VOLUME 18 NUMBER 2 (July to December 2025)

Integrating artificial intelligence and machine learning into modern health information, One Health, and bioengineering ecosystems: Advances and future directions

Artificial intelligence (AI) and machine learning (ML) are reshaping health information and bioengineering ecosystems across human, veterinary, and environmental domains. Beyond streamlining diagnostics and treatment, these technologies are driving advances in precision medicine, regenerative medicine, drug delivery, and digital health, transforming them into more adaptive and predictive systems. This article provides a comprehensive perspective and prospective review of recent breakthroughs in AI and ML. It includes AI-driven diagnostics, telemedicine platforms, dentistry, One Health surveillance tools, and innovations in bioengineering. A thematic synthesis of innovations highlights the growing importance of interoperability, clinical adaptability, and equitable access. Central to these developments is the emerging concept of meta-AI frameworks, supported by model context protocols (MCP), which can orchestrate multiple AI tools into responsive, context-aware systems. Such integration promises not only efficiency but resilience in managing complex health challenges. This article outlines the gaps and challenges posed by advances in AI systems, particularly in resource-limited settings in most developing countries and proposes solutions. From a prospective standpoint, we argue that the next decade will see health information and bioengineering ecosystems evolve into proactive decision-support environments, capable of anticipating risks and guiding personalized interventions. This convergence of AI/ML with bioengineering and One Health strategies positions intelligent informatics as both a present innovation and a cornerstone for future global health security.