Artificial intelligence (AI) is increasingly becoming part of the health care landscape, from clinical decision support and documentation to information retrieval and patient education. For wound care professionals, these tools may offer opportunities to work more efficiently and access relevant information at the point of care—but they also raise important questions about accuracy, reliability, and the role of clinical judgment.

In a recent episode of Wound Conversations, Catherine Milne, APRN, MSN, ANP/ACNS-BC, CWOCN-AP, spoke with Elaine Song, MD, PhD, MBA, about how AI is evolving in wound care and what clinicians should consider as they begin incorporating these tools into practice. Click here to listen to this and other episodes.

What is artificial intelligence in wound care?

AI in wound care can encompass several types of technology. Earlier “expert systems” largely relied on predetermined rules—essentially, if a user entered certain information, the system produced a corresponding output.

Machine learning and deep learning expanded those capabilities by allowing systems to learn from defined datasets. In wound care, for example, image databases may be used to train systems to recognize or classify tissue types, diabetic foot ulcers, or pressure injuries.

More recently, generative AI has introduced tools capable of retrieving, organizing, and generating information in response to questions or prompts.

Can clinicians trust AI-generated wound care information?

Not automatically.

One of the central cautions discussed in the podcast is the concept of “garbage in, garbage out.” An AI system's performance is influenced by the information used to train or inform it. If data are incorrectly labeled, incomplete, outdated, or insufficiently representative of different patient populations, the resulting output may also be unreliable.

Clinicians should therefore consider where an AI-generated answer comes from and whether supporting references are available rather than assuming that a confident-sounding response is correct.

Can AI accurately assess or classify wounds from photographs?

AI systems can be trained using databases of wound images, including images associated with tissue types and wound classifications. However, the podcast highlights important limitations.

The accuracy of these systems may depend on the quality and diversity of their training data. If an image was incorrectly labeled when it entered the dataset, for example, that error could affect what the system learns. Limited representation of different skin tones and variation in how clinicians classify wounds may create additional challenges.

For clinicians, an AI-generated classification should not substitute for a comprehensive patient and wound assessment.

How could AI help with wound care documentation?

Documentation is one area in which AI may have significant practical applications.

AI-enhanced systems can capture clinical conversations, organize information into documentation, and potentially generate relevant phrases or documentation templates based on what the clinician is recording.

AI may also assist with documentation audits by helping clinicians determine whether their records align with current coverage policies and documentation requirements. This could be particularly useful when requirements are extensive or change over time.

Can AI provide clinical information at the point of care?

Yes. One application discussed in the podcast involves combining AI with a defined, vetted knowledge base.

Rather than searching through multiple resources manually, a clinician could ask a question and receive information retrieved from an established set of clinical resources. The clinician could then access the underlying reference or clinical algorithm to review the information in greater detail.

This approach illustrates an important distinction: AI can be used not simply to generate an answer, but to help clinicians retrieve relevant information more efficiently.

Could AI help educate patients about wound care?

Potentially. AI can be used to support patient-specific education and make information more accessible.

However, the same concerns about reliability apply. Patients may encounter AI-generated health information independently and assume that an authoritative-sounding answer is accurate. Clinicians may increasingly need to help patients evaluate this information, correct misinformation, and understand which recommendations are supported by evidence.

Will AI replace wound care clinicians?

The perspective presented in the podcast is no.

AI may complement clinical decision-making and improve efficiency, but wound care involves human observations and contextual information that technology may not recognize. A clinician may notice, for example, that a patient's facial expression suggests pain even when the patient's words suggest otherwise.

Those observations, combined with clinical experience, assessment findings, and knowledge of the individual patient, remain critical to decision-making.

How should wound care clinicians approach AI today?

AI may be most useful when clinicians view it as a tool rather than an authority.

It can potentially help clinicians retrieve information, learn, document more efficiently, educate patients, and identify gaps in documentation. But clinicians still need to evaluate the information presented, determine whether the source is reliable, and apply their own judgment to the individual patient.

As Song emphasizes in the conversation, clinicians—not AI—should remain the ones guiding decisions. For wound care professionals, the opportunity may therefore be less about allowing AI to make decisions and more about learning how to use reliable AI tools to become more informed and efficient clinicians.

Want to hear the full conversation? Listen to this episode of Wound Conversations for Milne and Song's discussion of the evolution of AI, its emerging applications in wound care, and why human clinical judgment remains essential.