David J. Ozeri MD, Adiel Cohen MD, Noa Bacharach MD, Offir Ukashi MD, Amit Oppenheim MD
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.
Orly Gal-Or MD, Alon Tiosano MD, Inbar Perchik BSc, Yogev Giladi MD, Irit Bahar MD
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.
Natalie Nathan MD, Michael Saring MD, Noam Savion-Gaiger MD, Kira Radinsky PhD, Alma Peri MD
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.
Orit Wimpfheimer MD, Yotam Kimmel BSc
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.
Ela Giladi MD, Roy Israel MD, Wasseem Daud MD, Chen Gurevitz MD, Alaa Atamna MD, David Pereg MD, Abid Assali MD, Avishay Elis MD
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.