Artificial intelligence (AI)-assisted full-course case management for primary liver cancer: System design, preliminary implementation, and practical considerations

Abstract

Primary liver cancer presents substantial management challenges across the surgical trajectory, including high recurrence rates, prolonged rehabilitation, and fragmented post-discharge care. This correspondence presents a multidisciplinary physician–nurse co-led, artificial intelligence (AI)-assisted full-course case management model grounded in just-in-time adaptive intervention (JITAI) theory. The model spans four phases—peri-admission, perioperative, post-discharge home-based care, and long-term follow-up—supported by an intelligent platform enabling automated decision triggering, symptom monitoring, tailored health education, and intervention matching. A pilot study with 25 patients was conducted from March to May 2026 at a tertiary cancer hospital in Tianjin, China. Preliminary results revealed improvements in antiviral medication adherence (80–96%), targeted therapy adherence (96–100%), and satisfaction (99.8%), with reductions in missed follow-ups and symptom reporting delays. Multicenter controlled studies need to be conducted to evaluate this model's effectiveness, cost-effectiveness, and long-term sustainability.