Teleconsultation in Japan was initially introduced as a doctor-to-doctor or dentist-to-dentist (D-to-D) service, restricted to non-emergency, follow-up consultations for patients already under medical care and in stable condition. Following the onset of COVID-19 in February 2020 and the resulting restrictions on international travel, demand increased not only for first-time doctor-to-patient (D-to-P) consultations within Japan but also for cross-border teleconsultations. This study aimed to examine the regulatory evolution and implementation challenges of cross-border teleconsultation in Japan. While cross-border teleconsultation offers accessible and convenient care for international patients, broader implementation remains limited due to challenges such as inadequate international patient support systems and the absence of consistent international legal frameworks. The findings highlight the need to develop standardized international telemedicine frameworks and to establish robust communication channels among governments.

　Artificial intelligence (AI) is rapidly transforming internal medicine by enhancing diagnostic accuracy, enabling personalized treatment, and optimizing patient management. As of August 2024, the U.S. Food and Drug Administration has authorized nearly 950 AI/ML-enabled medical devices, while an American Medical Association survey reported that 66% of physicians already incorporate AI into clinical practice. This review provides a comprehensive overview of AI’s expanding role across internal medicine, highlighting its applications in medical interviews, text-based communication, and the interpretation of core diagnostic modalities such as electrocardiography, chest X-ray, and auscultation. While several FDA-approved AI tools are already integrated into clinical workflows, many technologies remain at the research or proof-of-concept stage, with validation often limited to retrospective or controlled trial settings.

　The transformative potential of AI is particularly relevant in urban healthcare, where population density, limited resources, and disproportionate burdens of chronic and lifestyle-related diseases underscore the need for innovative solutions. AI can mitigate physician shortages, streamline care in overburdened systems, and support equitable access to diagnostics and treatment in metropolitan areas. Key technologies, including machine learning, deep learning, and large language models, are critically examined, along with emerging innovations such as EHR-based foundation models.

　Despite its promise, AI integration raises ethical, legal, social, and regulatory challenges, including algorithmic bias, data privacy, validation standards, and workforce adaptation. This paper explores these multifaceted aspects, emphasizing the importance of collaborative efforts to ensure responsible and equitable implementation, ultimately aiming to improve patient outcomes and public health in the digital era.

　Heat-related illness (HRI) represents a growing public health challenge in the context of global warming. Ranging from mild symptoms such as heat cramps to life-threatening heatstroke, HRI requires prompt recognition and appropriate management to reduce morbidity and mortality. The diagnosis of HRI is primarily based on clinical symptoms but is supported by laboratory evaluation, especially in severe cases. Heatstroke, the most critical form, is defined by core body temperature ≥ 40°C and central nervous system dysfunction. In addition to conventional laboratory markers, recent advances in molecular biology have identified several biomarkers, such as heat shock proteins, HMGB1 (High Mobility Group Box 1), and mitochondrial DNA, which may improve early and accurate detection and prognostication. This review summarizes the diagnostic approach to HRI, highlighting key clinical findings, laboratory assessments, and emerging molecular markers.