Artificial Intelligence: its Future and Impact on Acute Medicine.

This commentary explores the potential impact of artificial intelligence (AI) in acute medicine, considering its possibilities and challenges. With its ability to simulate human intelligence, AI holds the promise for supporting timely decision-making and interventions in acute care. While AI has significantly contributed to improvements in various sectors, its implementation in healthcare remains limited. The development of AI tools tailored to acute medicine can improve clinical decision-making, and AI's role in streamlining administrative tasks, exemplified by ChatGPT, may offer immediate benefits. However, challenges include uniform data collection, privacy, bias, and preserving the doctor-patient relationship. Collaboration among AI researchers, healthcare professionals, and policymakers is crucial to harness the potential of AI in acute medicine and create a future where advanced technologies synergistically enhance human expertise.

Clinical applications of artificial intelligence in sepsis: A narrative review.

Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review is to give an overview of the literature and thereby identify knowledge gaps and prioritize areas with high priority for further research. A literature search was conducted in PubMed from inception to February 2019. Search terms related to AI were combined with terms regarding sepsis. Articles were included when they reported an area under the receiver operator characteristics curve (AUROC) as outcome measure. Fifteen articles on diagnosis of sepsis with AI models were included. The best performing model reached an AUROC of 0.97. There were also seven articles on prognosis, predicting mortality over time with an AUROC of up to 0.895. Finally, there were three articles on assistance of treatment of sepsis, where the use of AI was associated with the lowest mortality rates. Of the articles, twenty-two were judged to be at high risk of bias or had major concerns regarding applicability. This was mostly because predictor variables in these models, such as blood pressure, were also part of the definition of sepsis, which led to overestimation of the performance. We conclude that AI models have great potential for improving early identification of patients who may benefit from administration of antibiotics. Current AI prediction models to diagnose sepsis are at major risks of bias when the diagnosis criteria are part of the predictor variables in the model. Furthermore, generalizability of these models is poor due to overfitting and a lack of standardized protocols for the construction and validation of the models. Until these problems have been resolved, a large gap remains between the creation of an AI algorithm and its implementation in clinical practice.

Telehealth requires expansion of physicians' communication competencies training.

We are in the midst of transformation of health systems where remote consulting (via video, telephone, email, and mobile messaging) is soon to become the dominant mode of consultation. Most of the literature on telehealth omits mentioning the need for telehealth communication competencies. Yet evidence base has been growing about how critical this training is - whether from clinical communication research or litigation claims analysis. In this article, we are calling for an urgent expansion of communication skills curricula to encompass these new telehealth domains from medical schools, specialty trainings to CMEs.