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    Haptic Technologies to Support Spatial Cognition and Mobility in Visually Impaired People
    (Springer Nature Switzerland, 2026)
    Edwige Pissaloux
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    ;
    Simon Gay
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    On Spatial Cognition and Mobility Strategies
    (Springer Nature Switzerland, 2026)
    Edwige Pissaloux
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    ;
    Simon Gay
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    On the Design of New Assistive Mobility Devices to Enhance Spatial Cognition
    (Springer Nature Switzerland, 2026)
    E. Pissaloux
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    J. Nemargut
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    Perception-Action Loop and New Classification of Mobility Devices for Visually Impaired People
    (Springer Nature Switzerland, 2026)
    Edwige Pissaloux
    ;
    ;
    Simon Gay
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    MICAI and the Making of AI in Mexico Through 25 Years of Data-Driven Insight
    (Springer Nature Switzerland, 2025-10-20)
    Edgar Avalos-Gauna
    ;
    Leon Palafox
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    Risk Factors for Hypertension and Health Policy
    (Springer Nature Switzerland, 2025-10-20)
    Maximino Navarro Mentado
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    Vladimir Salazar Altamirano
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    Real-Time Emotion Recognition in Intelligent Tutoring Systems
    (Springer Nature Switzerland, 2025-10-20)
    Alcauter, Iara
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    Sphonic: Development of a Mobile Application Using AI and AR for Learning Biomedical Concepts
    (Springer Nature Switzerland, 2025)
    Escobar-Castillejos, Daisy
    ;
    Báez Gómez Tagle, Enrique Ulises
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    Chavarría-Reyes, Fernando Mauricio
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    Sigüenza Noriega, Iñaki
    ;
    Cruz-Ledesma, Iván
    This chapter covers the development of Sphonic, an iOS mobile application that combines artificial intelligence (AI) and augmented reality (AR) to improve biomedical education. Sphonic was designed to employ SwiftUI for its front end, while its back end is based on Django and hosted on Amazon Web Services. Sphonic intends to assist in explaining complex topics in chemistry and biology. Using Apple’s ARKit framework, Sphonic allows students to explore dynamic representations of DNA architecture, chemical bonding interactions, and biological processes to promote engagement and retention. On the other hand, the application features an OCR-based chemical equation solver, using Amazon Bedrock’s generative AI capabilities, that balances equations and offers comprehensive, step-by-step guidance, addressing conceptual deficiencies for learners. Additionally, Sphonic implements secure authentication protocols to protect user data and features a user-friendly interface that simplifies navigation. This chapter concludes by emphasizing the importance of AI and AR in modern education, demonstrating how these technologies could democratize access and understanding of scientific information and foster creativity in academic environments. ©The authors ©Srpinger.
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    Development of a Mobile Application for Dermatological Diagnosis Using Image Recognition: The DermAware Case Study
    (Springer Nature Switzerland, 2025)
    Cedillo-Maldonado, Luis
    ;
    Sigüenza-Noriega, Iñaki
    ;
    Miranda-Mateos, Sara Rocio
    ;
    Reinoso-Fuentes, Lorenzo
    ;
    Pérez-Aguirre, Mauricio
    This chapter discusses the development of DermAware, an iOS mobile application intended for assisting dermatological diagnosis through image recognition and machine learning. DermAware aims to serve as a supplementary resource for specialized medical professionals and patients, aiding the early identification and supervision of dermatological illnesses. The application was developed with SwiftUI for the front end and Django for the back end, providing scalability and secure data management. The application incorporates multiple components designed to enhance its usability. A real-time messaging module was designed to facilitate direct communication among users for prompt consultation scheduling. Health tracking functionalities, supported by Apple’s HealthKit, enable the collection and monitoring of patient data. The application integrates patient history management, allowing doctors to review previous assessments and track disease development conveniently. Ultimately, an authentication system guarantees data confidentiality and adherence to regulations. DermAware uses Apple’s CoreML framework alongside the ResNet50 convolutional neural network model to classify skin diseases. The system was trained using publicly accessible dermatological datasets, achieving an accuracy of 85% for detecting various skin conditions, including melanoma. The chapter finishes by addressing the technical challenges faced during development, evaluating potential enhancements, and discussing future developments in the field. These factors highlight the significance of AI-driven applications for improving medical diagnostics and healthcare accessibility. ©The authors ©Springer.