Israel Medical Association Journal

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.

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.

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.

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: The Gaza–Israeli conflict poses challenges for unbiased reporting due to its complexity and media bias. We explored recent scientific publications to understand scholarly discourse and potential biases surrounding this longstanding geopolitical issue.

Objectives: To conduct a descriptive bibliometric analysis of PubMed articles regarding the recent Gaza–Israeli conflict.

Methods: We reviewed 1628 publications using keywords and medical subject headings (MeSH) terms related to Gaza, Hamas, and Israel. We focused on articles written in English. A team of researchers assessed inclusion criteria, resolving disagreements through a third researcher.

Results: Among 37 publications, Lancet, BMJ, and Nature were prominent journals. Authors from 12 countries contributed, with variety of publication types (46% correspondence, 32% news). Pro-Gaza perspectives dominated (43.2%), surpassing pro-Israel (21.6%) and neutral (35.1%) viewpoints. Pro-Gaza articles exhibited higher Altmetric scores, indicating increased social media impact. Pro-Israel publications were predominantly authored by Israelis.

Conclusions: The prevalence of pro-Gaza perspectives underscores challenges in maintaining impartiality. Higher social media impact for pro-Gaza publications emphasizes the need for nuanced examination. Addressing bias is crucial for a comprehensive understanding of this complex conflict and promoting balanced reporting.

Background: Ventricular assist devices (VADs) play a critical and increasing role in treating end-stage heart failure in pediatric patients. A growing number of patients are supported by VADs as a bridge to heart transplantation. Experience with VADs in the pediatric population is limited, and experience in Israel has not been published.

Objectives: To describe this life-saving technology and our experience with VAD implantation in children with heart failure, including characteristics and outcomes.

Methods: We conducted a retrospective chart review of all patients who underwent VAD implantation at Schneider Children's Medical Center from 2018 to 2023.

Results: We analyzed results of 15 children who underwent VAD implantation. The youngest was 2.5 years old and weighed 11 kg at implantation. In eight patients, HeartMate 3, a continuous-flow device, was implanted. Seven patients received Berlin Heart, a pulsatile-flow device. Three children required biventricular support; 11 underwent heart transplants after a median duration of 169 days. Two patients died due to complications while awaiting a transplant; two were still on VAD support at the time of submission of this article. Successful VAD support was achieved in 86.6% of patients. In the last 5 years,79%  of our heart transplant patients received VAD support prior to transplant.

Conclusions: Circulatory assist devices are an excellent bridge to transplantation for pediatric patients reaching end-stage heart failure. VADs should be carefully selected, and implantation techniques tailored to patient's weight and diagnosis at a centralized pediatric cardiac transplantation center. Israeli healthcare providers should be cognizant of this therapeutic alternative.