Israel Medical Association Journal

Background: Patients with inflammatory bowel disease (IBD) are at increased risk after percutaneous coronary intervention (PCI).

Objectives: To compare the clinical outcomes within 30 days, one year, and five years of undergoing PCI.

Methods: We conducted a retrospective cohort study of adult patients with IBD who underwent PCI in a tertiary care center from January 2009 to December 2019.

Results: We included 44 patients, 26 with Crohn’s disease (CD) and 18 with ulcerative colitis (UC), who underwent PCI. Patients with CD underwent PCI at a younger age compared to UC (57.8 vs. 68.9 years, P < 0.001) and were more likely to be male (88.46% of CD vs. 61.1% of UC, P < 0.03). CD patients had a higher rate of non-steroidal treatment compared to UC patients (50% vs. 5.56%, P < 0.001). Acute coronary syndromes (ACS) and/or the need for revascularization (e.g., PCI) were the most common clinical events to occur following PCI, in both groups. Of patients who experienced ACS and/or unplanned revascularization within 5 years, 25% of UC vs. 40% of CD had target lesion failure (TLF) due to in-stent restenosis and 10% of CD had TLF due to stent thrombosis.

Conclusions: We observed higher rates of TLF in IBD patients compared to the general population as well as differences in clinical outcomes between UC and CD patients. A better understanding of the prognostic factors and pathophysiology of these differences may have clinical importance in tailoring the appropriate treatment or type of revascularization for this high-risk group.

Background: Opioid-base sedation is considered the first line choice in ventilated patients in intensive care units (ICU). Few studies have examined sedation in ventilated patients outside the ICU. A pilot program was initiated in the internal medicine ward A at Meir Hospital in Kfar Saba, Israel. A new sedation protocol was implemented for opioid-based versus benzodiazepine-based sedation in ventilated patients.

Objectives: To compare the rates and intensity of delirium between patients who received opioid-based sedation vs. benzodiazepine-based sedation. To compare parameters related to morbidity and mortality.

Methods: We conducted a retrospective before-after intervention study based on data collection. Patients who were admitted to the internal medicine ward A from January 2020 to January 2021 and required sedation and ventilation were included. Demographic data, medical history data, admission data, Richmond Agitation and Sedation Scale scores, hemodynamic parameters, reports of falls and self-harm, and data regarding unplanned extubation were collected, as well as the need for additional sedative drugs.

Results: Chronic hypertension was more common in the opioid group. Delirium intensity tended to be higher in the benzodiazepine group. The number of ventilation days was significantly higher in the benzodiazepine group, as was the number of times adjuvant sedation was required.

Conclusions: Opioid-based sedation outside the ICU was associated with shorter ventilation days, tendency toward lower intensity of delirium, and reduction in requirement of adjuvant sedative drugs compared to benzodiazepine-based sedation. Further studies are required to confirm the findings.

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: 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 powerful technology in medicine, with a potential to revolutionize various aspects of disease management. In recent years, substantial progress has been made in the development and implementation of AI algorithms and models for the diagnosis, screening, and monitoring of retinal diseases. We present a brief update on recent advancements in the implementation of AI in the field of retinal medicine, with a focus on age-related macular degeneration, diabetic retinopathy, and retinopathy of prematurity. AI algorithms have demonstrated remarkable capabilities in automating image analysis tasks, thus enabling accurate segmentation and classification of retinal pathologies. AI-based screening programs hold great promise in cost-effective identification of individuals at risk, thereby facilitating early intervention and prevention. Future integration of multimodal imaging data including optical coherence tomography with additional clinical parameters, will further enhance the diagnostic accuracy and support the development of personalized medicine, thus aiding in treatment selection and optimizing therapeutic outcomes. Further research and collaboration will drive the transformation of AI into an indispensable tool for improving patient outcomes and enhancing the field of retinal medicine.

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.

A rise in the incidence of chronic health conditions, notably heart failure, is expected due to demographic shifts. Such an increase places an onerous burden on healthcare infrastructures, with recurring hospital admissions and heightened mortality rates being prominent factors. Efficient chronic disease management hinges on regular ambulatory care and preemptive action. The application of intelligent computational models is showing promise as a key resource in the ongoing management of chronic diseases, particularly in forecasting disease trajectory and informing timely interventions. In this review, we explored a pioneering intelligent computational model by Diagnostic Robotics, an Israeli start-up company. This model uses data sourced from insurance claims to forecast the progression of heart failure. The goal of the model is to identify individuals at increased risk for heart failure, thus enabling interventions to be initiated early, mitigating the risk of disease worsening, and relieving the pressure on healthcare facilities, which will result in economic efficiencies.

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.

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.

A dramatic portrait bust of the physician Gabriele da Fonseca (1586? to 1668) at prayer is considered by art historians to be one of the finest late works of Gian Lorenzo Bernini (1598–1680), the preeminent sculptor of 17th century Rome. This statue is of medical as well as artistic interest. First, Fonseca is shown wearing his physician’s robe, thus celebrating his successful career as a leading medical figure in Rome, holding both Papal and university appointments at the highest level. In addition, the positioning of the statue in a special chapel designed by Bernini highlights Fonseca’s role as an influential participant in the introduction of quinine into Europe as a cure for malaria. Last, an examination of the statue’s hands identifies a number of pathologies and anatomical anomalies that raise interesting questions, regrettably unanswerable given the information presently available, concerning Fonseca’s illnesses and cause of death.

On 7 October 2023, a large-scale invasion by armed Hamas terrorists occurred in southern Israel. Approximately 1500 militants breached the Gaza security barrier using tractors, RPGs, and explosives. Concurrently, the terrorists utilized various means including armed vehicles, motorized paragliders, sea incursions, and a massive rocket attack launched toward Israel. On entering Israeli territory, the militants dispersed and targeted several towns, kibbutzim (collective communities), and Israel Defense Forces (IDF) military bases near Gaza. This strategy resulted in a death toll exceeded 1300 civilians and soldiers. In addition, more than 240 individuals were abducted. This attack occurred in one day. In this article, we introduce the Israeli National Institute of Forensic Medicine, which specialized in forensic analysis during mass casualty incidents, and pivotal role it played on 7 October. We present a detailed discussion on methods, challenges, and adaptations the institute took in response to the event of 7 October.

Background: In the past decade, numerous new imaging and laboratory tests have been implemented that significantly contribute to improved medical diagnostic capabilities. However, inappropriate utilization, which occurs on a large scale, has significant ramifications for both patient care and health systems.

Objectives: To assess the impact of a novel clinical decision support system (CDSS) applied to our electronic medical records on abdominal ultrasonography utilization pattern.

Methods: We conducted a retrospective cohort study comparing patterns of abdominal ultrasound utilization in cases of liver enzyme elevation, with and without CDSS, between February and May in 2017 (before CDSS implementation) and during the same months in 2018 (after CDSS implementation). The following parameters were collected: number of tests ordered according to the guidelines, tests with a diagnostic value, and order forms completed with any data or a diagnostic question. The comparison was conducted using chi-square test.

Results: Of 152 abdominal ultrasound tests, 72 were ordered in the pre-implementation period and 80 in the post-implementation period. The system failed to reach statistical significance regarding the rates of ordered tests according to the guidelines and/or tests with a diagnostic value. However, the use of the CDSS had a statistically significant impact regarding completing the order form with data, including a specific diagnostic question.

Conclusions: The effect of the system on the efficiency of test utilization was partial. However, our findings strongly suggested that CDSS has the potential to promote proper usage of complementary technologies.

Background: Unfractionated heparin is the preferred anticoagulant used during open heart surgeries, including left ventricular assist device (LVAD) implantation. In cases in which patients are heparin-induced thrombocytopenia positive (HIT+), the accepted practice has been to substitute heparin with bivalirudin. This practice may be associated with significant bleeding and adverse outcomes.

Objectives: To review our experience with HIT+ patients who were heparin-induced thrombocytopenia with thrombosis negative (HITT-) and who underwent HeartMate 3 LVAD implantation using heparin intraoperatively rather than bivalirudin.

Methods: From 2016 to 2022, 144 adult patients were implanted with HeartMate 3 LVAD at our center. Among them, 7 were detected as HIT+ but HITT- and therefore were prescribed intraoperatively with heparin and treated pre- and postoperatively with bivalirudin. We reviewed the preoperative, intraoperative, and postoperative characteristics as well as short-term mortality and the complication rates of these HIT+ patients.

Results: The median age of our cohort was 56 years (51–60), 71% were male (n=5), all were INTERMACS Level 1, and most were bridged to transplant (n=6, 86%). The 30-day mortality rate post-implantation was 0%. The average 24-hour chest drain postoperative output was 1502.86 ± 931.34 ml. There were no intraoperative pump thromboses, perioperative thromboses, cerebrovascular accidents, or gastrointestinal bleeding within the first 24 hours postoperative. One patient required a revision due to bleeding.

Conclusions: Intraoperative unfractionated heparin may be administered to patients who are HIT+ and HITT- while undergoing LVAD implantation. However, further investigation is required.