Israel Medical Association Journal

Background: Survivors of critical illness are at increased risk of long-term impairments, referred to as post-intensive care unit (ICU) syndrome (PICS). Post-traumatic stress disorder (PTSD) is common among ICU survivors with reported rates of up to 27%. The prevalence of PTSD among Israeli ICU survivors has not been reported to date.

Objectives: To evaluate the prevalence of new onset PTSD diagnosed in a post-ICU clinic at a tertiary center in Israel.

Methods: We conducted a retrospective, single center, cohort study. Data were collected from medical records of all patients who visited the Tel Aviv Sourasky Medical Center post-ICU clinic between October 2017 and June 2020. New onset PTSD was defined as PTSD diagnosed by a certified board psychiatrist during the post-ICU clinic visit. Data were analyzed using descriptive statistics.

Results: Overall, 39 patients (mean age 51 ± 17 years, 15/39 females [38%]) attended the post-ICU clinic during the study period. They were evaluated 82 ± 57 days after hospital discharge. After excluding 7 patients due to missing proper psychiatric analysis, 32 patients remained eligible for the primary analysis. New PTSD was diagnosed in one patient (3%).

Conclusions: We found lower incidence of PTSD in our cohort when compared to existing literature. Possible explanations include different diagnostic tools and low risk factors rate. Unique national, cultural, and/or religious perspectives might have contributed to the observed low PTSD rate. Further research in larger study populations is required to establish the prevalence of PTSD among Israeli ICU survivors.

Takotsubo syndrome (TTS) or Takotsubo cardiomyopathy (TCM) is a cardiomyopathy that develops rapidly and is usually caused by mental or physical stress. It is usually a transient cardiomyopathy. The presumed cause of the onset of the syndrome is the increase and extreme secretion of adrenaline and norepinephrine due to extreme stress. An infectious disease such as sepsis can also be the cause [1].

One of the most widespread diagnostic tools is the revised version of Mayo Clinic Diagnostic Criteria for TTS (2008) [2], which incorporates transient wall-motion abnormalities, absence of a potential coronary culprit, myocarditis, and pheochromocytoma. The prognosis for TTS is usually favorable and resolves with complete recovery in 4–8 weeks in more than 90% of patients.

Since 1980 dermatologists have been interested in the exceptional healing reported by patients who underwent treatments at the Dead Sea. Tens of thousands of patients have visited this area and more than 10,000 cases have been the subject of clinical and laboratory studies since this natural therapeutic option was discovered for psoriasis management. Through evaluation of the published articles on climatotherapy, we tried to reach a global assessment of the usefulness of this approach and to discover whether this treatment still can be recommended in the era of biologic treatments. I conducted a review of the available literature on clinical trials through PubMed, Medline, and Google Scholar using the terms psoriasis and Dead Sea. I found 26 studies published between 1982 and 2021. Assessment of patients showed major improvement through several selected parameters. Length of the stay and medical supervision positively influenced the major outcomes observed. Duration of improvement and possible long-term side effects of this natural treatment still need to be more precisely determined. Exposure to the unique climatic factors of the region, essentially the sun and the sea, induces fast and significant results with high clearance rates of psoriasis plaques. Dead Sea climatotherapy still has its place for the control of psoriasis symptoms.

In this special issue of Israel Medical Association Journal (IMAJ) we expose readers to the topic of artificial intelligence (AI) in medicine. AI has become a powerful tool, which enables healthcare professionals to personalize treatment based on many factors, including genetic analyses of tumors, and to consider other co-morbidities affecting a specific patient. AI gives physicians the ability to analyze huge amounts of data and to combine data from different sources. AI can be implemented make a diagnosis based on computed tomography (CT) scans and magnetic resonance imaging (MRI) scans using deep machine learning and data that are stored in the memory of mega computers. AI assists in tailoring more precise surgery to train surgeons before surgery and to support surgeons during procedures. This advancement may benefit surgical procedures by making them more accurate and faster without cutting unnecessary tissues (e.g., nerves and blood vessels); thus, patients face fewer complications, lower rates of infection, and more operation theater time. In this issue, we include three original studies that describe the use of AI in academia and eight review articles that discuss applications of AI in different specialties in medicine. One of the review articles addresses ethical issues and concerns that are raised due to the more advanced use of AI in medicine.

Background: The field of artificial intelligence (AI) is poised to significantly influence the future of medicine. With the accumulation of vast databases and recent advancements in computer science methods, AI's capabilities have been demonstrated in numerous areas, from diagnosis and morbidity prediction to patient treatment. Establishing an AI research and development unit within a medical center offers multiple advantages, particularly in fostering research and tapping into the immediate potential of AI at the patient's bedside.

Objectives: To outline the steps taken to establish a center for AI and big data within an innovation center at a tertiary hospital in Israel.

Methods: We conducted a retrospective analysis of projects developed in the field of AI at the Artificial Intelligence Center at the Rabin Medical Center, examining trends, clinical domains, and the predominant sectors over a specific period.

Results: Between 2019 and 2023, data from 49 AI projects were gathered. A substantial and consistent growth in the number of projects was observed. Following the inauguration of the Artificial Intelligence Center we observed an increase of over 150% in the volume of activity. Dominant sectors included cardiology, gastroenterology, and anesthesia. Most projects (79.6%) were spearheaded by physicians, with the remainder by other hospital sectors. Approximately 59.2% of the projects were applied research. The remainder were research-based or a mix of both.

Conclusions: Developing technological projects based on in-hospital medical data, in collaboration with clinicians, is promising. We anticipate the establishment of more centers dedicated to medical innovation, particularly involving AI.

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.

Background: Completing internal medicine specialty training in Israel involves passing the Israel National Internal Medicine Exam (Shlav Aleph), a challenging multiple-choice test. multiple-choice test. Chat generative pre-trained transformer (ChatGPT) 3.5, a language model, is increasingly used for exam preparation.

Objectives: To assess the ability of ChatGPT 3.5 to pass the Israel National Internal Medicine Exam in Hebrew.

Methods: Using the 2023 Shlav Aleph exam questions, ChatGPT received prompts in Hebrew. Textual questions were analyzed after the appeal, comparing its answers to the official key.

Results: ChatGPT 3.5 correctly answered 36.6% of the 133 analyzed questions, with consistent performance across topics, except for challenges in nephrology and biostatistics.

Conclusions: While ChatGPT 3.5 has excelled in English medical exams, its performance in the Hebrew Shlav Aleph was suboptimal. Factors include limited training data in Hebrew, translation complexities, and unique language structures. Further investigation is essential for its effective adaptation to Hebrew medical exam preparation.

Artificial intelligence in ophthalmology is used for automatic diagnosis, data analysis, and predicting responses to possible treatments. The potential challenges in the application and assimilation of artificial intelligence include technical challenges of the algorithms, the ability to explain the algorithm, and the ability to diagnose and manage the medical course of patients. Despite these challenges, artificial intelligence is expected to revolutionize the way ophthalmology will be practiced. In this review, we compiled recent reports on the use and application of deep learning in various fields of ophthalmology, potential challenges in clinical deployment, and future directions.

Artificial intelligence (AI) has emerged as a transformative group of technologies in the field of medicine. Specifically in cardiology, numerous applications have materialized, and these are developing exponentially. AI-based risk prediction models leverage machine learning algorithms and large datasets to probe multiple variables, aid in the identification of individuals at high risk for adverse events, facilitate early interventions, and enable personalized risk assessments. Unique algorithms analyze medical images, such as electrocardiograms, echocardiograms, and cardiac computed tomography scans to enable rapid detection of abnormalities and aid in the accurate identification of cardiac pathologies. AI has also shown promise in guiding treatment decisions during coronary catheterization. In addition, AI has revolutionized remote patient monitoring and disease management by means of wearable and implantable sensing technologies. In this review, we discussed the field of cardiovascular genetics and personalized medicine, where AI holds great promise. While the applications of AI in cardiology are promising, challenges such as data privacy, interpretability of the findings, and multiple matters regarding ethics need to be addressed. We presented a succinct overview of the applications of AI in cardiology, highlighting its potential to revolutionize risk prediction, diagnosis, treatment, and personalized patient care.

Over the last decade the use of artificial intelligence (AI) has reformed academic research. While clinical diagnosis of psoriasis and psoriatic arthritis is largely straightforward, the determining factors of a clinical response to therapy, and specifically to biologic agents, have not yet been found. AI may meaningfully impact attempts to unravel the prognostic factors that affect response to therapy, assist experimental techniques being used to investigate immune cell populations, examine whether these populations are associated with treatment responses, and incorporate immunophenotype data in prediction models. The aim of this mini review was to present the current state of the AI-mediated attempts in the field. We executed a Medline search in October 2023. Selection and presentation of studies were conducted following the principles of a narrative–review design. We present data regarding the impact AI can have on the management of psoriatic disease by predicting responses utilizing clinical or biological parameters. We also reviewed the ways AI has been implemented to assist development of models that revolutionize the investigation of peripheral immune cell subsets that can be used as biomarkers of response to biologic treatment. Last, we discussed future perspectives and ethical considerations regarding the use of machine learning models in the management of immune-mediated diseases.

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.

Medical imaging data has been at the frontier of artificial intelligence innovation in medicine with many clinical applications. There have been many challenges, including patient data protection, algorithm performance, radiology workflow, user interface, and IT integration, which have been addressed and mitigated over the last decade. The AI products in imaging now fall into three main categories: triage artificial intelligence (AI), productivity AI, and augmented AI, each providing a different utility for radiologists, clinicians, and patients. Adoption of AI products into the healthcare system has been slow, but it is growing. It is typically dictated by return on investment, which can be demonstrated in each use case. It is expected to lead to wider adoption of AI products in imaging into the clinical workflow in the future.

Background: The use of proprotein convertase subtilisin/kexin type 9 monoclonal antibodies (PCSK9 mAbs) is emerging for lowering low-density lipoprotein cholesterol (LDL-C). However, real-world data is lacking for their use among elderly patients.

Objective: To define the characteristics of elderly patients treated with PCSK9 mAbs and to evaluate the efficacy and tolerability compared with younger patients.

Methods: We conducted a retrospective cohort study of elderly patients (≥ 75 years at enrollment) treated with PCSK9 mAbs for primary and secondary cardiovascular prevention. Data were retrieved for demographic and clinical characteristics; indications for treatment; agents and dosages; concomitant lipid lowering treatment; LDL-C levels at baseline, 6, 12 months, and at the end of follow up. Data also included achieving LDL-C target levels and adverse effects.

Results: The cohort included 91 elderly patients and 92 younger patients, mean age 75.2 ± 3.76 and 58.9 ± 7.4 years (P < 0.0001). Most patients (82%, 80%) were in high/very high-risk categories. For almost all (98%, 99%), the indication was statin intolerance, with PCSK9 mAb monotherapy the most prevalent regimen. The average follow-up was 38.1 ± 20.5 and 30.9 ± 15.8 months (P = 0.0258). Within 6 months the LDL-C levels were reduced by 57% in the elderly group and by 59% in the control group (P = 0.2371). Only 53% and 57% reached their LDL-C target levels. No clinically significant side effects were documented.

Conclusion: PCSK9 mAbs have similar effects and are well tolerated among elderly patients as in younger patients.

To the reviewers of 2023, who gave of their time to constructively assess the articles sent to them.

9 November 2023: Just one month after the tragic events of 7 October 2023, 240 individuals are still held hostage, ensnared by Hamas. Their medical plight is shrouded in silence. In the heart of Tel Aviv, a sea of health professionals gathers before the International Committee of the Red Cross (ICRC) offices pleading for decisive action. Among the medical pleas for help is the haunting image of a young soldier in dire need of his inhaler [Figure 1]. Ron needs it to live. I, a pediatric pulmonologist intimately familiar with respiratory distress, captured that moment.