{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T06:30:23Z","timestamp":1786516223631,"version":"build-2736575974"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chemotherapy imposes substantial physical and psychological burdens that may reduce treatment adherence and quality of life. Traditional follow-up is often fragmented, reactive, and insufficiently personalized. Artificial intelligence (AI) may improve continuity, responsiveness, and patient-centered care; however, its integration with humanistic care remains underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This prospective, single-center, quasi-experimental study was conducted from January to June 2024. A total of 189 patients undergoing chemotherapy were allocated to an AI-assisted follow-up group (\n                      <jats:italic>n<\/jats:italic>\n                      = 96) or a conventional care group (\n                      <jats:italic>n<\/jats:italic>\n                      = 93). The nurse-supervised AI-assisted follow-up system integrated rule-based risk monitoring, DeepSeek-based large language model-assisted communication, personalized follow-up, and data visualization. Outcomes included self-management ability, quality of life (QoL), and patient satisfaction. Between-group comparisons used independent-samples\n                      <jats:italic>t<\/jats:italic>\n                      -tests, with adjusted analyses performed using analysis of covariance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      At 3 months, the AI-assisted group had higher self-management ability scores (168.83 \u00b1 7.03 vs. 150.11 \u00b1 7.39), QoL scores (66.34 \u00b1 4.30 vs. 59.81 \u00b1 4.46), and satisfaction scores (52.35 \u00b1 2.44 vs. 48.75 \u00b1 2.48) than the conventional care group (all\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.001). Follow-up completion was 96%, and the 24-h response rate was 98%. Of 214 generated alerts, 87.4% were clinically validated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The nurse-supervised AI-assisted follow-up system was feasible and associated with better short-term patient-reported outcomes than conventional follow-up. Its distinguishing feature was the integration of rule-based risk management, large language model-assisted communication, and operationalized humanistic care within a closed-loop oncology nursing workflow. Randomized multicenter studies are required to confirm these findings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1863027","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:54:39Z","timestamp":1786514079000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of an AI-assisted digital health follow-up system integrating humanistic care for patients undergoing chemotherapy: a prospective quasi-experimental study"],"prefix":"10.3389","volume":"14","author":[{"given":"Luning","family":"Jing","sequence":"first","affiliation":[{"name":"The Department of Radiation Oncology, Shinan Campus, Affiliated Hospital of Qingdao University","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoling","family":"Ye","sequence":"additional","affiliation":[{"name":"The Department of Radiation Oncology, Shinan Campus, Affiliated Hospital of Qingdao University","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Liu","sequence":"additional","affiliation":[{"name":"The Department of Gynecology, Pingdu Campus, Affiliated Hospital of Qingdao University","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1007\/s11427-024-2657-y","article-title":"Understanding the global cancer statistics 2022: growing cancer burden","volume":"67","author":"Liu","year":"2024","journal-title":"Sci China Life Sci."},{"key":"B2","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s00520-007-0380-2","article-title":"Symptoms and treatment burden associated with cancer treatment: results from a cross-sectional national survey in the 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