Israel Medical Association Journal

Neuroendocrine tumors (NETs) are a group of rare, heterogenous neoplasms that maintain unique morphologic and clinical features of neuroendocrine neoplasia and account for approximately 0.5% of all newly diagnosed malignancies. NETs are divided into two groups based on their histopathological morphology: well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). Well differentiated NETs are classified as G1, G2, or G3 based on their proliferation rate, whereas NECs are highly proliferative and poorly differentiated by definition [1]. Neuroendocrine neoplasms can occur almost anywhere in the body; however, they are most often seen in the gastrointestinal tract, pancreas, and lungs [2]. The extrahepatic bile duct is one of the rarest primary sites for NETs, accounting for 0.1% to 0.2% of NETs of the gastrointestinal tract [3]. Signet ring cell bile duct NETs are extremely uncommon and have no established incidence and prognosis due to their rarity. There is sparse information available regarding these tumors, and only a few cases have been reported in the literature to date. In this report, we presented the clinical course and surgical management of a 31-year-old female patient with a Klatskin signet ring cell NET.

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: 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.

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 (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.

In recent years, we have been experiencing a technological revolution, which signifies an ethical and societal transformation. Artificial intelligence (AI) based technologies have gradually permeated all aspects of life and solidified their position. Within this context, the emergence of these technologies offers new opportunities in the medical field, including palliative care, which is aimed at alleviating suffering and improving the quality of life for terminally ill patients and their families. In Israel, the Dying Patient Act of 2005 (the law), which promotes values such as the sanctity of life and individual autonomy, allows terminally ill patients to determine their preferred treatment, and withhold life-saving treatment under certain circumstances. The law represents a significant step toward improving care for terminally ill patients, reducing pain and suffering, and respecting the patient's wishes and worldviews in their final days. However, the practical implementation of the law has encountered numerous challenges, ranging from lack of familiarity among doctors and healthcare professionals and the requirement to determining life expectancy to fulfilling the law's purpose. These challenges are associated with ethical, cultural, and religious perspectives. In this article, we describe how AI-based technologies hold immense potential in applying the law and providing palliative care based on their predictive capabilities, prognostic accuracy, and optimization of treatment as well as communication between patients and healthcare providers. However, as an innovative, developing, and complex technology, it is crucial not to overlook the ethical, societal, and legal challenges inherent in implementing and using AI-based technologies in the context of palliative care.

Twenty-five years ago, we, the undersigned together with the chairman of the Israel Medical Association at the time, Prof. Yoram Blachar, and the Secretary General of the Israel Medical Association, Adv. Leah Wapner, joined forces to found an Israeli medical journal in English. The purpose of this journal was to present to the world Israeli clinical medicine and medical research. That journal is none other than the Israel Medical Association Journal (IMAJ), and in 2023 its 300th issue was published. In 2024 we keep going, taking pride in the fact that every one of those past 300 issues has been published and dispatched on time, without delay, regardless of any circumstances.

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.

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: At the beginning of 2020, the coronavirus disease 2019 (COVID-19) pandemic presented a new burden on healthcare systems.

Objectives: To evaluate the impact of the COVID-19 pandemic on the outcome of non-COVID patients in Israel.

Methods: We conducted a retrospective observational cohort study at a tertiary medical center in Israel. From December 2018 until June 2022, 6796 patients were hospitalized in the internal medicine wards. Patients were grouped based on their admission date: admitted during COVID waves (waves group), admitted between waves (interim group), and admitted during the same months in the previous year (former-year group).

Results: Mortality during hospitalization and 30-day mortality were higher in the waves group compared to the interim and former-year groups (41.4% vs. 30.5% and 24%, 19.4% vs. 17.9% and 12.9%, P < 0.001). In addition, 1-year mortality was higher in the interim group than in the waves and former-year group (39.1 % vs. 32.5% and 33.4%, P = 0.002). There were significant differences in the readmissions, both at 1 year and total number. The waves group had higher rates of mechanical ventilation and noradrenaline administration during hospitalization. Moreover, the waves group exhibited higher troponin levels, lower hemoglobin levels, and more abnormalities in liver and kidney function.

Conclusions: Hospitalized non-COVID patients experienced worse outcomes during the peaks of the pandemic compared to the nadirs and the preceding year, perhaps due to the limited availability of resources. These results underscore the importance of preparing for large-scale threats and implementing effective resource allocation policies.

Background: Computed tomography (CT) is the main diagnostic modality for detecting pancreatic adenocarcinoma.

Objectives: To assess the frequency of missed pancreatic adenocarcinoma on CT scans according to different CT protocols.

Methods: The medical records of consecutive pancreatic adenocarcinoma patients were retrospectively collected (12/2011–12/2015). Patients with abdominal CT scans performed up to a year prior to cancer diagnosis were included. Two radiologists registered the presence of radiological signs of missed cancers. The frequency of missed cancers was compared between portal and pancreatic/triphasic CT protocols.

Results: Overall, 180 CT scans of pancreatic adenocarcinoma patients performed prior to cancer diagnosis were retrieved; 126/180 (70.0%) were conducted using pancreatic/triphasic protocols and 54/180 (30.0%) used portal protocols. The overall frequency of missed cancers was 6/180 (3.3%) in our study population. The frequency of missed cancers was higher with the portal CT protocols compared to the pancreatic/triphasic protocols: 5/54 (9.3%) vs. 1/126 (0.8%), P = 0.01. CT signs of missed cancers included small hypodense lesions, peri-pancreatic fat stranding, and dilated pancreatic duct with a cut-off sign.

Conclusions: The frequency of missed pancreatic adenocarcinoma is higher on portal CT protocols. Physicians should consider the cancer miss rate on different CT protocols.