Israel Medical Association Journal

Background: Advancements in artificial intelligence (AI) and natural language processing (NLP) have led to the development of language models such as ChatGPT. These models have the potential to transform healthcare and medical research. However, understanding their applications and limitations is essential.

Objectives: To present a view of ChatGPT research and to critically assess ChatGPT's role in medical writing and clinical environments.

Methods: We performed a literature review via the PubMed search engine from 20 November 2022, to 23 April 2023. The search terms included ChatGPT, OpenAI, and large language models. We included studies that focused on ChatGPT, explored its use or implications in medicine, and were original research articles. The selected studies were analyzed considering study design, NLP tasks, main findings, and limitations.

Results: Our study included 27 articles that examined ChatGPT's performance in various tasks and medical fields. These studies covered knowledge assessment, writing, and analysis tasks. While ChatGPT was found to be useful in tasks such as generating research ideas, aiding clinical reasoning, and streamlining workflows, limitations were also identified. These limitations included inaccuracies, inconsistencies, fictitious information, and limited knowledge, highlighting the need for further improvements.

Conclusions: The review underscores ChatGPT's potential in various medical applications. Yet, it also points to limitations that require careful human oversight and responsible use to improve patient care, education, and decision-making.

Large language models have revolutionized natural language processing. The emergence phenomenon is observed in these models and has the potential to revolutionize data processing and management. In this review, we discuss the concept of emergence in artificial intelligence, give detailed examples, and elaborate on the risks and limitations of large language models. The review exposes physicians to large language models, their advantages, and the inherent opportunities. We also describe the limitations and dangers, as these models are expected to impact medicine soon.

In this study, we analyzed computed tomography (CT) radiological findings from trauma treated at a single hospital in the aftermath of the terror attack in Israel on 7 October 2023. The study includes images from 34 consecutive patients, consisting of 33 males and 1 female, ranging in age from 19 to 68 years. The majority of these patients underwent both chest-abdominal-pelvic (76%) and head and neck CT scans (64.7%). Key findings highlight a high incidence of head and neck injuries (55.9%), chest trauma (44.1%), and various injuries such as soft tissue lacerations (100%), fractures particularly skull fractures at 32.4%, and brain hemorrhages (23.5%). The limitations of this study include its single-center scope and the focus on stable patients, which may bias the representation of injury types. This case series provides critical insights into the radiological impacts of large-scale terror events, emphasizing the importance of comprehensive preparedness and research in the field of mass-casualty incident response.