The Role of AI in Chronic Wound Care
Streamlining Care and Improving Outcomes
As people age, they can experience an increased risk of developing chronic wounds. In the United States, these wounds cost the health care system approximately $20 billion each year,1 with the highest incidence of those conditions occurring in individuals 75 and older.2 Because proper wound care is crucial to preventing complications and maintaining overall health and well-being, individuals with chronic wounds need frequent medical visits for wound care management.3
Artificial intelligence (AI) is a way to streamline the wound care process, improve patient outcomes, generate new breakthroughs, advance education in the field, reallocate valuable resources, and reduce costs. Because of this technology’s success and future potential, it’s now being used to help make diagnosing, assessing, and caring for chronic wounds more accurate and efficient.
Understanding and Assessing Chronic Wounds
Other types of chronic wounds include neuropathic ulcers, affecting pressure points in the peripheral nervous system, and ischemic ulcers, which result from poor circulation. Older adults can also experience pressure injuries caused by friction and other traumas; these occur most often on the lower back and the heel. Such injuries can vary by depth and severity.4
One major source of chronic wounds is diabetes mellitus. CMS reported in 2017 that one in five Americans aged 65 or older had been diagnosed with type 1 or type 2 diabetes.5,6 As many as 50% of individuals who live with some form of diabetes can experience diabetic neuropathy—nerve damage from elevated glucose levels—potentially leading to chronic wounds, especially in the feet, a condition known as diabetic foot syndrome (DFS).7 Located at the bottom of the foot, this type of wound is open and circular and is usually preceded by a blister. Because patients typically do not feel those injuries and, hence, do not notice them, such wounds can lead to gangrene and even amputation.7
According to a 2016 study, “DFS prevalence rates between 4% and 15% have been recorded. Among all possible complications of type 2 diabetes mellitus, DFS is the leading reason for hospitalization [sic]. Among all diabetics the lifetime risk for developing a diabetic foot ulceration is 25% of which the majority will need amputation within four years of initial diagnosis. … Compared to nondiabetics the need for major amputation is about 30 to 40 times higher in patients with diabetes mellitus type 2. The five-year mortality rate following amputation is estimated at 39%–68%.”7
Given the seriousness of this condition, particularly for an older population, proper wound diagnosis and treatment is crucial.
For wound care to be effective, patients require a comprehensive approach, which encompasses care for the wound itself and also any health conditions that could be causing the issue. Becoming aware of a wound, keeping it clean and dry, using specified dressings to help promote healing, managing any accompanying pain, and addressing other health issues that could impact healing are all vital to help promote healing.
The acronym TIME can be helpful when caring for chronic wounds—examining the Tissue, assessing Infection or Inflammation, balancing Moisture, and assessing the Edges of the wound.8 Additionally, protein supplements can be helpful, as can disease-specific wound therapy. Among the latter treatments, depending on a wound’s type, are pressure reduction, compression therapy, and circulation evaluation. In some cases, advanced wound care techniques such as debridement, skin grafting, or negative pressure wound therapy may be necessary to promote healing and prevent complications. Overall, effective wound care is essential for maintaining the health, well-being, and quality of life of older adults and can help to prevent complications and improve outcomes.
AI and Wound Care
AI is a machine’s ability to complete cognitive tasks and achieve a specific goal based on the information available. This technology is changing many aspects of daily life, including health care. Computers are able to scan billions of pieces of data, discern what is relevant, and recognize patterns in ways that are beyond human capability.
Chronic wound assessment typically relies on subjective, time-consuming methods, including that of visual inspection and manual measurements by health care providers. One assessment tool health care providers employ is the Bates-Jensen Wound Assessment Tool, an objective measurement tool that is used to both assess chronic wounds and track their progress. Another common instrument is the Pressure Ulcer Scale for Healing, which also relies on objective measurements. Because those manual tests rely on health care providers to administer them, they can hamper chronic wound care and are not always reliable.
AI can help improve the accuracy and consistency of wound assessment and progression analysis, which can help providers develop effective treatment plans. Using advanced algorithms to analyze images of a wound and provide objective data about its size, depth, and other characteristics, AI-based tools can enable standardization of the assessment process and help ensure health care providers use the same evaluation criteria.
Chronic wound treatment can also be automated using AI. One AI-based tool that can benefit those areas is predictive modeling, which can help clinicians foresee which wounds likely need advanced interventions. AI can additionally be used to provide real-time feedback to health care providers on the most effective treatment strategies. For example, some AI-based tools can use machine learning algorithms to analyze data from previous wound cases to recommend treatment plans that also consider a patient’s health history and data. The swiftness of this process can help decrease the risk of complications, resulting in improved patient outcomes.
Suchismita Das, health care and life sciences research analyst at Frost & Sullivan, notes that patients “increasingly prefer ‘at-home’ solutions, simple and effective wound monitoring devices, and solutions that require less intervention from clinicians.” Das adds that AI-based solutions, such as wearable devices that are sensor-based and wound-assessment devices, can assist health care providers provide accurate diagnoses of complex wounds that can aid in creating successful treatment plans.9
Research on AI in Chronic Wound Care
Because wound measurement is only one aspect of wound assessment, point-of-care devices can help measure such factors as perfusion and infections. Such devices enable information to be shared and accessed quickly through the use of mobile applications.
In “A Time Motion Study of Manual Versus Artificial Intelligence Methods for Wound Assessment,” researchers analyzed how much time medical providers spent on wound assessments with an AI tool vs manual approaches. In that study, the researchers employed a standard digital camera as part of the manual approach and concluded that AI was significantly faster in assessing wounds than the digital camera by an average of 62 seconds.10
A 2023 study focused on capturing wound images with a mobile device. Inexpensive devices were used to calculate the area and contours of wounds and to classify relevant tissues. The researchers concluded that “a smartphone gives sufficiently consistent results to be useful in clinical practice.”11 Further, the team discovered that using the two variables of determining a wound’s area and its classification of the tissues involved, health care providers could have a detailed view of the current state of the wound and how it could predictably change over time, helping them then determine healing time. “This variable is important for determining what factors predispose the healing process and whether a treatment is effective or not,” they reported.11
Other aspects of wound care researchers tested were tissue classification, finding that by being able to digitally “capture the amount of tissue that can be in a wound is a major breakthrough in wound management. The detection of necrotic tissue is, without a doubt, one of the most important functionalities to take into account in the healing process, as its presence determines whether or not to apply treatment.”11
Other research has been examining the role of AI in automating the process of detecting and classifying wounds such as pressure ulcers, and AI is proving beneficial for early detection and improving patient outcomes. Such technology could also enable health care providers to reallocate resources that are typically focused on wound care detection and classification.
Areas of Potential Growth
As funding for developing next-generation wound care solutions and the overall savings AI generates for health care are slated to increase, new opportunities are likely to emerge, including improved recommendations for preventative solutions, AI-enabled wound care solutions for faster and accurate diagnosis, and smart bandages with embedded sensors that offer real-time information on healing and medication delivery.
In 2020, University of California, Santa Cruz announced that researchers there, partnering with teams from the University of California, Davis, and Tufts University, received a contract of nearly $16 million, provided by the Defense Advanced Research Projects Agency, to develop AI-powered smart bandages. The team’s lead researcher, Marco Rolandi, a professor at the Baskin School of Engineering at University of California, Santa Cruz, said of the project, “It’s an interdisciplinary team with expertise ranging from bioelectronic devices and machine learning to clinical medicine. Wound healing problems affect many people, from veterans and firefighters to people with chronic diabetic ulcers, so it’s important to develop a new strategy to improve the treatment of hard-to-heal wounds.”13
Beyond direct care for patients, AI can benefit the field overall and promote education. According to an editorial by Douglas Queen, editor of the International Wound Journal, “Wound care, like any health care arena, is … perhaps one of the most data rich areas because of its interaction with or relationship to multiple human comorbidities. AI is certainly going to revolutionise [sic] this data management and aid with both assessment and treatment. … Generative AI, however, can help understand this complexity and provide ‘accurate’ summaries of data and outcomes. It can help provide summaries of individual and multiple studies, helping researchers not only understand the content but also provide the outputs to educate others.”14
AI also can be used increasingly in telemedicine and remote monitoring. Because telemedicine can allow clinicians to assess and treat wounds remotely, tools such as video conferencing can provide opportunities to communicate with patients and provide care. Through assessment and monitoring systems, algorithms can automatically measure wound size, depth, color, and other characteristics while also providing opportunities for real-time feedback to clinicians. Another example of remote monitoring is the WoundMatrix system, which enables patients to use a mobile app to capture images of wounds. AI algorithms are then used for wound assessment, healing time predictions, and treatment recommendations.
Although AI is not a replacement for provider intervention, the technology has been successfully integrated into health care treatment plans, benefiting patients and the field of chronic wound care as a whole. Existing technologies already have positively influenced patient care and health care education, as well as resource reallocation and cost reduction in health care. Additionally, past and emerging research demonstrates AI’s ongoing and evolving efficacy. The technology offers efficient ways for patients to work closely with their physicians to accurately assess, track, and treat chronic wounds and can continue to improve patients’ quality of life and ultimate outcomes.
— Susan Chapman, MA, MFA, is a Los Angeles–based freelance writer and editor.
2. Alam W, Hasson J, Reed M. Clinical approach to chronic wound management in older adults. J Am Geriatr Soc. 2021;69(8):2327-2334.
3. Frykberg RG, Banks J. Challenges in the treatment of chronic wounds. Adv Wound Care (New Rochelle). 2015;4(9):560-582.
4. Hoversten KP, Kiemele LJ, Stolp AM, Takahashi PY, Verdoorn BP. Prevention, diagnosis, and management of chronic wounds in older adults. Mayo Clin Proc. 2022;95(9):2021-2034.
5. Diabetic neuropathy. Mayo Clinic website. https://www.mayoclinic.org/diseases-conditions/
6. Centers for Medicare and Medicaid Services. Diabetes occurrence, costs, and access to care among Medicare beneficiaries aged 65 years and older. https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/MCBS/Downloads/Diabetes_DataBrief_2017.pdf. Published September 2017.
7. Volmer-Thole M, Lobmann R. Neuropathy and diabetic foot syndrome. Int J Mol Sci. 2016;17(6):917.
8. Wound assessment and management. The Royal Children’s Hospital Melbourne website. https://www.rch.org.au/rchcpg/hospital_clinical_guideline_index/Wound_assessment_and_
9. Frost & Sullivan. Artificial Intelligence to boost the global wound care market by 2026 with minimal intervention solutions. PRNewswire website. https://www.prnewswire.com/news-releases/artificial-intelligence-to-boost-the-global-wound-care-market-by-2026-with-minimal-intervention-solutions-301401695.html. Published October 18, 2021.
10. Mohammed HT, Bartlett RL, Babb D, Fraser RDJ, Mannion D. A time motion study of manual versus artificial intelligence methods for wound assessment. PloS One. 2022;17(7):e0271742.
11. Reifs D, Casanova-Lozano L, Reig-Bolaño R, Grau-Carrion S. Clinical validation of computer vision and artificial intelligence algorithms for wound measurement and tissue classification in wound care. Informatics in Medicine Unlocked. 2023;37:101185.
12. Sahni N, Stein G, Zemmel R, Cutler D. What happens when AI comes to healthcare. CEPR website. https://cepr.org/voxeu/columns/what-happens-when-ai-comes-healthcare#:~:text=
13. Stephens T. UC Santa Cruz leads collaboration to speed wound healing with a novel smart bandage. University of California, Santa Cruz website. https://news.ucsc.edu/2020/02/wound-healing.html. Published February 27, 2020.
14. Queen D. Could wound care benefit from the artificial intelligence storm taking place worldwide. Int Wound J. 2023;20(5):1337-1338.