Laboratory medicine 2050: from testing to health intelligence network, a vision from the IFCC Emerging Technologies Division

ARTIFICIAL INTELLIGENCE IN MODERN MEDICINE: A COMPREHENSIVE REVIEW OF CURRENT APPLICATIONS, CHALLENGES, AND FUTURE PERSPECTIVES

Introduction: The digital transformation of healthcare is accelerating, driven by unprecedented advancements in Artificial Intelligence (AI). From large language models (LLMs) to biomolecular structure prediction, AI is redefining modern diagnostic and therapeutic standards. Aim: This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation. Materials and Methods: A structured analysis was conducted utilizing a hybrid approach that combines a multi-decade bibliometric trend perspective with a detailed synthesis of 21 landmark publications, clinical trials, and meta-analyses from high-impact journals. Results: AI demonstrates expert-level performance in medical knowledge retrieval and spatiotemporal diagnostics. AlphaFold 3 has revolutionized computational therapeutics through all-atom biomolecular interaction prediction, while ambient AI scribes significantly reduce physician burnout by automating clinical documentation workflows. However, data-driven "hallucinations" in LLMs and the inherent "black box" nature of deep learning architectures remain critical barriers to autonomous deployment. Conclusions: AI is successfully transitioning from an isolated research tool into an essential clinical "co-pilot." Achieving its full potential in Medicine 4.0 requires robust frameworks for algorithmic explainability, global dataset diversification, and a strategic synergy between machine precision and human clinical judgment.

The evolving landscape of laboratory medicine: A look into the future

Laboratory medicine is undergoing a radical transformation, driven by sustained technological innovation, shifting workforce demographics, increasing diagnostic complexity, and mounting systemic pressures within healthcare delivery systems. Contemporary clinical laboratories have progressively transitioned from predominantly technical service units into highly integrated, data-intensive clinical hubs that generate and interpret information central to a large proportion of medical decisions. In parallel, persistent shortages of qualified personnel, escalating administrative and regulatory demands, growing cybersecurity vulnerabilities, and the rapid integration of automation and artificial intelligence (AI) are redefining professional roles, required competencies, and organisational structures. This personal overview aims to summarise the leading structural and systemic trends currently reshaping laboratory medicine, including workforce sustainability and training gaps, the continuous expansion and stratification of diagnostic testing, the evolving burden of infectious diseases and antimicrobial resistance, and the accelerating implementation of automation and AI-supported diagnostic workflows. It further addresses emerging risks such as cyberattacks targeting healthcare infrastructure and the progressive administrative overload affecting laboratory activities. Collectively, these factors highlight the need for adaptive governance models, resilient laboratory systems, and reinforced clinical orientation of laboratory professionals.

Innovative Technologies

Bridging gaps in health artificial intelligence: challenges in MDPI research articles

This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer to highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics.

AI-Enabled Smart Healthcare Systems: Innovative Approaches to Disease Prediction, Accurate Diagnosis, and Effective Public Health Management

It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve.

Artificial intelligence-based software as a medical device: regulatory evolution toward continuous oversight

This structured narrative review examines how AI-SaMD regulation is moving beyond single-point premarket evaluation toward continuous and dynamic oversight, and synthesizes five evidence dimensions central to ongoing regulatory assurance: data evidence, algorithm evidence, software and cybersecurity evidence, clinical scenario and human factors evidence, and real-world data/real-world evidence with change management.