Artificial intelligence needs big data to develop reliable predictions. Therefore, storing and processing health data is essential for the new diagnostic and decisional technologies but, at the same time, represents a risk for privacy protection. This scoping review is aimed at underlying the medico-legal and ethical implications of the main artificial intelligence applications to healthcare, also focusing on the issues of the COVID-19 era. Starting from a summary of the United States (US) and European Union (EU) regulatory frameworks, the current medico-legal and ethical challenges are discussed in general terms before focusing on the specific issues regarding informed consent, medical malpractice/cognitive biases, automation and interconnectedness of medical devices, diagnostic algorithms and telemedicine. We aim at underlying that education of physicians on the management of this (new) kind of clinical risks can enhance compliance with regulations and avoid legal risks for the healthcare professionals and institutions.

Background On May 12, 2020, a symposium titled “Liability of healthcare professionals and institutions during COVID-19 pandemic” was held in Italy with the participation of national experts in malpractice law, hospital management, legal medicine, and clinical risk management. The symposium’s rationale was the highly likely inflation of criminal and civil proceedings concerning alleged errors committed by health care professionals and decision makers during the COVID-19 pandemic. Its aim was to identify and discuss the main issues of legal and medicolegal interest and thus to find solid solutions in the spirit of preparedness planning. Methods There were 5 main points of discussion: (A) how to judge errors committed during the pandemic because of the application of protocols and therapies based on no or weak evidence of efficacy, (B) whether hospital managers can be considered liable for infected health care professionals who were not given adequate personal protective equipment, (C) whether health care professionals and institutions can be considered liable for cases of infected inpatients who claim that the infection was transmitted in a hospital setting, (D) whether health care institutions and hospital managers can be considered liable for the hotspots in long-term care facilities/care homes, and (E) whether health care institutions and hospital managers can be considered liable for the worsening of chronic diseases. Results and Conclusion Limitation of the liability to the cases of gross negligence (with an explicit definition of this term), a no-fault system with statal indemnities for infected cases, and a rigorous methodology for the expert witnesses were proposed as key interventions for successfully facing future proceedings.

IntroductionIn primary care, almost 75% of outpatient visits by family doctors and general practitioners involve continuation or initiation of drug therapy. Due to the enormous amount of drugs used by outpatients in unmonitored situations, the potential risk of adverse events due to an error in the use or prescription of drugs is much higher than in a hospital setting. Artificial intelligence (AI) application can help healthcare professionals to take charge of patient safety by improving error detection, patient stratification and drug management. The aim is to investigate the impact of AI algorithms on drug management in primary care settings and to compare AI or algorithms with standard clinical practice to define the medication fields where a technological support could lead to better results.Methods and analysisA systematic review and meta-analysis of literature will be conducted querying PubMed, Cochrane and ISI Web of Science from the inception to December 2021. The primary outcome will be the reduction of medication errors obtained by AI application. The search strategy and the study selection will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the population, intervention, comparator and outcome framework. Quality of included studies will be appraised adopting the quality assessment tool for observational cohort and cross-sectional studies for non-randomised controlled trials as well as the quality assessment of controlled intervention studies of National Institute of Health for randomised controlled trials.Ethics and disseminationFormal ethical approval is not required since no human beings are involved. The results will be disseminated widely through peer-reviewed publications.

ObjectivesThe aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type.MethodsA systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials.ResultsStudies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety.ConclusionThis study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.

“Hospitals” as a name for a journal might appear simply as an umbrella term for healthcare-relevant research [...]