慢性創傷ケアにおけるAI支援臨床意思決定支援システムを用いた遠隔医療:ランダム化比較試験
Dun-Hao Chang, Duc-Khanh Nguyen, Thi-Ngoc Nguyen1
1From the Department of Information Management, Yuan Ze University, Taoyuan.
Annals of plastic surgery
|January 23, 2026
まとめ
慢性創傷ケアのためのAI支援遠隔医療プラットフォームは、従来の治療法と同等の治癒率を示したが、外来受診回数は減少した。また、介護者のQOLも大幅に改善し、創傷管理におけるAIの可能性を強調した。
科学分野:
- Digital Health; Artificial Intelligence in Medicine; Wound Care Management
背景:
- Chronic wounds are a growing concern, exacerbated by an aging population and increased chronic diseases.
- Inexperienced home caregivers often lead to suboptimal chronic wound healing.
- Telemedicine offers solutions for care gaps, but manual wound assessment is burdensome; AI can enhance this process.
研究 の 目的:
- To evaluate an AI-assisted clinical decision support system, "Ulcer Care" (UC), integrated into telemedicine for chronic wound management.
- To assess the impact of the UC system on patient and caregiver psychosocial outcomes.
- To compare the effectiveness of AI-assisted telemedical care versus conventional care for chronic ulcers.
主な方法:
- A randomized controlled trial (RCT) involving 40 patients with chronic ulcers from July 2023 to February 2024.
- The UC system used AI for automated wound measurement, tissue classification, and deterioration alerts.
- Primary endpoint: wound healing at 12 weeks; secondary endpoints: quality of life (QoL), clinic visits, and complications.
主要な成果:
- Thirty-five patients completed the study; wound healing rates were comparable between the AI-assisted telemedicine group and the control group (64.9% vs 73.4%).
- The AI-assisted group required significantly fewer clinic visits (5.5 vs 13.2, P=0.009).
- Caregiver quality of life showed greater improvement in the AI-assisted group (FDLQI improvement: 10.3 vs 5.7, P=0.08).
結論:
- The "Ulcer Care" AI-assisted telemedicine platform is non-inferior to conventional care for wound healing and improves caregiver QoL.
- AI in telemedicine shows promise for reducing healthcare utilization and enhancing chronic wound management.
- Further AI accuracy and usability improvements are recommended for broader clinical adoption.
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