Israel Medical Association Journal

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).

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 (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.

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.

Background: The coronavirus disease 2019 (COVID-19) pandemic caused significant global turmoil, including changes in social and societal conduct such as lockdowns, social isolation, and extensive regulations. These changes can be major sources of stress. The first wave of the pandemic (April–May 2020) was a time of global uncertainty. We evaluated symptom severity among 29 Israeli children and adolescents with obsessive-compulsive disorder (OCD). Our previous study found that most of these participants did not experience an exacerbation of symptoms.

Objective: To re-evaluate the OCD symptoms of 18 participants from the original group of 29 children and adolescents during three time points: before the pandemic, during the first wave, and 2 years later.

Methods: Obsessive-compulsive symptoms (OCS) were assessed using the Clinical Global Impression Scale (CGI), a functional questionnaire, and the Obsessive-Compulsive Inventory-child version (OCI-CV).

Results: OCS in patients did not change significantly during the three time points. Participants reported minimal changes in their general functioning 2 years after the outbreak of COVID-19 and showed minimal change in OCI-CV scale scores.

Conclusions: Our results indicated clinical stability of OCD symptoms among most of the participants.

Background: Pregnant women are at higher risk for severe coronavirus disease 2019 (COVID-19). Since the release of the BNT162b2 messenger RNA vaccine (Pfizer/BioNTech), there has been accumulated data about the three vaccine doses. However, information regarding obstetric and neonatal outcomes of pregnant women vaccinated with the third (booster) vaccine is limited and primarily retrospective.

Objectives: To evaluate the obstetric and early neonatal outcomes of pregnant women vaccinated during pregnancy with the COVID-19 booster vaccine compared to pregnant women vaccinated only by the first two doses.

Methods: We conducted a cross-sectional study of pregnant women who received the BNT162b2 vaccine during pregnancy. Obstetric and neonatal outcomes were compared between pregnant women who received only the first two doses of the vaccine to those who also received the booster dose.

Results: Overall, 139 pregnant women were vaccinated during pregnancy with the first two doses of the vaccine and 84 with the third dose. The third dose group received the vaccine earlier during their pregnancy compared to the two doses group (21² vs. 31⁵ weeks, respectively, P < 0.001). No differences in obstetric and early neonatal outcomes between the groups were found except for lower rates of urgent cesarean delivery in the third dose group (adjusted odds ratio 0.21; 95% confidence interval 0.048–0.926, P = 0.039).

Conclusions: Compared to the first two doses of the BNT162b2 vaccine given in pregnancy, the booster vaccination is safe and not associated with an increased rate of adverse obstetric and early neonatal outcomes.

Background: There are conflicting data on the significance of hyperuricemia or hyperuricosuria in urolithiasis formation and on the need for medical treatment.

Objectives: To assess the significance of hyperuricemia or hyperuricosuria in urolithiasis formation, particularly when hyperuricemia occurs with normal uricosuria.

Methods: The electronic medical records of patients treated in Haifa and the Western Galilee district of Clalit Health Services, Israel, were retrospectively screened for diagnosis of nephrolithiasis or renal or urinary tract/bladder calculi between February 2014 and April 2019. The diagnosis was confirmed by ultrasonography or computed tomography. The study group included patients with one of these diagnoses. Patients in the control group did not have these diagnoses. The inclusion criterion for all patients was the presence of both serum and urinary uric acid levels.

Results: The study group included 359 patients and the control group 267. After adjustment by logistic regression, we found no significant differences in the prevalence of hyperuricosuria in the study group (14.8%) compared to the control group (9.7%), odds ratio (OR) 1.54 (95% confidence interval [95%CI] 0.74–3.2, P = 0.245). No significant differences between the groups were observed for hyperuricemia prevalence (45.4% vs. 55.1%, respectively, OR 0.82, 95%CI 0.54–1.25, P = 0.355), nor among those without hyperuricosuria (OR 0.83, 95%CI 0.52–1.33, P = 0.438) and after propensity score matching (OR 0.93, 95%CI 0.66–1.3, P = 0.655).

Conclusions: There were no significant differences in hyperuricemia or hyperuricosuria between the two groups of patients or in hyperuricemia among participants without hyperuricosuria.

Background: Pulmonary aspiration is a potentially lethal perioperative complication that can be precipitated by gastric insufflation. Face mask ventilation (FMV), a ubiquitous anesthetic procedure, can cause gastric insufflation. FMV with an inspiratory pressure of 15 cm H₂O provides the best balance between adequate pulmonary ventilation and a low probability of gastric insufflation. There is no data about the effects of FMV > 120 seconds.

Objectives: To investigate the effect of prolonged FMV on gastric insufflation.

Methods: We conducted a prospective observational study at a tertiary medical center with female patients who underwent oocyte retrieval surgery under general anesthesia FMV. Pre- and postoperative gastric ultrasound examinations measured the gastric antral cross-sectional area to detect gastric insufflation. Pressure-controlled FMV with an inspiratory pressure of 15 cm H₂O was continued from the anesthesia induction until the end of the surgery.

Results: The study comprised 49 patients. Baseline preoperative gastric ultrasound demonstrated optimal and good image quality. All supine measurements were feasible. The median duration of FMV was 13 minutes (interquartile range 9–18). In the postoperative period, gastric insufflation was detected in only 2 of 49 patients (4.1%). There was no association between the duration of FMV and delta gastric antral cross-sectional area (β -0.01; 95% confidence interval -0.04 to 0.01, P = 0.31).

Conclusions: Pressure-controlled FMV with an inspiratory pressure of 15 cm H₂O carries a low incidence of gastric insufflations, not only as a bridge to a definitive airway but as an alternative ventilation method for relatively short procedures in selective populations.

Background: The management of complicated appendicitis is inconclusive. Guidelines have not been established for the use of personalized antibiotic treatment.

Objectives: To investigate specific risk factors to consider during the initial first-choice antibiotic therapy in children with complicated appendicitis.

Methods: This study included all pediatric patients younger than 18 years of age who underwent a laparoscopic appendectomy during 2012–2022 at a single tertiary medical center.

Results: In total, 300 pediatric patients underwent laparoscopic appendectomy due to complicated appendicitis. The patients were treated with ceftriaxone + metronidazole (CM). For 57 (19%) patients, the empirical treatment was changed to tazobactam/piperacillin (TP) due to resistant bacteria or clinical deterioration. The presence of generalized peritonitis during surgery and C-reactive protein (CRP) levels above 20 mg/L at admission were identified as risk factors for changing the antibiotic regimen from CM to TP.

Conclusions: Generalized peritonitis and CRP > 20 gr/L were highly correlated with changing the antibiotic regimen to TP. For such patients, initial treatment with TP may result in clinical improvement and shorter hospitalization. 

Background: Laryngopharyngeal reflux (LPR) refers to the backflow of acidic stomach content into the larynx, pharynx, and upper aerodigestive tract. The diagnosis of LPR is based on the patient's history and findings of the laryngoscopy associated with LPR. Other possible manifestations consistent with LPR symptoms include laryngeal cancer, vocal fold granulomas, Reinke's space edema, and vocal polyps. In this study, we compared the characteristics of patients with LPR symptoms and incidental laryngeal findings (ILF) in the laryngoscopic evaluation to those without ILF (WILF).

Objectives: Determine the characteristics of LPR-symptomatic patients with ILF versus WILF.

Methods: In this retrospective study, we examined 160 medical charts from patients referred to the otolaryngology clinic at Galilee Medical Center for LPR evaluation 2016–2018. The reflux symptoms index (RSI), reflux finding score (RFS), and demographics of the patient were collected. All patients with a positive RSI score for LPR (RSI > 9) were included, and the profiles of patients with versus without ILF on laryngoscopy examination were compared.

Results: Of the 160 patients, 20 (12.5%) had ILF during laryngoscopy. Most had vocal cord findings such as leukoplakia (20%), polyps (15%), and nodules (20%). Hoarseness, throat clearing, swallowing difficulty, breathing difficulties, and total RSI score were significantly higher in patients with ILF.

Conclusions: Evaluation of LPR symptoms may provide otolaryngologists with a tool to identify patients with other findings on fiberoptic laryngoscopy. A laryngoscopic examination should be part of the examination of every patient with LPR to enable diagnosis of incidental findings.