The role of artificial intelligence in wound care: applications, evidence and future directions.
Artificial intelligence (AI) has the potential to transform wound care by addressing inconsistencies in assessment, clinical inefficiencies, and alleviating resource constraints in a speciality that imposes significant economic burden on healthcare systems. This article explores AI's applications, e...
| Publicado en: | Wounds International Vol. 16; no. 3; pp. 30 - 44 |
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| Autor principal: | |
| Formato: | pictorial review tables/charts Journal Article |
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SB Communications Group, A Schofield Media Company
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189074082&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189074082 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20440049 BJI4 jtl: Wounds International issn: 20440049 maglogo: N pubinfo: dt: Oct2025 vid: 16 iid: 3 pid: 13783 pub: SB Communications Group, A Schofield Media Company artinfo: ui: 189074082 189074082 189074082 189074082 ppf: 30 ppct: 14 formats: fmt: @attributes: type: P tig: atl: The role of artificial intelligence in wound care: applications, evidence and future directions. aug: au: Chadwick, Paul affil: Professor of Wound Repair and Regeneration, Birmingham sug: subj: Wound Care Artificial Intelligence Machine Learning Deep Learning Natural Language Processing Early Diagnosis Skin Pigmentation Guideline Adherence Patient Safety Ethics, Medical Predictive Validity Sensitivity and Specificity Documentation Workforce Health Care Delivery Telehealth ab: Artificial intelligence (AI) has the potential to transform wound care by addressing inconsistencies in assessment, clinical inefficiencies, and alleviating resource constraints in a speciality that imposes significant economic burden on healthcare systems. This article explores AI's applications, evidence and future directions in wound management. It reviews core AI methodologies -- machine learning, deep learning, and natural language processing -- and how they are driving innovations including computer vision for wound imaging, predictive analytics for healing trajectories, and smart dressings for real-time monitoring. These technologies can enhance diagnostic accuracy, standardise assessments, and enable early detection of complications, supporting personalised treatment strategies. With this prospective step change in our approach to wound care, challenges persist, including infrastructure needs, data privacy concerns, bias in AI imaging with different skin tones, workforce training requirements, and financial investment barriers. Successful integration requires alignment with clinical workflows, adherence to ethical standards, and unwavering focus on patient safety. It is crucial that AI is designed and seen to augment rather than replace clinical expertise, highlighting the need for ethical governance and ongoing evaluation. By balancing technological innovation with clinical excellence, AI can enhance patient outcomes, optimise resource allocation, and maintain high standards in wound care. Realising AI's full potential will depend on collaboration among clinicians, researchers, and policymakers to build resilient, patient-centred healthcare systems. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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