Israel Medical Association Journal

Background: The rapid evolution of large language models warrants updated benchmarking in ophthalmology to determine whether newer versions offer clinically meaningful improvements over earlier models and human comparators.

Objectives: To evaluate the diagnostic accuracy of ChatGPT-4o and ChatGPT-5 in ophthalmic cases and to compare it with previously reported results of ChatGPT-3.5, residents, and specialists.

Methods: This retrospective cohort study was conducted in one academic tertiary medical center. We reviewed data of patients admitted to the ophthalmology department from June 2022 to January 2023. We then created two clinical cases for each patient. The first was according to medical history alone (Hx). The second added the clinical examination (Hx and Ex). For each case, we asked for the three most likely diagnoses from ChatGPT-4o and ChatGPT-5. We then compared the accuracy rates (at least one correct diagnosis) with previous results of ChatGPT-3.5, residents, and specialists.

Results: A total of 63 cases were analyzed, first using history alone and then with examination findings. Based on history alone, GPT-5 and GPT-4o correctly identified 73% and 70% of cases, respectively, outperforming GPT-3.5 (54%, P < 0.05) and approaching the accuracy of residents (75%) and attending physicians (71%, P < 0.05). When physical examination was included, diagnostic accuracy rose to 94% for GPT-5 and 89% for GPT-4o, surpassing GPT-3.5 (68%, P < 0.05) and closely matching or exceeding human performance (residents 94%, attendings 87%).

Conclusions: ChatGPT-4o and ChatGPT-5 significantly outperformed GPT-3.5 and achieved diagnostic accuracy similar or even higher to clinicians in diagnosing ophthalmology cases.

Background: Gait disturbances are common in patients undergoing hemodialysis and are associated with increased fall risk, mobility decline, and adverse health outcomes. Prior research suggests that hemodialysis may impact gait parameters such as speed, stride length, and variability; however, findings are inconsistent.

Objectives: To evaluate acute changes in gait metrics before and after hemodialysis using an artificial intelligence (AI) based video gait analysis system.

Methods: We initially enrolled 38 hemodialysis patients, two were excluded due to clothing interference with video analysis (27.8% female, 72.2% male). AI-driven gait analysis was performed immediately before and after dialysis. The system extracted spatiotemporal gait and joint range of motion. Statistical analyses included the Shapiro-Wilk test for normality, Wilcoxon signed-rank tests for non-normally distributed data, and paired t-tests for normally distributed data (P < 0.05).

Results: Gait speed (0.59 m/sec pre-dialysis) remained unchanged post-dialysis (P = 0.876), as did cycle length and time. However, step length significantly decreased post-dialysis (P = 0.001), suggesting a more conservative gait pattern. Knee flexion and extension increased slightly but did not reach statistical significance.

Conclusions: Dialysis does not acutely affect overall gait speed but significantly reduces stride length. Post-dialysis fatigue or hemodynamic shifts may alter walking patterns, highlighting the need for fall prevention strategies and physical rehabilitation interventions in dialysis care. AI-based gait analysis may provide a practical tool for monitoring mobility changes in hemodialysis patients.

Artificial intelligence (AI) has become the emblem of progress. We are told it learns faster, sees patterns invisible to the human eye, and will soon outthink us in every domain, from finance to philosophy, from language to life. In medicine, where decisions carry the weight of saving lives, this narrative has gained traction. Algorithms promise precision without fatigue, accuracy without bias, and reproducibility without emotion. Yet, sometimes, the data tell a quieter story.

Artificial intelligence (AI) and machine learning have moved to the forefront of scientific discourse and clinical medicine, offering improved accuracy and efficiency while raising concerns about transparency, accountability, and unintended consequences. Recent developments, particularly large-scale and generative models, have fueled these debates. However, efforts to mimic aspects of human intelligence long predate ChatGPT. These efforts include the early rule-based systems to Weizenbaum’s ELIZA program, which humorously simulated a Rogerian psychotherapist in its Doctor script [1]. For clinicians, the real test is not whether predictions become marginally more accurate on average, but whether they improve the identification of high-risk patients and meaningfully change management.

Medical education has evolved significantly since the establishment of university-based training in the 12th century, continually adapting to scientific, technological, and societal changes. In the 21st century, rapid advances in digital technologies, artificial intelligence, and the widespread availability of medical information are reshaping the competencies required of physicians. This review explores the concept of the doctor of the future and the implications for medical education and training. Future physicians will require strong cognitive abilities to interpret clinical data, utilize decision support technologies, and make informed clinical judgments while maintaining responsibility for final clinical decisions. At the same time, lifelong learning will be essential as medical knowledge and technologies rapidly evolve. Despite technological progress, core human qualities including empathy and effective communication, are essential traits that will be needed by future physicians. These skills require educational models that integrate technological competence, humanistic care, interdisciplinary teamwork, and adaptable career pathways to meet the challenges of evolving healthcare systems.

Background: Large language models (LLMs) are rapidly advancing, with the potential to improve healthcare. While LLM performance on medical licensing exams were studied extensively, their performance in rheumatology exams requires specific evaluation.

Objective: To assess Chat Generative Pre-Trained Transformer (ChatGPT) performance on 200 validated Israeli rheumatology board exam questions.

Methods: ChatGPT performance was evaluated using 200 multiple-choice questions from the 2023 and 2024 Israeli official rheumatology board examinations. Three gpt-4-turbo based variants were assessed: base model (Model 1), few-shot chain of thought (CoT) model (Model 2), and knowledge-augmented prompting model incorporating rheumatology guidelines (Model 3). Model 1 was assessed using both the original Hebrew and a validated English translated version, while Models 2 and 3 were assessed using the English version only.

Results: Overall, Model 3 achieved the highest numerical accuracy (81%), followed by Model 1 (English, 77%), Model 2 (75%), and Model 1 (Hebrew, 74.5%); however, these differences were not statistically significant. Performance varied markedly by question type. For text-only questions (n=177), accuracies ranged from 78.5% to 83.1%, with Model 3 showing the highest point estimate (83.1%). In contrast, all models demonstrated substantially lower performance on questions that included images (n=23), with accuracies ranging from 34.8% to 65.2%. Model 3 yielded the highest numerical accuracy (65.2%).

Conclusions: The study highlights the potential role of LLMs in rheumatology board examinations but also emphasizes their critical limitations. Future research should focus on addressing limitations, especially image interpretation and management of complex cases to enable efficient application of LLMs in rheumatology.

During these challenging times, following the October 7th terrorist attacks and the ongoing Iron Swords war, there is a greater need to strengthen the Israeli Society of Plastic and Aesthetic Surgery. Prof. Josef Haik, chair of the Israeli Society of Plastic and Aesthetic Surgery, leads this effort. In addition, it is vital to highlight Israel's contributions to the global Plastic and Aesthetic Surgery community and to encourage collaboration with the nursing division for plastic surgery and burns. Our department is involved in presenting our findings and collaborating with colleagues. In this article, we outlined five main topics: the role of plastic surgery in wartime, reconstructive plastic surgery, aesthetic plastic surgery, microsurgery, and innovation in plastic surgery.

Background: Identifying drug–drug interactions (DDIs) in dermatology can be cumbersome and time-consuming using traditional methods.

Objectives: To explore the potential of ChatGPT-4o, an artificial intelligence (AI)-based chatbot, to streamline the identification process.

Methods: ChatGPT-4o was tasked with assessing DDIs among commonly prescribed dermatological medications. The accuracy and reliability of the chatbot's outputs were compared against established references for 43 interactions.

Results: ChatGPT-4o successfully identified all evaluated interactions. It accurately described the interaction effects in 42 cases, with only one example of misdescription.

Conclusions: The findings highlight the potential of ChatGPT to serve as an effective and efficient alternative for identifying and understanding DDIs in dermatology, despite one noted error that emphasizes the need for ongoing verification against trusted references. Further research is needed to validate its use across a broader range of medications and clinical scenarios.

Artificial Intelligence (AI), particularly large language models (LLMs) like OpenAI's ChatGPT, has shown potential in various medical fields, including pediatrics. We evaluated the utility and integration of LLMs in pediatric medicine. We conducted a search in PubMed using specific keywords related to LLMs and pediatric care. Studies were included if they assessed LLMs in pediatric settings, were published in English, peer-reviewed, and reported measurable outcomes. Sixteen studies spanning pediatric sub-specialties such as ophthalmology, cardiology, otology, and emergency medicine were analyzed. The findings indicate that LLMs provide valuable diagnostic support and information management. However, their performance varied, with limitations in complex clinical scenarios and decision-making. Despite excelling in tasks requiring data summarization and basic information delivery, the effectiveness of the models in nuanced clinical decision-making was restricted. LLMs, including ChatGPT, show promise in enhancing pediatric medical care but exhibit inconsistent performance in complex clinical situations. This finding underscores the importance of continuous human oversight. Future integration of LLMs into clinical practice should be approached with caution to ensure they supplement, rather than supplant, expert medical judgment.

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.