Israel Medical Association Journal

Background: Pediatric urolithiasis is relatively uncommon and is generally associated with predisposing anatomic or metabolic abnormalities. In the adult population, emergency department (ED) admissions have been associated with an increase in ambient temperature. The same association has not been evaluated in the pediatric population.

Objectives: To analyze trends in ED admissions due to renal colic in a pediatric population (≤ 18 years old) and to assess the possible effect of climate on ED admissions.

Methods: We conducted a retrospective, multicenter cohort study, based on a computerized database of all ED visits due to renal colic in pediatric patients. The study cohort presented with urolithiasis on imaging during their ED admission. Exact climate data was acquired through the Israeli Meteorological Service (IMS).

Results: Between January 2010 and December 2020, 609 patients, ≤ 18 years, were admitted to EDs in five medical centers with renal colic: 318 males (52%), 291 females (48%). The median age was 17 years (IQR 9–16). ED visits oscillated through the years, peaking in 2012 and 2018. A 6% downward trend in ED admissions was noted between 2010 and 2020. The number of ED admissions in the different seasons was 179 in autumn (30%), 134 in winter (22%), 152 in spring (25%), and 144 in summer (23%) (P = 0.8). Logistic regression multivariable analysis associated with ED visits did not find any correlation between climate parameters and ED admissions due to renal colic in the pediatric population.

Conclusions: ED admissions oscillated during the period investigated and had a downward trend. Unlike in the adult population, rates of renal colic ED admissions in the pediatric population were not affected by seasonal changes or rise in maximum ambient temperature.

Background: Hypothermia, as a sign of serious bacterial infection (SBI) in children and infants older than 90 days is poorly characterized, especially in the post-pneumococcal vaccine era.

Objectives: To assess the prevalence of SBI in children and infants presenting to the pediatric emergency department (PED) with reported or documented hypothermia.

Methods: Retrospective data analysis was conducted of all well-appearing children aged 0–16 years who presented with a diagnosis of hypothermia at two tertiary PEDs from 2010 to 2019.

Results: The study comprised 99 children, 15 (15.2%) age 0–3 months, 71 (71.7%) 3–36 months, and 13 (13.1%) > 36 months. The youngest age group had increased length of stay in the hospital (P < 0.001) and increased rates of pediatric intensive care unit admissions (P < 0.001). Empirical antibiotic coverage was initiated in 80% of the children in the 0–3 months group, 21.1% in the 3–36 months group, and 15.4% in > 36 months (P < 0.001). Only one case of SBI was recorded and no bacteremia or meningitis. Hypothermia of unknown origin was the most common diagnosis in all age groups (34%, 42%, 46%), respectively, followed by bronchiolitis (26%) and hypoglycemia (13.3%) for 0–3 month-old children, unspecified viral infection (20%) and otitis media (7%) for 3–36-month old, and unspecified viral infection (23%) and alcohol intoxication (15.2%) in > 36 months.

Conclusion: There is a low incidence of SBI in well-appearing children presenting to the PED with hypothermia and a benign course and outcome in those older than 3 months.

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: 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.

A 69-year-old woman with a 30-year history of rheumatoid arthritis (RA) on leflunomide presented with dizziness and weakness. Vital signs, cardiopulmonary auscultation, and laboratory studies were normal. The serological status of her RA was unknown. She exhibited ulnar deviation and swan-necking of the hands but no nodular skin lesions. She was an active smoker. Chest radiography revealed an opacity in the right lung. Computed tomography (CT) showed multiple pulmonary nodules and a dominant thick-walled cavitary mass in the periphery of the right lower lobe [Figure 1A]. Due to concern for a malignancy or infection, she underwent a bronchoscopy with a biopsy of the mass, which was non-diagnostic. A subsequent transthoracic needle biopsy demonstrated a central zone of necrosis surrounded by a cuff of palisading epithelioid histiocytes with the presence of occasional giant cells [Figure 1B]. There was no malignancy, and stains for micro-organisms were negative. In this clinical context, biopsy results were consistent with a pulmonary rheumatoid nodule (PRN).

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.

In the grand theater of modern medicine, artificial intelligence (AI) has swiped the lead role, with a performance so riveting it deserves an Oscar, or at least a Nobel. From the intricate labyrinths of our arteries to the profound depths of our peepers, AI is the new maestro, conducting symphonies of data with the finesse of a seasoned virtuoso [1,2].

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.