Israel Medical Association Journal

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.

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.

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.

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.

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.

Background: Pediatric patients with newly diagnosed type 1 diabetes mellitus (T1DM) are commonly treated with daily multiple insulin injections or an insulin pump. They tend to have higher body mass index-standard deviation scores (BMI-SDS) than non-diabetic children.

Objectives: To identify patterns in the changes in BMI in the 3 years after T1DM diagnosis, and to discover factors that relate to excessive weight gain.

Methods: This retrospective study included clinical and laboratory data for 194 boys and girls aged 2–18 years at the time of diagnosis and at 1, 2, and 3 years after. Their BMI values were compared to non-diabetic children using BMI percentile and z-score (standard deviation) based on the U.S. Centers for Disease Control and Prevention (CDC) growth charts.

Results: Both males and females had low mean BMI-SDS at diagnosis (-0.4499 ± 1.38743 male, 0.3050 ± 1.29887 female) that increased after 1 year (-0.0449 ± 1.14772 male, 0.1451 ± 0.98893 female). Lower glycated hemoglobin (HbA1c) at 1 year correlated with higher BMI-SDS (r = -0.215, P = 0.011). No such correlation was found in the following 2 years. The daily dose of basal insulin correlated with higher BMI-SDS at 1 year (r = 0.183, P = 0.026) and 3 years (r = 0.297, P < 0.01). No association was found between the use of an insulin pump or continuous glucose monitoring and higher BMI-SDS.

Conclusions: BMI-SDS of children with T1DM was lower than average at the time of diagnosis and rose higher than average in the 3 years following. Higher BMI-SDS was not significantly associated with sex or ethnicity. The most prominent increase happened in the first year.

Background: Clinical dysentery causes hundreds of thousands of deaths annually worldwide. However, current recommendations reserve antibiotics for those either clinically sick or with highly suspected cases of shigellosis. This treatment stems from rising antibiotic resistance. Children diagnosed with clinical dysentery in the pediatric emergency department (PED) are regarded more cautiously.

Objectives: To explore the use of antibiotics in children diagnosed with clinical dysentery in the PED.

Methods: A retrospective case study of children with clinical dysentery at a single PED during the years 2015 and 2018. Demographics as well as clinical findings were compared to culture results and antibiotic treatment.

Results: The study included 281 children who were diagnosed with clinical dysentery during the study period; 234 (83%) were treated with antibiotics. However, cultures were positive in only 162 cases (58%). Only 32% were Shigella spp. Younger age, fever, and leukocytosis were related to antibiotic treatment.

Conclusions: The diagnosis of clinical dysentery is misgiven commonly in the PED leading to widespread use of antibiotics when not indicated. This treatment may impact antibiotic resistance patterns. Further studies and interventions are necessary to create clear guidelines in the PED setting.

Background: Severe asthma affects up to 20,000 citizens of Israel. Novel biological therapies, which individually have been proven to reduce asthma morbidity in clinical trials, have become available in recent years. Comparative data among different drugs are scarce.

Objectives: To describe and compare the clinical outcomes of biological therapies in severe asthma patients treated at Shamir Medical Center.

Methods: We conducted a cohort study based on a review of cases treated with monoclonal antibodies for severe asthma at our center. Data were extracted for demographics, eosinophil count, lung function (FEV1), exacerbation rate, and median dose of oral prednisone. Between-drug comparison was conducted by repeated measures ANOVA.

Results: The cohort included 62 patients receiving biological therapy. All biologic drugs were found to reduce exacerbation rate [F(1, 2) = 40.4, P < 0.0001] and prednisone use [F(1, 4) = 16, P < 0.001] significantly. ANOVA revealed no difference of efficacy endpoints between the different drugs. Eosinophil count was significantly reduced post-biologic treatment in the anti-interleukin-5 agents (P < 0.001) but not under treatment with omalizumab and dupilumab.

Conclusions: All of the biological therapies were effective for improving clinical outcomes. None of the agents was clearly superior to any other. These data emphasize the need for severe asthma patients to be seen by pulmonary medicine specialists and offered, where appropriate, biological therapies.