Artificial Intelligence for the Recognition of Diabetes-Related Foot Disease.
Diabetes Mellitus; Diabetes-Related Foot Disease; Artificial Intelligence; Clinical Podiatry Nursing; Primary Health Care; Digital Health.
Diabetes-related foot disease is one of the most severe chronic complications of Diabetes Mellitus, being
associated with increased rates of ulceration, infection, hospitalization, preventable amputations, and
mortality. Despite the rapid advancement of Artificial Intelligence (AI) in healthcare, digital technologies
designed to support prevention, risk stratification, and clinical decision-making in Primary Health Care remain
limited. This study aimed to produce validity evidence for the intelligent digital platform i9 Pé, developed to
support nurses in the clinical assessment and early recognition of diabetes-related foot disease. This
methodological study focused on technological development and validation, grounded in the principles of
translational research and Knowledge Translation, and was conducted in two stages. The first stage consisted
of a scoping review to identify scientific evidence on the application of Artificial Intelligence to diabetes-related
foot disease, supporting the refinement of the platform's clinical requirements, computational architecture, and
user interface. The second stage comprised the scientific validation of the technology, involving Clinical
Podiatry nurse specialists for content and interface validation, followed by Primary Health Care nurses who
evaluated the platform's applicability and usability. Data analysis included the Content Validity Index,
Cronbach's alpha, the System Usability Scale, descriptive and inferential statistics, and qualitative analysis of
participants' suggestions. Ethical aspects: the study was approved by the Research Ethics Committee of the
Federal University of Rio Grande do Norte and was conducted in accordance with Resolution No. 466/2012 of
the Brazilian National Health Council, ensuring participants' confidentiality, anonymity, and informed consent.
Main results demonstrated a global Content Validity Index of 0.91, a Cronbach's alpha of 0.93, and high
usability scores, indicating scientific consistency, reliability, and suitability of the platform for clinical practice.
Participants' recommendations contributed to improvements in the interface, navigation, and clinical content,
resulting in a more robust version of the technology. Conclusion: the i9 Pé platform provides validity evidence
to support nurses in the early recognition of diabetes-related foot disease, improving nursing consultations,
strengthening clinical decision-making, and contributing to the prevention of avoidable amputations. Work
outcomes include the consolidation of a digital health technology with potential for implementation in
healthcare services, the integration of Nursing, Software Engineering, and Artificial Intelligence, and the
generation of new research opportunities involving algorithm validation, remote monitoring, and digital health.
Social impact: the technology has the potential to expand access to specialized foot assessment within
Primary Health Care, promote early diagnosis, reduce diabetes-related complications, strengthen the role of
Clinical Podiatry Nursing, and improve the quality and sustainability of the Brazilian Unified Health System by
preventing avoidable amputations through evidence-based technological innovation.