AI in Wound Care: Assessment for Hard-to-Heal Wounds

Artificial intelligence (AI) is rapidly expanding our capabilities to assess and manage hard‑to‑heal wounds, achieving high accuracy in specific tasks.

Limitations of traditional manual wound assessments not only lead to diagnostic inconsistencies but also increase patient frustration, affecting overall care satisfaction. However, AI technologies offer transformative potential by mitigating these challenges.

Recent research shows that AI-enhanced wound evaluations provide a new tier of accuracy that is transforming diagnostics. In image-based wound classification tasks, studies report AUROC around 0.90, with reductions in inter‑rater variability in study settings.

Among early adopters and pilot programs, such findings are beginning to reshape how clinicians integrate AI tools to better address chronic wound challenges. The same AI algorithms that enhance imaging precision also standardize key elements of documentation (for example, wound area measurements and tissue‑type labels), enabling more consistent tracking that can inform debridement and dressing choices.

Emerging studies suggest AI-enabled remote monitoring may aid earlier infection recognition and individualized planning, but formal guideline endorsements are limited; benefits remain investigational. These capabilities could be particularly valuable for rural health providers by allowing clinicians to track wound healing in real time.

Despite promising results, AI performance depends on adequate infrastructure and staffing; without these, tools may underperform—reinforcing the need for investment in deployment and training. To realize that promise—particularly for rural providers—training modules integrated into medical education and continuing education will be essential.

Key Takeaways:

• When applied to image-based tasks, AI can deliver more consistent measurements than manual assessment, supporting more reliable tracking.
• Integrating AI in clinical practice can improve documentation consistency and support decision-making, but most benefits are demonstrated in study or pilot settings.
• Successful implementation requires infrastructure, staffing, and education; without these, tools may underperform.