Transforming literature screening: The emerging role of large language models in systematic reviews.

Systematic reviews (SR) synthesize evidence-based medical literature, but they involve labor-intensive manual article screening. Large language models (LLMs) can select relevant literature, but their quality and efficacy are still being determined compared to humans. We evaluated the overlap between title- and abstract-based selected articles of 18 different LLMs and human-selected articles for three SR.

[Quo vadis joint emergency dispatch system?].

The call volume in emergency medical service (EMS) dispatch centers has seen a drastic increase for many years now, especially looking at urban regions of Germany. In this context, the control mechanisms of the EMS dispatch center can be utilized to break new ground regarding the handling of emergency calls and dispatch practice in order to manage incoming calls as efficiently as possible. This article clearly explains standardized protocol-based emergency medical call taking, internal structuring of control centers and pathways also during the COVID-19 pandemic, using the Berlin EMS dispatch enter as an example.

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied.

[Prehospital perimortem cesarean section during cardiopulmonary resuscitation for traumatic cardiac arrest : Case report and lessons learned].

The following case report discusses the resuscitation of a pregnant woman in traumatic cardiac arrest after a fall from a height with consecutive resuscitative hysterotomy for maternal and fetal salvage. The report illustrates all lessons learned from critical appraisal amid new guideline recommendations and gives an overview of the published literature on the matter. Despite extensive resuscitation efforts, ultimately both the mother and the newborn were pronounced life extinct at the scene.

Success Factors of Artificial Intelligence Implementation in Healthcare.

Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years' significant academic AI advancements and the comparably low level of practical application in healthcare. A literature and real life cases analysis was conducted in Scopus and OpacPlus as well as the Google advanced search database.

The Economic Impact of Artificial Intelligence in Health Care: Systematic Review.

Background: Positive economic impact is a key decision factor in making the case for or against investing in an artificial intelligence (AI) solution in the health care industry. It is most relevant for the care provider and insurer as well as for the pharmaceutical and medical technology sectors. Although the broad economic impact of digital health solutions in general has been assessed many times in literature and the benefit for patients and society has also been analyzed, the specific economic impact of AI in health care has been addressed only sporadically.