Introduction
Artificial Intelligence (AI) is revolutionizing healthcare, offering unprecedented capabilities that were once confined to the realm of science fiction. As we enter the third epoch of AI, known as the era of generative AI, these advancements bring both extraordinary potential and significant challenges. In wound care, AI is being leveraged to improve clinical decision-making, providing clinicians with faster and more accurate access to critical information. The responsible use of AI in wound care is not just a matter of enhancing efficiency; it is about ensuring that AI serves as a complement to human judgment, not a replacement.
The Evolution of AI in Healthcare
AI in healthcare has undergone significant transformations, marked by three distinct epochs. The first epoch focused on rule-based systems, where AI was programmed with predefined rules to assist in clinical decision-making. While these systems were useful, they were limited by their inability to adapt to new information or learn from experience.
The second epoch introduced machine learning, where AI systems could learn from data and improve their performance over time. This era saw the development of more sophisticated diagnostic tools, predictive analytics, and personalized treatment plans. However, these systems were still primarily data-driven, with limited understanding of context or the nuances of human judgment.
Today, we are in the third epoch of AI, characterized by generative AI. This era has brought new capabilities, such as the ability to generate human-like text, images, and even entire treatment plans. However, it also introduces new risks, including the phenomenon of AI "hallucinations," where the system generates incorrect or fabricated information. These risks highlight the importance of responsible AI use, particularly in healthcare settings where patient outcomes are at stake.
AI in Wound Care: Enhancing Clinical Decision-Making
Wound care is a complex and multifaceted field, requiring clinicians to make decisions based on a wide range of factors, including wound type, patient history, and available treatments. The introduction of AI into this field has the potential to transform how clinicians approach wound management.
One of the primary benefits of AI in wound care is its ability to enhance clinical decision-making by providing quick access to evidence-based information. AI-powered tools can search through a vast amount of data, and present clinicians with the most relevant information for a given case. This capability can significantly reduce the time spent searching for answers, allowing clinicians to focus on patient care.
The Importance of Responsible AI Use in Wound Care
While AI offers many benefits, it is essential to recognize that it is not infallible. The potential for AI to generate incorrect or misleading information—known as AI hallucinations—poses a significant risk in healthcare.
Responsible AI use in wound care requires a robust framework that ensures AI tools are accurate, reliable, and transparent. This includes rigorous testing and validation of AI models, continuous monitoring of their performance, and clear guidelines for their use. It also involves educating clinicians about the limitations of AI and the importance of using it as a tool to support, not replace, their judgment.
Wound Reference Pioneering Responsible AI in Wound Care and Hyperbaric Medicine
To address these issues and help clinicians find reliable answers in wound care, WoundReference developed myWAI - Wound AI, a responsible, AI-powered clinical intelligence solution.
myWAI leverages the latest AI models to quickly access and retrieve relevant information from WoundReference, which has been edited and vetted by our editorial team under strict non-biased editorial processes. As a result, we are able to quickly provide clinicians with responsible, evidence-based answers to clinical and reimbursement answers in wound care and hyperbaric medicine. By aggregating data from its continually updated and evidence-based knowledge base, Wound AI delivers evidence-based, referenced information that clinicians can use to inform their decisions. This tool has been particularly valuable in helping clinicians navigate the complexities of wound care, where timely and accurate information is critical.
myWAI provides a solid example of how responsible AI can be implemented in wound care and hyperbaric medicine. In developing myWAI, we followed a meticulous process to identify and leverage the most adequate AI models. This process included multiple phases of testing and evaluation, where different AI models were assessed based on their ability to provide accurate answers to wound care and hyperbaric medicine-related questions.
Through this rigorous evaluation process, WoundReference was able to identify the AI model with the highest accuracy and reliability. This model was then further trained and calibrated until it achieved a 100% accuracy score. In addition, our editorial team created a library of frequently asked questions structured in a way to retrieve most accurate replies (i.e. prompts), to ensure that the information provided by Wound AI when using the prompts is both accurate and trustworthy.
Balancing AI Innovation with Human Judgment
While AI can significantly enhance clinical decision-making, it is crucial to maintain the balance between technological innovation and human judgment. AI should be viewed as a tool that complements the expertise of clinicians, not as a replacement for it.
One of the key challenges in integrating AI into clinical practice is ensuring that clinicians remain the ultimate decision-makers. AI can provide valuable insights and recommendations, but the final decision should always rest with the clinician, who can consider the broader context of the patient's condition, preferences, and unique circumstances.
Moreover, AI should be used to enhance, not undermine, the patient-clinician relationship. In wound care, where trust and communication are critical, it is essential that AI tools are used in a way that supports, rather than disrupts, this relationship. For example, AI can be used to provide clinicians with additional information that they can then discuss with the patient, ensuring that the patient is fully informed and involved in their care.
Conclusion
The integration of AI into wound care holds tremendous promise, offering the potential to enhance clinical decision-making and improve patient outcomes. However, this promise can only be realized if AI is used responsibly. The development process of WoundReference’s myWAI - Wound AI illustrates the importance of rigorous testing, continuous monitoring, and clear guidelines in developing and implementing AI tools in wound care.
It is also essential to remember that these tools are meant to support, not replace, human judgment.
References
- Howell MD, Corrado GS, DeSalvo KB. Three epochs of artificial intelligence in health care. JAMA. 2024 Jan 16;331(3):242–4.
- I’m an ER doctor, here’s how I’m using ChatGP to help treat patients [Internet]. [cited 2023 Jun 4]. Available from: https://www.fastcompany.com/90895618/how-a-doctor-uses-chat-gpt-to-treat-patients
- ChatGPT in the emergency room? The AI software doesn’t stack up [Internet]. [cited 2024 May 31]. Available from: https://www.fastcompany.com/90863983/chatgpt-medical-diagnosis-emergency-room
- Ferreira FK, Song EH, Gomes H, Garcia EB, Ferreira LM. New mindset in scientific method in the health field: Design Thinking. Clinics. 2015 Dec 10;70(12):770–2.
Resources
About the Authors
Elaine Horibe Song, MD, PhD, MBA
Dr. Song is a Co-Founder and Chief Executive Officer of WoundReference, Inc., a clinical and reimbursement decision support & telemedicine platform for wound care and hyperbaric clinicians. With a medical, science and business background, Dr. Song previously served as medical director for a regenerative medicine-focused biotech company in California, and for a Joint Commission International-accredited hospital network. Dr. Song also served as a management consultant for Kaiser Permanente, practiced as a plastic surgeon in private practice and academia, and conducted bench and clinical research in wound healing, microsurgery and transplant immunology. Dr. Song holds a position as Affiliate Professor, Division of Plastic Surgery, Federal University of Sao Paulo, and is a volunteer, Committee Chair of the Association for the Advancement of Wound Care. She has authored more than 100 scientific publications, book chapters, software registrations and patents.
Catherine Milne, APRN, MSN, BC-ANP/CS, CWOCN-AP
Catherine Milne is an Advanced Practice WOC Nurse providing care to patients across the continuum in acute care, long-term care, home health and outpatient settings. Employed by Connecticut Clinical Nursing Associates, she also provides consulting to organizations wishing to improve wound outcomes, conducts clinical research, lectures nationally and internationally. Cathy provides clinical support to WoundReference.com and serves as a volunteer Board Member for WhyWoundCare.com. Cathy is the Co-Editor of the text Wound, Ostomy, Continence Secrets and Clinical Editor of WoundSource. Additionally, she serves as a Clinical Instructor at the Yale School of Nursing. As an active member of the Association for the Advancement of Wound Care, she recently served as a Nurse Board Member.
Ana Carolina Lucchese,
Ana Carolina Lucchese serves as Marketing & Communications Lead at WoundReference. She holds a background in engineering and business, with a diploma from Harvard University. With extensive experience in the technology and health sectors, Ana has held positions at major corporations like Microsoft. Additionally, she has provided valuable guidance to healthtech startups, assisting in the development of business plans and the execution of marketing strategies.
