Artificial Intelligence and Machine Learning in Transplantation Surgery Care Pathway

In the preoperative phase, deep learning algorithms improve anatomical assessment, volumetry and graft weight estimation, while ML-based functional status evaluation and urgency scoring refine candidate selection, and predictive models incorporating metabolic and physiological data support surgical eligibility and targeted prehabilitation strategies. Extends AI from single imaging tasks to candidate selection and prehabilitation decisions, forming an integrated view across multiple preoperative planning steps. Review-level synthesis without specific sample sizes or effect sizes; strength based on the overall description of cited studies.

In the perioperative phase, ML models outperform conventional approaches in predicting massive transfusion, intraoperative hemorrhage 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. Highlights explainability as a key element of clinical trust and incorporates functional equivalence or superiority of robotic platforms into the perioperative management evidence chain. Review-level comparison without specific performance metrics or controlled data; strength based on the overall description of cited studies.

In the postoperative phase, 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; optimized perioperative strategies including analgesic regimens and fluid management further enhance donor recovery and rehabilitation outcomes. Expands postoperative monitoring from single-complication prediction to longitudinal markers and perioperative strategy optimization, covering long-term graft function and donor recovery. Review-level synthesis without specific predictive performance or follow-up data; strength based on the overall description of cited studies.

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, improving 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. Proposes multimodal integration as a bridge linking diagnostic, prognostic and therapeutic decisions, and incorporates emerging technologies such as hyperspectral imaging and automated histopathology. Review-level description without specific validation data or clinical outcomes; strength based on the overall description of cited studies.

This is a review-level summary intended for readers seeking an overview of AI and ML applications across the transplantation surgery care pathway, including preoperative planning, perioperative management and postoperative recovery; its conclusions point toward clinical translation directions requiring rigorous validation, dataset diversity, and ethical and regulatory governance, rather than quantitative evidence directly usable for clinical decisions.

This is a fast-parsed review summary without specific figures, tables, sample sizes, effect sizes or controlled data, so it is not possible to assess risk of bias, external validity, or performance differences among AI models in the cited studies; readers interested in the validation level or clinical implementation conditions of a specific application should consult the original research.