Israel Medical Association Journal

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.

This mini review includes two case descriptions. It introduces the use of chicken egg yolk antibody (IgY) solutions in the prevention and cure of viral and bacterial infections. Application for the protection against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), rotavirus, and influenza viruses, as well as for the eradication of Pseudomonas aeruginosa, caries, various enteric bacteria and other pathogens, and toxins have been developed. This approach is a fast, reliable, safe, and tested method for producing molecular shield and protection against emerging pathogens and epidemics. In the current pandemic situation caused by coronavirus disease-2019 (COVID-19), this method of passive immunization could be applied for rapid protection against modifiable agents. The specific IgY antibodies start to accumulate into egg yolks about 3 weeks after the immunization of the chicken. The product can be collected safely, as the antigen is not found in the eggs. This method for microbial safety uses natural means and commonly used food substances, which have been tested and could be produced for both blocking epidemics and applying personalized medicine

Psoriatic arthritis (PsA) is a chronic inflammatory condition associated with skin psoriasis and manifests a wide clinical phenotype, with proposed differences between sexes. Current treatments are based on traditional disease-modifying anti-rheumatic drugs (DMARD), and biologic agents and studies have reported different clinical response patterns depending on sex factors. We aimed to identify sex differences in drug retention rate in patients with PsA and performed a systematic research on MEDLINE, EMBASE and Cochrane databases (1979 to June 2015) for studies regarding effectiveness (measured as drug retention rate) in PsA in both traditional DMARDs and biologics. Demographic data as well as retention rates between sexes were extracted. From a total 709 retrieved references, we included 9 articles for the final analysis. Only one study reported data regarding DMARDs, while eight studies reported retention rate for anti-tumor necrosis factor (TNF) biologics, mainly infliximab, adalimumab and etanercept. No differences were reported in retention rates between sexes for methotrexate, while women manifested lower retention rates compared to men with regard to anti-TNF. We highlight the need to include sex differences in the management flow chart of patients with PsA.

Background: Predictive biomarkers for personalized treatment of neoplasms are suggested to be a major advancement in oncology and are increasingly used in clinical practice, albeit based on level II evidence. Target Now® (TN) employs immunostaining and RNA expression on tumor samples to identify potentially beneficial or ineffective drugs. 

Objectives: To explore retrospectively the predictive value of TN for patients with colorectal and gastric carcinomas. 

Methods: The study group comprised colorectal and gastric carcinoma patients with TN test reports. We identified chemotherapy regimens given for stage IV disease for which TN reports indicated prediction. Protocols were classified as having clinical benefit (CB; i.e., stable disease or any objective response) or progressive disease, and this was compared with the TN prediction. 

Results: Nineteen patients – 12 colorectal and 7 gastric carcinomas – met the inclusion criteria. There were 26 evaluable treatment protocols; of 18 with a CB 15 were predicted to have a CB while 3 were predicted to have a lack of CB. Of eight protocols that had no CB, seven were predicted to have a CB and one was predicted to have a lack of CB. A chi-square test was non-significant (P = 0.78). An exploratory analysis yielded a positive predictive value of 68% and a sensitivity of 83% for the TN test. 

Conclusions: This study emphasizes the need for larger multicenter studies to validate the TN test before it is adopted into clinical practice.