Artificial Intelligence (AI) is transforming medical imaging, especially pathology through deep learning. In low-resource settings like Burkina Faso, early breast cancer detection remains difficult due to limited infrastructure and specialists. This paper reviews AI methods for detecting breast cancer from histopathological images, analyzing 30/50 selected studies. Results show high performance of deep learning models, particularly CNNs, with sensitivity up to 95-100%. However, challenges persist in data availability, interpretability, and adaptation to local contexts. The study emphasizes the need for local datasets, capacity building, and adapted infrastructure.

In rural healthcare facilities in Burkina Faso, prenatal ultrasound examinations are often limited by the shortage of qualified professionals, requiring pregnant women to travel to urban centers for proper monitoring. These exams, though low-cost and simple, are essential for tracking pregnancy development. Additionally, paper-based health records often lead to lost or incomplete follow-up. To address these challenges, we implemented a digital solution based on MedShakeEHR, customized for pregnancy monitoring. The system introduces a unique pregnancy identifier, enables secure and centralized management of medical records, and organizes ultrasound imaging efficiently. Our approach improves continuity of care, facilitates remote consultations, and enhances decision-making for healthcare professionals. The proposed platform has the potential to increase access to quality prenatal care in rural Burkina Faso.

Medical and paramedical training is still a major issue in Africa, given the growing needs that contrast with the shortage of teachers, hospitals and educational facilities. The aim of the present work is to design an artificial intelligence that enables a virtual patient to hold a conversation with a doctor or a student. To achieve this, we first carried out a literature review on the various approaches to designing artificial intelligence of this type. Next, we selected the tools needed to develop the intelligent model. Finally, we developed and trained our intelligent model to hold a conversation with a healthcare professional during a medical consultation.

The introduction of the Licence-Master-Doctorate (LMD) system in Africa, particularly in Burkina Faso, has created challenges for medical training due to a lack of infrastructure and personnel. This study develops a custom GPT model for medical dialogue simulation, overcoming the limitations of a previous MLP model. A structured dataset was designed, grouping diseases into modules and defining patient profiles. An open-source GPT-2 model was modified and trained, with user interaction facilitated through an API. After initial training, the model showed promising results, indicating stable learning. This model offers better context management and customization suited for medical dialogues. Future improvements include expanding to other pathologies and optimizing performance for effective integration into medical training in Africa.

OBJECTIVE:To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance "precision in prevention and health surveillance" (2024 Yearbook topic). The focus is on leveraging advanced data analytics to improve the timeliness and accuracy of identifying emerging health concerns, thus enabling more proactive and effective health interventions. METHODS:A comprehensive literature search strategy was conducted on PubMed, focusing on papers published in 2023 related to consumer health informatics, precision prevention, and the intersection with social media. The search aimed to identify studies that utilized AI and machine learning techniques to analyse social media data for health surveillance purposes. Bibliometric analyses were applied to the retrieved articles, and tools such as "Bibliometrix" were used to assess keyword frequencies, co-occurrence networks, and thematic maps. The studies were then independently reviewed and screened for relevance, with a final selection of 10 articles made based on their alignment with the 2024 Yearbook topic and their methodological innovation. RESULTS:The analysis of 89 articles revealed several key themes and findings. Social media data offers a rich source of real-time insights into public health trends, and encompasses diverse demographic groups. AI methodologies, including machine learning, natural language processing (NLP), and deep learning, play a crucial role in extracting and analysing health-related information from social media content. The integration of AI in health surveillance can provide early warnings of potential health crises, as demonstrated by studies on topics such as suicide prevention, mental health, and the impact of social media use on e-cigarette consumption among youth. Ethical and privacy considerations are paramount, necessitating robust data anonymization and transparent data handling practices. Advanced AI techniques, such as transformer-based topic modelling and federated learning, enhance the precision and security of health surveillance systems. The document highlights several case studies that demonstrate the practical applications of AI in health surveillance, such as monitoring public discussions about delta-8 THC and assessing suicide-related tweets and their association with help-seeking behaviour in the US. CONCLUSION:Integrating AI and social media content analysis in precision prevention and health surveillance has significant potential to improve public health outcomes. By leveraging real-time, comprehensive data from social media platforms, AI can enhance the timeliness and accuracy of identifying health concerns. Addressing ethical and privacy challenges is crucial to ensure responsible and effective implementation. The continuous advancement of AI technologies will play a critical role in safeguarding public health and responding to emerging health threats.

With a view to designing a medical tele-education platform incorporating a teleconsultation simulator, our research initially involved designing a 3D model of a virtual patient. To do this, we first carried out a literature review of existing work and a review of the literature on 3D modeling and animation. Next, we made a selection of modeling and animation tools, materials and techniques. Finally, we drew up scenarios for the signs and symptoms of the diseases we selected for the animation of the character in our study. The result was a 3D model with a skeleton for animation.

The introduction of the Licence-Master-Doctorate (LMD) system in African higher education has significantly reshaped university organization, particularly in health-related fields, by exacerbating structural challenges such as the shortage of faculty and inadequate infrastructure. In this context, the present work aims to construct a structured dialogical corpus designed for the training of a customized GPT-2 model, with the goal of simulating medical consultations and supporting the training of medical students. The methodology combines the use of reliable medical sources, the controlled generation of dialogues using existing artificial intelligence systems, and role-playing exercises involving medical students, with detailed annotation of clinical, emotional, and behavioral metadata. The final corpus comprises over 36 million tokens for pre-training and more than 8,326 simulated dialogues for fine-tuning, covering the most prevalent pathologies in Burkina Faso. This multilingual and culturally contextualized approach represents a significant departure from dominant Western corpora, laying the groundwork for a medical conversational model adapted to African realities. While the model is still in training, the complete results will be presented at a later stage. Nevertheless, the collected data already constitute a valuable resource for the development of realistic, diverse, and reusable educational simulators across various medical training contexts.

The adaptation of a breast cancer detection platform based on artificial intelligence, designed for use on Android devices, is an initiative driven by the particular challenges faced in Africa, where access to computers is often limited due to their high cost and limited availability, a significant issue in Burkina Faso. It is especially crucial to find tailored and more efficient solutions for healthcare professionals in such environments. This mobile adaptation aims to make this advanced technology more accessible to healthcare professionals across the country, with mobile devices being far more common and accessible, with around 86% coverage in Burkina Faso. Our goal is to simplify the work of pathologists by enabling them to benefit from the advantages of AI for early and accurate breast cancer detection, directly from mobile devices, without requiring expensive infrastructure.

The accurate and efficient assignment of unique identifiers to biomedical specimens, along with the generation of barcodes for pathology samples, are crucial elements in ensuring the quality and reliability of medical diagnostics. In Burkina Faso, the adoption of an automated system for these tasks marks a significant advancement in laboratory management. This article explores the impact of this automation on reducing human errors, improving sample traceability, and optimizing operational processes. Indeed, the integration of AJAX queries for the dynamic management of specimen numbers allows for real-time updates and reduces the risks of duplication or incorrect assignment. Furthermore, the use of specialized libraries for the automatic generation of barcodes ensures a unique and secure identification of each sample, thereby facilitating its tracking throughout the analysis process. This modernization of sample management practices not only improves the efficiency of laboratories but also optimizes processing times, thus enhancing the quality of care and diagnostics provided to patients.

The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represents a significant advancement in the field of healthcare. Faced with the public health issue posed by breast cancer, this study focuses on the use of AI to improve early and accurate detection of this disease from histopathological images. For the implementation of the system, we utilized a customized architecture tailored to our context where image quality is low, based on the convolutional neural networks algorithm from the Keras library of TensorFlow. Subsequently, we developed a platform to facilitate its use. This article aims to present the methodology that was used and the results obtained.

Traditional medicine offers a wide range of application for in silico study techniques. This drug research and development strategy is embryonic in the West African context, particularly in Burkina Faso, which is increasingly faced with emerging diseases such as dengue fever. Circulation of the 4 serotypes of this virus has been documented in the country. This study aims to evaluate the therapeutic potential of phytocompounds contained in the West African pharmacopoeia against dengue virus NS2B/NS3 protein, using computational methods integrating several software packages and databases. Based on a literature review, we identified 191 molecules from 30 plants known for their antiviral effects. Five met the inclusion criteria for molecular docking: patulin from calotropis procera, resiniferonol from Euphorbia poissonii, Securinol A from Flueggea virosa, Shikimic acid and Methyl gallate from Terminalia macroptera. The best binding scores were observed between resiniferonol and the serotypes 1, 2 and 4 NS2B/NS3 protease, with binding energies of -7.4 Kcal/mol, -6.8 Kcal/mol and -7.3 Kcal/mol respectively; while the NS2B/NS3 protease of serotype 3 had the best affinity for securinol A (-7 Kcal/mol). This study points the way to further research in computer aided drug design field and calls for multidisciplinary collaboration to promote West African medicinal plants against health challenges.

This article discusses the revolution of medical education through immersive learning experiences in Virtual Reality (VR). It highlights the advantages, challenges, and future advancements of VR in medical education. The benefits of VR include immersive and interactive scenarios that help students understand complex medical concepts and empower personalized and self-directed learning. However, implementing VR in medical education is difficult due to technical challenges and potential physiological side effects. Despite these challenges, the growth of the VR industry offers hope for transforming medical education by closing gaps, increasing accessibility, and encouraging collaboration. The article emphasizes the need for collaboration among educators, researchers, and institutions to fully harness the transformative potential of VR in medical education. It concludes by stating that VR not only transforms medical education technologically but also changes how medical knowledge is acquired, internalized, and applied, creating a dynamic and immersive learning experience for future healthcare professionals.

BackgroundAccess to reliable and accurate digital health web-based resources is crucial. However, the lack of dedicated search engines for non-English languages, such as French, is a significant obstacle in this field. Thus, we developed and implemented a multilingual, multiterminology semantic search engine called Catalog and Index of Digital Health Teaching Resources (CIDHR). CIDHR is freely accessible to everyone, with a focus on French-speaking resources. CIDHR has been initiated to provide validated, high-quality content tailored to the specific needs of each user profile, be it students or professionals. ObjectiveThis study’s primary aim in developing and implementing the CIDHR is to improve knowledge sharing and spreading in digital health and health informatics and expand the health-related educational community, primarily French speaking but also in other languages. We intend to support the continuous development of initial (ie, bachelor level), advanced (ie, master and doctoral levels), and continuing training (ie, professionals and postgraduate levels) in digital health for health and social work fields. The main objective is to describe the development and implementation of CIDHR. The hypothesis guiding this research is that controlled vocabularies dedicated to medical informatics and digital health, such as the Medical Informatics Multilingual Ontology (MIMO) and the concepts structuring the French National Referential on Digital Health (FNRDH), to index digital health teaching and learning resources, are effectively increasing the availability and accessibility of these resources to medical students and other health care professionals. MethodsFirst, resource identification is processed by medical librarians from websites and scientific sources preselected and validated by domain experts and surveyed every week. Then, based on MIMO and FNRDH, the educational resources are indexed for each related knowledge domain. The same resources are also tagged with relevant academic and professional experience levels. Afterward, the indexed resources are shared with the digital health teaching and learning community. The last step consists of assessing CIDHR by obtaining informal feedback from users. ResultsResource identification and evaluation processes were executed by a dedicated team of medical librarians, aiming to collect and curate an extensive collection of digital health teaching and learning resources. The resources that successfully passed the evaluation process were promptly included in CIDHR. These resources were diligently indexed (with MIMO and FNRDH) and tagged for the study field and degree level. By October 2023, a total of 371 indexed resources were available on a dedicated portal. ConclusionsCIDHR is a multilingual digital health education semantic search engine and platform that aims to increase the accessibility of educational resources to the broader health care–related community. It focuses on making resources “findable,” “accessible,” “interoperable,” and “reusable” by using a one-stop shop portal approach. CIDHR has and will have an essential role in increasing digital health literacy.

This article explores the transition from a traditional histopathological examination system to an innovative platform using artificial intelligence (AI) for breast cancer detection from histopathological images in Burkina Faso. The existing system is analyzed in detail, highlighting the steps of querying, sample preparation, analysis by the pathologist, and validation by the physician. From this analysis, the needs and challenges are identified, emphasizing the opportunities for AI to improve the efficiency and accuracy of the diagnosis. The design of the AI platform is then presented, including data collection, AI model development, and its integration into existing processes. Finally, the expected results and implications for improving healthcare in Burkina Faso are discussed, highlighting the potential benefits and challenges to overcome for the successful adoption of this promising technology.

Artificial Intelligence (AI) has revolutionized many fields, including medical imaging. This revolution has enabled the digitization of medical images, the development of algorithms allowing the use of data captured in natural language, and deep learning, enabling the development of algorithms for automatic processing of medical images from massive medical data. In Burkina Faso, early and accurate detection of breast cancer is a significant challenge due to limited resources and lack of specialized expertise. In this article, we examine the effectiveness of different artificial intelligence algorithms for breast cancer detection from pathological image.

After having designed and implemented a telemedicine solution equipped with a video presence tool for teleconsultation and tele-expertise and in order to obtain a faithful communication between healthcare professional and patient despite language differences, our study was to perform a literary review on the various existing works and to perform analysis on the different types of neural network for designing an voice intelligent agent for translation during exchanges between doctor and patient during teleconsultation and make tool choices for its development.

Santé Numérique Rouen-Nice (SaNuRN; “Digital Health Rouen-Nice” in English) is a 5-year project by the University of Rouen Normandy (URN) and Côte d’Azur University (CAU) consortium to optimize digital health education for medical and paramedical students, professionals, and administrators. The project includes a skills framework, training modules, and teaching resources. In 2027, SaNuRN is expected to train a significant portion of the 400,000 health and paramedical students at the French national level. Our purpose is to give a synopsis of the SaNuRN initiative, emphasizing its novel educational methods and how they will enhance the delivery of digital health education. Our goals include showcasing SaNuRN as a comprehensive program consisting of a proficiency framework, instructional modules, and educational materials and explaining how SaNuRN is implemented in the participating academic institutions. SaNuRN is aimed at educating and training health and paramedical students in digital health. The project is a cooperative effort between URN and CAU, covering 4 French departments. It is based on the French National Referential on Digital Health ( FNRDH ), which defines the skills and competencies to be acquired and validated by every student in the health, paramedical, and social professions curricula. The SaNuRN team is currently adapting the existing URN and CAU syllabi to FNRDH and developing short-duration video capsules of 20-30 minutes to teach all the relevant material. The project aims to ensure that the largest student population earns the necessary skills, and it has developed a 2-tier system involving facilitators who will enable the efficient expansion of the project’s educational outreach and support the students in learning the needed material efficiently. With a focus on real-world scenarios and innovative teaching activities integrating telemedicine devices and virtual professionals, SaNuRN is committed to enabling continuous learning for health care professionals in clinical practice. The SaNuRN team introduced new ways of evaluating health care professionals by shifting from a knowledge-based to a competencies-based evaluation, aligning with the Miller teaching pyramid and using the Objective Structured Clinical Examination and Script Concordance Test in digital health education. Drawing on the expertise of URN, CAU, and their public health and digital research laboratories and partners, SaNuRN represents a platform for continuous innovation, including telemedicine training and living labs with virtual and interactive professional activities. SaNuRN provides a comprehensive, personalized, 30-hour training package for health and paramedical students, addressing all 70 FNRDH competencies. The project is enhanced using artificial intelligence and natural language processing to create virtual patients and professionals for digital health care simulation. SaNuRN teaching materials are open access. It collaborates with academic institutions worldwide to develop educational material on digital health in English and multilingual formats. SaNuRN offers a practical and persuasive training approach to meet the current digital health education requirements.

IntroductionNeurodevelopment and related mental disorders (NDDs) are one of the most frequent disabilities among young people. They have complex clinical phenotypes often associated with transnosographic dimensions, such as emotion dysregulation and executive dysfunction, that lead to adverse impacts in personal, social, academic, and occupational functioning. Strong overlap exists then across NDDs phenotypes that are challenging for diagnosis and therapeutic intervention. Recently, digital epidemiology uses the rapidly growing data streams from various devices to advance our understanding of health's and disorders' dynamics, both in individuals and the general population, once coupled with computational science. An alternative transdiagnostic approach using digital epidemiology may thus better help understanding brain functioning and hereby NDDs in the general population. ObjectiveThe EPIDIA4Kids study aims to propose and evaluate in children, a new transdiagnostic approach for brain functioning examination, combining AI-based multimodality biometry and clinical e-assessments on an unmodified tablet. We will examine this digital epidemiology approach in an ecological context through data-driven methods to characterize cognition, emotion, and behavior, and ultimately the potential of transdiagnostic models of NDDs for children in real-life practice. Methods and analysisThe EPIDIA4Kids is an uncontrolled open-label study. 786 participants will be recruited and enrolled if eligible: they are (1) aged 7 to 12 years and (2) are French speaker/reader; (3) have no severe intellectual deficiencies. Legal representative and children will complete online demographic, psychosocial and health assessments. During the same visit, children will perform additionally a paper/pencil neuro-assessments followed by a 30-min gamified assessment on a touch-screen tablet. Multi-stream data including questionnaires, video, audio, digit-tracking, will be collected, and the resulting multimodality biometrics will be generated using machine- and deep-learning algorithms. The trial will start in March 2023 and is expected to end by December 2024. DiscussionWe hypothesize that the biometrics and digital biomarkers will be capable of detecting early onset symptoms of neurodevelopment compared to paper-based screening while as or more accessible in real-life practice.