Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study’s primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.

Human milk is the most important way to feed and protect newborns as it has the components to ensure human health. Human Milk Banks (HMBs) form a network that offers essential services to ensure that newborns and mothers can take advantage of the benefits of human milk. Despite this, there is low adherence to exclusive breastfeeding in Brazil, and human milk stocks available in HMBs are usually below demand. This study aimed to co-develop a smart conversational agent (Lhia chatbot) for breastfeeding education and human milk donor recruitment for HMBs. The co-design methodology was carried out with health professionals from the HMB of the University Hospital of the Federal University of Maranhão (HMB-UHFUMA). Five natural language processing pipelines based on deep learning were trained to classify different user intents. During the rounds in the co-design procedure, improvements were made in the content and structure of the conversational flow, and the data produced were used in subsequent training sessions of pipelines. The best-performing pipeline achieved an accuracy of 93%, with a fallback index of 15% for 1851 interactions. In addition, the conversational flow improved, reaching 2904 responses given by the chatbot during the last co-design round. The pipeline with the best performance and the most improved conversational flow were deployed in the Lhia chatbot to be put into production.

The increasing integration of Artificial Intelligence (AI) into dermatology holds significant potential to enhance the diagnosis and prognosis of skin diseases, which represent a considerable public health burden. While numerous AI models for skin conditions have been developed, their successful translation into real-world clinical practice critically depends on rigorous external validation to ensure generalizability and reliability beyond their development environment. Despite the growing recognition of external validation’s importance in medical AI, a comprehensive synthesis specifically addressing this process for AI models in dermatology has been lacking. This systematic literature review aimed to identify and synthesize studies focused exclusively on the external validation of AI models for skin diseases, thereby assessing their clinical applicability, methodological robustness, and identifying key challenges. Following the PRISMA framework, a systematic search was conducted across five major digital libraries (ACM Digital Library, IEEE Xplore, Web of Science, PubMed, and Scopus). Study quality was assessed using a customized checklist, and data extraction focused on validation practices, model characteristics, targeted diseases, performance assessment, and identified limitations. The systematic study selection led to a detailed analysis of 41 studies. We observed a clear predominance of retrospective external validation studies focused on diagnostic models, while prognostic models and prospective validation designs were notably scarce. Although some models demonstrated promising performance in external validation, several critical limitations persist. These include the narrow representation of skin disease categories, lack of subgroup fairness analyses, small and imbalanced sample sizes, and insufficient standardization of data collection and evaluation metrics. These findings underscore the need for more rigorous and transparent validation frameworks to support the reliable integration of AI into dermatological practice.

Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the NLMeasurer mobile application. The paired t-test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the MoveNet Thunder INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were <10° in 8 of 10 analyzed movements. HPE models demonstrated better performance in shoulder flexion and abduction movements while exhibiting unsatisfactory performance in elbow flexion. Challenges such as image perspective distortion, environmental lighting conditions, images in monocular view, and complications in the pose may influence the models’ performance. Nevertheless, HPE models show promise in identifying KJPs and facilitating ROM measurements, potentially enhancing convenience and efficiency in assessments. However, their current accuracy for this application is unsatisfactory, highlighting the need for caution when considering automated upper limb ROM measurement with them. The implementation of these models in clinical practice does not diminish the crucial role of examiners in carefully inspecting images and making adjustments to ensure measurement reliability.

IntroduçãoA hanseníase é uma enfermidade infectocontagiosa que pode gerar desde a incapacidade as sequelas físicas quando não tratada precocemente.Objetivo Descrever o perfil epidemiológico da Hanseníase em Bacabal no estado do Maranhão. Métodos Trata-se de um estudo transversal, retrospectiva com abordagem quantitativa, sobre casos de hanseníase registrados no Sistema de Informação de Agravos de Notificação no período de 2008 a 2017 sendo analisados por estatística descritiva. Resultados Foram diagnosticados 1.309 casos, na faixa etária de 20 a 34 anos (25,7%), sexo masculino (60,2%), raça/pretos (39,8%), com ensino fundamental (63,5%), ocupação de agropecuários (32,3%), atenção básica notificou a maioria dos casos (49,9%) e residiam na zona urbana (78,7%). Verificou-se que a classificação operacional predominante foi em casos Multibacilar (76%), na forma clínica dimorfa (60,9%), múltiplas lesões de 2 a 5 (33%), sem nervos afetados (60,6%) e os pacientes não tiveram nenhuma incapacidade física (45,5%). Apenas 43% dos casos realizaram a baciloscopia e a poliquimioterapia Multibacilar com 12 doses predominou com 75,8% nos casos diagnosticados. A maioria dos casos eram classificados como novos (77,8%), foram detectados por demanda espontânea (38,5%) e mais da metade foram curados (80,3%). Conclusão A hanseníase é uma doença endêmica na região se configurando um problema saúde pública devido a elevada magnitude dos casos. Ressalta-se a importância da realização de atividades educativas com enfoque na prevenção através da busca ativa para o diagnóstico precoce; no intuito de rastrear casos e contatos na comunidade a fim de reduzir as sequelas físicas sendo um fator determinante para o enfrentamento da doença.