Artificial intelligence and machine learning in transplantation surgery care pathway

Table 2 Consolidated future directions for artificial intelligence and machine learning research across the transplantation surgery care pathway, organised by domain

Domain	Future directions
Preoperative imaging & planning	Multicentre validation, inclusion of diverse donor/recipient populations, integration of AI tools into clinical workflows, expansion to paediatric and left-lobe graft planning
Risk stratification & functional status	Prospective multicentre validation, integration of objective frailty and performance measures, incorporation of socio-clinical context into models
Prehabilitation & surgical eligibility	Broader external validation, development of precision prehabilitation programmes, incorporation of metabolic biomarkers, and standardised eligibility frameworks
Perioperative risk prediction	Incorporation of intraoperative real-time signals, multicentre prospective trials, explainability-focused models for clinical adoption
Robotic & ML-enhanced surgery	Cost-effectiveness studies, technological refinement, definition of training standards, long-term comparative outcome studies
Postoperative monitoring	Large-scale validation, integration with real-time EHR systems, multimodal models combining clinical, imaging, and biomarker data
Longitudinal recovery & rehabilitation	Validation of composite recovery biomarkers, mechanistic studies on perioperative interventions, personalised rehabilitation protocols
Cross-cutting imaging & pathology	Development of real-time video-capable imaging, dataset expansion, integration of AI histopathology into diagnostic standards
Multiorgan & multidisciplinary models	Multimodal, cross-organ models; integration into perioperative and critical care decision-support systems; dataset standardisation across institutions
Ethical & regulatory frameworks	Development of ethical governance frameworks, bias audits, and explainable AI standards to guide deployment in transplantation

EHR: Electronic health record; ML: Machine learning; AI: Artificial intelligence.