AI and ML in radiology: Making progress

Primer on large language models: an educational overview for intensivists

The integration of artificial intelligence (AI) and machine learning-enabled medical technologies into clinical practice is expanding at an unprecedented pace. Among these, large language models (LLMs) represent a subset of machine learning designed to comprehend linguistic patterns, semantics, and contextual meaning by processing vast amounts of textual data. This educational primer aims to inform intensivists on the foundational concepts of LLMs and how to approach emerging literature in this area. In critical care, LLMs have the potential to enhance various aspects of patient management, from triage and clinical documentation to diagnostic support and prognostic assessment of patient deterioration. They have also demonstrated high appropriateness in addressing critical care-related clinical inquiries and are increasingly recognized for their role in post-ICU rehabilitation and as educational resources for patients' families and caregivers. Despite these promising applications, LLMs still have significant limitations, and integrating LLMs into clinical workflows presents inherent challenges, particularly concerning bias, reliability, and transparency. Given their emerging role as decision-support tools and potential collaborative partners in medicine, LLMs must adhere to rigorous validation and quality assurance standards. As the trajectory toward AI-driven healthcare continues, responsible and evidence-based integration of LLMs into critical care practice is imperative to optimize patient outcomes while ensuring ethical and equitable deployment.

How AI Could Help Us in the Epidemiology and Diagnosis of Acute Respiratory Infections?

Acute respiratory infections (ARIs) represent a significant global health burden, contributing to high morbidity and mortality rates, particularly in vulnerable populations. Traditional methods for diagnosing and tracking ARIs often face limitations in terms of speed, accuracy, and scalability. The advent of artificial intelligence (AI) has the potential to revolutionize these processes by enhancing early detection, precise diagnosis, and effective epidemiological tracking. This review explores the integration of AI in the epidemiology and diagnosis of ARIs, highlighting its capabilities, current applications, and future prospects. By examining recent advancements and existing studies, this paper provides a comprehensive understanding of how AI can improve ARI management, offering insights into its practical applications and the challenges that must be addressed to realize its full potential.

Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment

The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors such as cost-effectiveness and institutional information technology support. When a technology is new and relatively untested in a field, professional confidence is lacking and there is a sense of the need to go above the baseline level of validation and compliance. In this article, we propose an approach that goes beyond standard regulatory compliance for AI apps that are approved for marketing, including independent benchmarking in the lab as well as clinical audit in practice, with the aims of increasing trust and preventing harm.

Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review

Artificial intelligence (AI) in medicine is an increasingly studied and widespread phenomenon, applied in multiple clinical settings. Alongside its many potential advantages, such as easing clinicians' workload and improving diagnostic accuracy, the use of AI raises ethical and legal concerns, to which there is still no unanimous response. A systematic literature review on medical professional liability related to the use of AI-based diagnostic algorithms was conducted using the public electronic database PubMed selecting studies published from 2020 to 2023. The systematic review was performed according to 2020 PRISMA guidelines. The literature review highlights how the issue of liability in case of AI-related error and patient's damage has received growing attention in recent years. The application of AI and diagnostic algorithm moreover raises questions about the risks of using unrepresentative populations during the development and about the completeness of information given to the patient. Concerns about the impact on the fiduciary relationship between physician and patient and on the subject of empathy have also been raised. The use of AI in medical field and the application of diagnostic algorithms introduced a revolution in the doctor-patient relationship resulting in multiple possible medico-legal consequences. The regulatory framework on medical liability when AI is applied is therefore inadequate and requires urgent intervention, as there is no single and specific regulation governing the liability of various parties involved in the AI supply chain, nor on end-users. Greater attention should be paid to inherent risk in AI and the consequent need for regulations regarding product safety as well as the maintenance of minimum safety standards through appropriate updates.