Over the years, the healthcare community has witnessed many improvements in methods and technologies used in healthcare delivery, including mHealth as an emerging area of healthcare applications to improve access to health services. However, challenges involved in implementing mHealth to optimal advantage do exist. In this article, we identify some of the most important challenges and propose feasible solutions.

This study explores how patients and stakeholders envision integrated digital health systems. Background/Objectives: Integrating artificial intelligence (AI), wearable data, electronic health records (EHRs), and patient-reported outcomes could enable proactive and personalized healthcare. However, current solutions remain fragmented and poorly aligned with user expectations. This study aimed to explore patient and stakeholder needs for AI-driven integration and propose a conceptual framework to inform future system design. Methods: As part of the NSF Innovation Corps (I-Corps) program, we conducted semi-structured interviews with 44 participants representing Health Enthusiasts, Chronic Condition Managers, and Low-Engagement Users. Interviews followed the I-Corps customer discovery framework and were thematically analyzed using a hybrid deductive–inductive approach. Results: Participants highlighted four priorities: (i) interoperability and unification of data from wearables, EHRs, and self-reports; (ii) actionable personalization with predictive insights; (iii) trust and transparency in AI recommendations, often requiring clinician oversight; and (iv) usability through low-friction, intuitive interfaces. Age- and persona-specific differences emerged: younger participants favoring predictive features and older participants emphasizing safety, reassurance, and clinical integration. Conclusions: This exploratory qualitative study identified stakeholder needs that informed a conceptual framework for integrated digital health platforms. While preliminary, the framework provides a blueprint for future technical development and validation of patient- and provider-centered systems.

A brief analysis of challenges in implementing telehealth in a rural setting Varadraj P. Gurupur, Zhuqi Miao

The objective of this article is to discuss the inherent bias involved with artificial intelligence-based decision support systems for healthcare. In this article, the authors describe some relevant work published in this area. A proposed overview of solutions is also presented. The authors believe that the information presented in this article will enhance the readers’ understanding of this inherent bias and add to the discussion on this topic. Finally, the authors discuss an overview of the need to implement transdisciplinary solutions that can be used to mitigate this bias.

Background: Dentistry is undergoing a digital transformation driven by emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins (DTs), and Large Language Models (LLMs). These advancements offer new paradigms in clinical diagnostics, patient monitoring, treatment planning, and medical education. However, integrating these technologies also raises critical questions around security, privacy, ethics, and trust. Objective: This review aims to provide a structured synthesis of the recent literature exploring AI, IoT, DTs, and LLMs in dentistry, with a specific focus on their application domains and the associated ethical, privacy, and security concerns. Methods: A comprehensive literature search was conducted across PubMed, IEEE Xplore, and SpringerLink using a custom Boolean query string targeting publications from 2020 to 2025. Articles were screened based on defined inclusion and exclusion criteria. In total, 146 peer-reviewed articles and 18 technology platforms were selected. Each article was critically evaluated and categorized by technology domain, application type, evaluation metrics, and ethical considerations. Results: AI-based diagnostic systems and LLM-driven patient support tools were the most prominent technologies, primarily applied in image analysis, decision-making, and health communication. While numerous studies reported high performance, significant methodological gaps exist in evaluation design, sample size, and real-world validation. Ethical and privacy concerns were mentioned frequently, but were substantively addressed in only a few works. Notably, IoT and Digital Twin implementations remained largely conceptual or in pilot stages, highlighting a technology gap in dental deployment. Conclusions: The review identifies significant potential for converged intelligent dental systems but also reveals gaps in integration, security, ethical frameworks, and clinical validation. Future work must prioritize cross-disciplinary development, transparency, and regulatory alignment to realize responsible and patient-centered digital transformation in dentistry.