Gallios JM, et al.2026 Jun 20. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–.2026 Jun 20. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–.PMID: 42475469Free Books & Documents. * [ Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care. ](https://pubmed.ncbi.nlm.nih.gov/40283559/) Leivaditis V, Maniatopoulos AA, Lausberg H, Mulita F, Papatriantafyllou A, Liolis E, Beltsios E, Adamou A, Kontodimopoulos N, Dahm M.Leivaditis V, et al.J Clin Med. 2025 Apr 16;14(8):2729. doi: 10.3390/jcm14082729.J Clin Med. 2025.PMID: 40283559Free PMC article.Review. * [ Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges. ](https://pubmed.ncbi.nlm.nih.gov/41939694/) Parizad R, Hatwal J, Brar A, Desai R, Batta A, Mohan B.Parizad R, et al.Vasc Health Risk Manag. 2026 Mar 28;22:590502. doi: 10.2147/VHRM.S590502. eCollection 2026.Vasc Health Risk Manag. 2026.PMID: 41939694Free PMC article. * [ [Expert consensus on optimizing the multidisciplinary clinical pathways and management models for pulmonary function testing]. ](https://pubmed.ncbi.nlm.nih.gov/41912385/) Task Forces on Pulmonary Function Testing from Chinese Association of Chest Physicians (CACP); Chinese Thoracic Society (CTS); Chinese Geriatrics Society (CGS).Task Forces on Pulmonary Function Testing from Chinese Association of Chest Physicians (CACP), et al.Zhonghua Jie He He Hu Xi Za Zhi. 2026 Apr 12;49(4):385-395. doi: 10.3760/cma.j.cn112147-20251114-00713.Zhonghua Jie He He Hu Xi Za Zhi. 2026.PMID: 41912385Chinese. * [ AI-Driven Optimization of Kidney Allocation: Enhancing Precision in Donor-Recipient Matching. ](https://pubmed.ncbi.nlm.nih.gov/42538662/) Okhovvat M, Amirkhanlou S, Simforoosh N, Poor-Reza Gholi F, Erfani SS.Okhovvat M, et al.Exp Clin Transplant. 2026 Jul;24(Suppl 2):78-83. doi: 10.6002/ect.MESOT2025.O25.Exp Clin Transplant. 2026.PMID: 42538662 [ See all similar articles ](https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed&from_uid=42751007) ## References 1. 1. Oh N, Kim JH, Rhu J, Jeong WK, Choi GS, Kim JM, Joh JW. 3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning. Int J Surg. 2024;110:1975–1982. - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC11020049/) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/38668656/) 2. 1. Park R, Lee S, Sung Y, Yoon J, Suk HI, Kim H, Choi S. Accuracy and Efficiency of Right-Lobe Graft Weight Estimation Using Deep-Learning-Assisted CT Volumetry for Living-Donor Liver Transplantation. Diagnostics (Basel) 2022;12:590. - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC8946991/) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/35328143/) 3. 1. Machry M, Ferreira LF, Lucchese AM, Kalil AN, Feier FH. Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence. World J Transplant. 2023;13:290–298. - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC10758682/) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/38174151/) 4. 1. Brennan M, Puri S, Ozrazgat-Baslanti T, Feng Z, Ruppert M, Hashemighouchani H, Momcilovic P, Li X, Wang DZ, Bihorac A. Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study. Surgery. 2019;165:1035–1045. - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC6502657/) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/30792011/) 5. 1. Chu NM, Chen X, Bae S, Brennan DC, Segev DL, McAdams-DeMarco MA. Changes in Functional Status Among Kidney Transplant Recipients: Data From the Scientific Registry of Transplant Recipients. Transplantation. 2021;105:2104–2111. - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC8273213/) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/33449609/) Show all 40 references ## Publication types * Review Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Review%22%5Bpt%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Review) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42751007/) [x] Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM **Send To** * [Clipboard](https://pubmed.ncbi.nlm.nih.gov/42751007/) * [Email](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42751007%2F%23open-email-panel) * [Save](https://pubmed.ncbi.nlm.nih.gov/42751007/) * [My Bibliography](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42751007%2F%23open-bibliography-panel) * [Collections](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42751007%2F%23open-collections-panel) * [Citation Manager](https://pubmed.ncbi.nlm.nih.gov/42751007/) [x] NCBI Literature Resources [MeSH](https://www.ncbi.nlm.nih.gov/mesh/) [PMC](https://www.ncbi.nlm.nih.gov/pmc/) [Bookshelf](https://www.ncbi.nlm.nih.gov/books) [Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). 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