Evidence map›Paper›PMID 42368302›Full record

ArticleGlobal health & medicine2026

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

Yanhui Wang, Xian Yue, Ruishuang Zheng, Lu Chen, Ying Wang, Wanmin Qiang

Abstract readLetter
In one paragraph

Article in Global health & medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yanhui WangDepartment of Hepatobiliary Cancer, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Xian YueDepartment of Hepatobiliary Cancer, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Ruishuang ZhengDepartment of Hepatobiliary Cancer, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Lu ChenDepartment of Hepatobiliary Cancer, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Ying WangDepartment of Nursing, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Wanmin QiangDepartment of Nursing, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencecase managementfull-course managementjust-in-time adaptive interventionprimary liver cancer

Identifiers

PMID42368302
PMCPMC13306866

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.