Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making - A systematic review.

Introduction: Advance care planning is a critical process that brings patients, their families, and healthcare providers together to set goals and outline preferences for future medical treatments, especially when chronic or terminal illnesses are involved. Recently, artificial intelligence has begun playing a key role in shared decision making, offering personalized recommendations based on detailed data analysis to help refine treatment decisions.

Objective: This review explores Artificial Intelligence's role in shared decision making, noting its potential to enhance treatment precision, reduce the workload for healthcare providers, and empower patients to engage more actively in their cares.

From Blame to Learning: The Evolution of the London Protocol for Patient Safety.

Over the past two decades, patient safety and clinical risk management have become strategic priorities for healthcare systems worldwide. In this context, the London Protocol has emerged as one of the most influential methodologies for investigating adverse events through a systemic, non-punitive lens. The 2024 edition, curated by Vincent, Adams, Bellandi, and colleagues, represents a significant evolution of the original 2004 framework.

Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective.

Introduction: The evolution of Laboratory Medicine (LM) has expanded diagnostic and therapeutic horizons, yet ethical concerns pervade its advancements. This article explores the ethical challenges inherent in LM and advocates for a personalist bioethical framework as a guiding principle. It addresses issues such as patient data privacy, healthcare equity, communication of complex analysis results, genetic information management, and conflicts of interest.

Integrating enterprise risk management to address AI-related risks in healthcare: Strategies for effective risk mitigation and implementation.

The incorporation of artificial intelligence (AI) in health care offers revolutionary enhancements in patient diagnostics, clinical processes, and overall access to services. Nevertheless, this technological transition brings forth various new, intricate risks that pose challenges to current safety and ethical norms. This research explores the ability of enterprise risk management as an all-encompassing framework to tackle these arising risks, providing both a forward-looking and responsive strategy designed for the health care industry.

Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.

Introduction: Adverse events in hospitals significantly compromise patient safety and trust in healthcare systems, with medical errors being a leading cause of death globally. Despite efforts to reduce these errors, reporting remains low, and effective system changes are rare. This systematic review explores the potential of artificial intelligence (AI) in clinical risk management.

From the Operating Theater to the Pathology Laboratory: Failure Mode, Effects, and Criticality Analysis of the Biological Samples Transfer.

: The frozen section intra-operative consultation is a pathology procedure that provides real-time evaluations of tissue samples during surgery, enabling quick and informed decisions. In the pre-analytical phase, errors related to sample collection, transport, and identification are common, and tools like failure mode, effects, and criticality analysis help identify and prevent risks. This study aims to enhance patient safety and diagnostic quality by analyzing risks and optimizing sample management.

Robotics and AI into healthcare from the perspective of European regulation: who is responsible for medical malpractice?

The integration of robotics and artificial intelligence into medical practice is radically revolutionising patient care. This fusion of advanced technologies with healthcare offers a number of significant benefits, including more precise diagnoses, personalised treatments and improved health data management. However, it is critical to address very carefully the medico-legal challenges associated with this progress.