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Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine. Open in new tab Download slide Open in new tab Download slide [ABSTRACT FROM AUTHOR]

AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much data is needed by an AI-based system to learn surgical context with high fidelity? To answer this question, we leveraged a large-scale, diverse, cholecystectomy video dataset. We assessed surgical workflow recognition and report a deep learning system, that not only detects surgical phases, but does so with high accuracy and is able to generalize to new settings and unseen medical centers. Our findings provide a solid foundation for translating AI applications from research to practice, ushering in a new era of surgical intelligence. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of Philanthropy on Cardiovascular Medicine. Topics discussed include fundraisers for cardiovascular medicine by the British Heart Foundation; Governmental initiatives for providing digital cardiovascular health platform, artificial intelligence and machine learning; and role of the Karla Völlm Foundation in the process of governmental funding and research projects.

The purpose of this study was to develop a diagnostic tool to automatically detect temporomandibular joint osteoarthritis (TMJOA) from cone beam computed tomography (CBCT) images with artificial intelligence. CBCT images of patients diagnosed with temporomandibular disorder were included for image preparation. Single-shot detection, an object detection model, was trained with 3,514 sagittal CBCT images of the temporomandibular joint that showed signs of osseous changes in the mandibular condyle. The region of interest (condylar head) was defined and classified into 2 categories—indeterminate for TMJOA and TMJOA—according to image analysis criteria for the diagnosis of temporomandibular disorder. The model was tested with 2 sets of 300 images in total. The average accuracy, precision, recall, and F1 score over the 2 test sets were 0.86, 0.85, 0.84, and 0.84, respectively. Automated detection of TMJOA from sagittal CBCT images is possible by using a deep neural networks model. It may be used to support clinicians with diagnosis and decision making for treatments of TMJOA. [ABSTRACT FROM AUTHOR]

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine learning algorithms is proposed, which only uses non-invasive parameters readily available from ordinary monitors and manual measurement. A new feature selection method based on the Bayes error rate is developed to select valuable features. Based on non-invasive parameters, four machine learning models were trained for early mortality prediction. The subjects contained in this study suffered from general critical diseases including but not limited to cancer, bone fracture, and diarrhea. Comparison tests among five traditional scoring methods and these four machine learning models with and without laboratory measurement variables are performed. Only using the non-invasive parameters, the LightGBM algorithms have an excellent performance with the largest accuracy of 0.797 and AUC of 0.879. There is no apparent difference between the mortality prediction performance with and without laboratory measurement variables for the four machine learning methods. After reducing the number of feature variables to no more than 50, the machine learning models still outperform the traditional scoring systems, with AUC higher than 0.83. The machine learning approaches only using non-invasive parameters achieved an excellent mortality prediction performance and can equal those using extra laboratory measurements, which can be applied in rural areas and remote battlefield for mortality risk evaluation. [ABSTRACT FROM AUTHOR]

Nadie discute los beneficios de la transformación digital en la industria de la salud. Los obstáculos tradicionales se desvanecen y la covid-19 ejerce un poderoso efecto catalizador. Quienes no asuman los cambios corren el riesgo de quedar fuera del mercado. [ABSTRACT FROM AUTHOR]

Die Symposiumsreihe,,Vom Biomarker zur Therapie' der Trillium Akademie bietet einen Einblick in die moderne onkologische Präzisionsmedizin. Im Fokus der diesjährigen Veranstaltung, die wegen der Corona-Pandemie ausschließlich virtuell stattfand, standen neben,,Liquid Profiling' für die Früherkennung und Verlaufskontrolle auch die Digitalisierung der Analytik und das Zusammenspiel von Molekularpathologie und personalisierter Krebstherapie. Trotz der rasanten Entwicklungen finden sich derzeit nur für einen kleinen Teil der Patienten angreifbare Alterationen oder Marker, die sie für eine Immuntherapie qualifizieren. Doch auch in dieser Hinsicht wird die Individualisierung der Therapie weiter voranschreiten. [ABSTRACT FROM AUTHOR]

Background: Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent, and its use with respect to oral oncology is still in the nascent stage.Discussion: A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed.Conclusion: Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image level is needed and future collaborations with computer scientists may progress the field of oral oncology. [ABSTRACT FROM AUTHOR]

Background and Aim: About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options. Methods: Data of 263 patients (mean age 37.0 ± 14.0‐years, 176, 77% male) with acute severe UC admitted during a 12‐year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN). Results: Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C‐reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower. Conclusion: It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors. [ABSTRACT FROM AUTHOR]

Oral cancer is easily detectable by physical (self) examination. However, many cases of oral cancer are detected late, which causes unnecessary morbidity and mortality. Screening of high-risk populations seems beneficial, but these populations are commonly located in regions with limited access to health care. The advent of information technology and its modern derivative artificial intelligence (AI) promises to improve oral cancer screening but to date, few efforts have been made to apply these techniques and relatively little research has been conducted to retrieve meaningful information from AI data. In this paper, we discuss the promise of AI to improve the quality and reach of oral cancer screening and its potential effect on improving mortality and unequal access to health care around the world. [ABSTRACT FROM AUTHOR]

Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and validated deep learning models to automatically classify cervical neoplasms on colposcopic photographs. Pre-trained convolutional neural networks were fine-tuned for two grading systems: the cervical intraepithelial neoplasia (CIN) system and the lower anogenital squamous terminology (LAST) system. The multi-class classification accuracies of the networks for the CIN system in the test dataset were 48.6 ± 1.3% by Inception-Resnet-v2 and 51.7 ± 5.2% by Resnet-152. The accuracies for the LAST system were 71.8 ± 1.8% and 74.7 ± 1.8%, respectively. The area under the curve (AUC) for discriminating high-risk lesions from low-risk lesions by Resnet-152 was 0.781 ± 0.020 for the CIN system and 0.708 ± 0.024 for the LAST system. The lesions requiring biopsy were also detected efficiently (AUC, 0.947 ± 0.030 by Resnet-152), and presented meaningfully on attention maps. These results may indicate the potential of the application of AI for automated reading of colposcopic photographs. [ABSTRACT FROM AUTHOR]

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The databases of the Ministry of Public Health of Ecuador in the 2013-2017 period contain valuable information that can be used to determine the strengths, weaknesses, potential problems, among others, that affect the public health of the country. This knowledge can serve to draw better public health policies. This paper aims to propose a methodology that allows us to extract knowledge from these databases and at the same time to obtain association rules based on the combination of algorithms such as FP-growth and k-means. In summary, the methodology consists of the following steps: first, the dataset is stored in 5 files in the SPSS (Statistical Package for the Social Sciences) format, and then the disease-related attributes are grouped and encoded, according to the code ICD-10, for this purpose it is proposed to apply the WEKA software. Finally, the FP-Growth algorithm is used to extract association rules from frequent items with the support of RAPIDMINER, which has the advantage of allowing us the use of WEKA algorithms. The methodology is illustrated with an example that shows how to use it and its usefulness to extract association rules in real-life situations from medical databases. With these representations of the information, morbidity and incidence behavior analysis of the registered groups and diseases can be made. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including the importance of incorporating identity into moral psychology; artificial intelligence in clinical neuroscience; and environmental ethics and uncertainty.

The article examines the legal and medical issues involving topics like informed consent and the use of artificial intelligence (AI) and machine learning (ML) in the treatment of patients in the U.S.

Aim: To verify the diagnostic performance of an artificial intelligence system based on the deep convolutional neural network method to detect periapical pathosis on cone‐beam computed tomography (CBCT) images. Methodology: images of 153 periapical lesions obtained from 109 patients were included. The specific area of the jaw and teeth associated with the periapical lesions were then determined by a human observer. Lesion volumes were calculated using the manual segmentation methods using Fujifilm‐Synapse 3D software (Fujifilm Medical Systems, Tokyo, Japan). The neural network was then used to determine (i) whether the lesion could be detected; (ii) if the lesion was detected, where it was localized (maxilla, mandible or specific tooth); and (iii) lesion volume. Manual segmentation and artificial intelligence (AI) (Diagnocat Inc., San Francisco, CA, USA) methods were compared using Wilcoxon signed rank test and Bland–Altman analysis. Results: The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctly detecting a periapical lesion was 92.8%. The deep convolutional neural network volumetric measurements of the lesions were similar to those with manual segmentation. There was no significant difference between the two measurement methods (P > 0.05). Conclusions: Volume measurements performed by humans and by AI systems were comparable to each other. AI systems based on deep learning methods can be useful for detecting periapical pathosis on CBCT images for clinical application. [ABSTRACT FROM AUTHOR]

The article focuses on entanglement of adverse event tracking, artificial intelligence (AI) tools and pay-for-performance metrics in hospitals. It mentions hospital Boards of Directors must take a forceful ethical stance on role of quality and patient safety as a central goal of hospital actions. It also mentions onslaught of expensive data analytics tools harmed and often undercompensated Medicare patient model needs to be developed and tested that uses Medicare tools to offer compensation.

Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treatment, prognosis, etc.). The study of liver diseases by radiomics will contribute to early diagnosis and treatment of liver diseases and improve survival and cure rates of liver diseases. This field is currently in the ascendant and may have great development in the future. Therefore, we summarize the progress of current research in this article and then point out the related deficiencies and the direction of future research. [ABSTRACT FROM AUTHOR]

Objectives: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data.Materials and Methods: Using an ICU dataset of 13 728 records, we identify clinically significant sepsis subpopulations with distinct organ dysfunction patterns. We perform classification experiments with random forest, gradient boost trees, and support vector machines, using the identified subpopulations to distinguish patients who develop sepsis in the ICU from those who do not.Results: The classification results show that features selected using sepsis subpopulations as background knowledge yield a superior performance in distinguishing septic from non-septic patients regardless of the classification model used. The improved performance is especially pronounced in specificity, which is a current bottleneck in sepsis prediction machine learning models.Conclusion: Our findings can steer machine learning efforts toward more personalized models for complex conditions including sepsis. [ABSTRACT FROM AUTHOR]

The article discusses the potential benefits and legal issues concerning the use of technology and artificial intelligence in the U.S. health care sector. Topics include the issues on the cost, quality, and access to care in the sector, the potential of artificial intelligence technology in enhancing quality of care and medical research, and the possible application of tort liability laws in regulating medical technology.

Aim of the study: To develop a platform designed for glaucoma screening, based on deep learning algorithms, for the diagnosis of glaucomatous optic neuropathy from colour fundus images and intraocular pressure, not requiring medical staff. Material and methods: A modular platform for glaucoma screening is developed which uses classifiers that independently evaluate the parameters of the visual system. The fundus image classifier is based on trainable mathematical models, while an intraocular pressure classifier is a threshold classifier. Performance analysis is conducted in terms of the statistical parameters: sensitivity, accuracy, precision, and specificity. Glaucoma images were classified by two experts. The cut-off of vertical cup to disc ratio (vCDR) for glaucoma was set at = 0.7. In the training stage 933 healthy and 754 glaucoma images were used. If the intraocular pressure (IOP) was = 24 mmHg in at least one eye the patient was classified in the glaucoma category independently of the fundus image category. During the training stage the following parameters of the image classifier were achieved: sensitivity 0.82 and specificity 0.63. Results and conclusions: For the test data from two campaigns were used (total 1104 fundus images). For the image classifier, sensitivity 0.73 and specificity 0.83 were obtained for the first campaign, while sensitivity 0.84 and specificity 0.67 were obtained for the second campaign. The final achieved parameters of the platform are: sensitivity 0.79 and specificity 0.67 for the first campaign, sensitivity 0.92 and specificity 0.42 for the second campaign. The results are in accordance with other studies and the platform proved its usability and good performance. [ABSTRACT FROM AUTHOR]

Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning. We provide an overview of current machine learning applications in rheumatology, mainly supervised learning methods for e-diagnosis, disease detection and medical image analysis. In the future, machine learning will be likely to assist rheumatologists in predicting the course of the disease and identifying important disease factors. Even more interestingly, machine learning will probably be able to make treatment propositions and estimate their expected benefit (e.g. by reinforcement learning). Thus, in future, shared decision-making will not only include the patient's opinion and the rheumatologist's empirical and evidence-based experience, but it will also be influenced by machine-learned evidence. [ABSTRACT FROM AUTHOR]

Vertebral fractures are a common problem and the most prevalent of thoracolumbar compression and burst fractures. However, vertebral fractures are difficult to diagnose: an experienced orthopedist or radiologist is required to detect and determine the type of vertebral fracture. Thus, artificial intelligence methods for diagnosing vertebral fractures are clinically useful. On the basis of a review of 12 studies in the literature, the earliest of which was published in 2020, we propose a machine learning model that detects and determines the type of vertebral fracture on the basis of X-ray data. In this method, YOLOv4 and ResUNet are used to segment vertebral bodies from X-ray images. In evaluation experiments, our method had a precision of 99%, 74%, and 94% in identifying healthy vertebrae, compression fractures, and burst fractures, respectively. • Proposed AI model for vertebral fracture detection using YOLOv4 and ResUNet. • Achieved high precision (99%, 74%, 94%) in identifying healthy, compression, and burst fractures. • First literature mention of detecting compression and burst fractures using X-ray images. • Surveyed deep learning methods for vertebral fracture diagnosis using X-ray data. • Model outperformed counterparts with 85.2% Dice coefficient for segmentation. [ABSTRACT FROM AUTHOR]

Introduction: A crisis is the result of natural or human disasters that can affect the health of a country. Therefore, managing the crisis as quickly and efficiently as possible has become a kind of goal; A goal that can be approached more with the help of artificial intelligence. In this research, one of the common scenarios in the crisis, i.e. lack of food, and how to manage it with the help of artificial intelligence have been investigated. Methods: In the present study, the keywords Nutrition, Artificial Intelligence, Disaster Management, Natural Disaster, and Nutrition Surveys were searched in PubMed, Google Scholar, and Scopus in the time range of 2014 to 2024. Results: One of the most important issues in the crisis is the management of nutritional needs. According to the experiences that have been made in this field so far; Different methods of management style can be implemented, one of the most recent of which is management based on artificial intelligence. Studies have shown that AI is useful in predicting the occurrence of disasters, and food insecurity and describing the current food situation. Another study points to AI's ability to recognize the nutritional needs of the population, which begins to make decisions with broader visibility and align with new nutritional guidelines by prioritizing age and high-risk groups, considering the region's policies and facilities. Conclusions: The use of artificial intelligence in crisis management, especially the management of food resources, has already been done successfully. Although it still has limitations. Fortunately, compared to traditional methods, it is faster and more efficient in decision-making and constantly develops. [ABSTRACT FROM AUTHOR]

Obesity, as one of the leading causes of preventable death, is a complex disease and needs intelligent intervention. Artificial intelligence (AI), as a multidisciplinary field of study using tools such as machine learning, fuzzy systems, and optimization algorithms, prepares a variety of intelligent facilities regarding obesity curing and management. AI is used in identifying personalized health assessments, tailored interventions, predictive analytics, obesity surgery, handling BMI,s shortcomings, virtual weight management, early metabolic disorder detection, obesity-related chronic and cancer diseases, and obesity drug discovery. These intelligent analyses are performed by analyzing clinical, laboratory, genetic, medical images, lifestyle, and behavioral data. AI-based systems can offer continuous monitoring, cognitive motivation, and reminders while performing precise body composition assessments. Hence, considering obesity as a multifaceted disease, intelligent therapy and management through AI-based systems are recommended. [ABSTRACT FROM AUTHOR]

The article focuses on the "MedtecSUMMIT im Fokus" series, organized by the innovation network GESUNDHEIT of Bayern Innovativ on behalf of the Bavarian Ministry of Economic Affairs, with the first event in 2024 focusing on "Clinical Robotics" to be held on March 19, 2024.

Introduction: Drug discovery is a complex and time-consuming process that requires significant resources. The purpose of this study is to investigate the potential of artificial intelligence in drug discovery Search Method: Various databases, including PubMed, Scopus, and Web of Science, were searched for articles published between 2015 and 2022 on AI's role in drug discovery. The criteria for entering the articles into the study were the use of artificial intelligence such as machine learning and deep learning in the discovery of new drugs. Results: In our review, we identified several studies that demonstrated AI's effectiveness in drug discovery. The use of AI techniques has been applied to predicting the binding affinity of small molecules to target proteins, identifying novel drug targets, designing new drug molecules, and optimizing drug candidates. Using AI has also reduced the time and cost associated with drug discovery significantly. While AI has shown promise in drug discovery, there are still several challenges that need to be addressed. One of the main challenges is the lack of high-quality data. Drug discovery datasets are often small and biased, which can limit the effectiveness of AI techniques. Another challenge is the interpretability of AI models. Conclusion: AI has the potential to revolutionize the drug discovery process. The use of AI techniques can lead to the discovery of new drugs that are more effective and have fewer side effects. DSP-1181, which was created using artificial intelligence (AI), has entered a Phase I clinical trial for the treatment of obsessive-compulsive disorder (OCD). DSP-1181 is a long-acting, potent serotonin 5-HT1A receptor agonist. However, there are still several challenges that need to be addressed before AI can become a mainstream tool in drug discovery. Further research is needed to address these challenges and to fully realize the potential of AI in drug discovery. [ABSTRACT FROM AUTHOR]

Knowing about the internal state of the body that is not under our conscious control and continuing to control it has always been a goal for development in the science. Today, artificial intelligence has been able to bring us closer to this goal with diverse and powerful algorithms and based on data that has been extracted over the years on various human characteristics. One of the important issues in human life is stress. The activity of the stress system actually affects all the organs and functions of them. A small part of understanding the activity of this system is at the conscious level, but most of it is at the physiological and unconscious level, which is affecting the functions of other parts of our body, especially the brain. We at the Neuroscience Research Center of Baqiyatollah University of Medical Sciences during seven years collected extensive data from Iranians. The various electrophysiological indicators from heart and brain recordings and hormonal indicators, especially salivary cortisol concentration in different conditions without stress and degrees Different types of stress were collected. We were able to reach the successful classification algorithms of cortisol level as the golden standard of stress in optimal and non-optimal condition with 99% power and even prediction of amount of the salivary cortisol concentration with an error of about 8%. We also designed a software according to the findings that tells us the state of the stress system by taking the heart rate for one minute. [ABSTRACT FROM AUTHOR]

This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Zafar et al.)

Background While large language models show potential as beneficial tools in medicine, their reliability, especially in the realm of obstetrics and gynecology (OB-GYN), is not fully comprehended. This study seeks to measure and contrast the performance of ChatGPT and HuggingChat in addressing OB-GYN-related medical examination questions, offering insights into their effectiveness in this specialized field. Methods ChatGPT and HuggingChat were subjected to two standardized multiple-choice question banks: Test 1, developed by the National Board of Medical Examiners (NBME), and Test 2, gathered from the Association of Professors of Gynecology & Obstetrics (APGO) Web-Based Interactive Self-Evaluation (uWISE). Responses were analyzed and compared for correctness. Results The two-proportion z-test revealed no statistically significant difference in performance between ChatGPT and HuggingChat on both medical examinations. For Test 1, ChatGPT scored 90%, while HuggingChat scored 85% (p = 0.6). For Test 2, ChatGPT correctly answered 70% of questions, while HuggingChat correctly answered 62% of questions (p = 0.4). Conclusion Awareness of the strengths and weaknesses of artificial intelligence allows for the proper and effective use of its knowledge. Our findings indicate that there is no statistically significant difference in performance between ChatGPT and HuggingChat in addressing medical inquiries. Nonetheless, both platforms demonstrate considerable promise for applications within the medical domain., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Kirshteyn et al.)

Objectives: This study presents a design framework to enhance the accuracy by which large language models (LLMs), like ChatGPT can extract insights from clinical notes. We highlight this framework via prompt refinement for the automated determination of HEART (History, ECG, Age, Risk factors, Troponin risk algorithm) scores in chest pain evaluation., Methods: We developed a pipeline for LLM prompt testing, employing stochastic repeat testing and quantifying response errors relative to physician assessment. We evaluated the pipeline for automated HEART score determination across a limited set of 24 synthetic clinical notes representing four simulated patients. To assess whether iterative prompt design could improve the LLMs' ability to extract complex clinical concepts and apply rule-based logic to translate them to HEART subscores, we monitored diagnostic performance during prompt iteration., Results: Validation included three iterative rounds of prompt improvement for three HEART subscores with 25 repeat trials totaling 1200 queries each for GPT-3.5 and GPT-4. For both LLM models, from initial to final prompt design, there was a decrease in the rate of responses with erroneous, non-numerical subscore answers. Accuracy of numerical responses for HEART subscores (discrete 0-2 point scale) improved for GPT-4 from the initial to final prompt iteration, decreasing from a mean error of 0.16-0.10 (95% confidence interval: 0.07-0.14) points., Conclusion: We established a framework for iterative prompt design in the clinical space. Although the results indicate potential for integrating LLMs in structured clinical note analysis, translation to real, large-scale clinical data with appropriate data privacy safeguards is needed., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Authors. Journal of the American College of Emergency Physicians Open published by Wiley Periodicals LLC on behalf of American College of Emergency Physicians.)

Introduction: Large language models (LLMs) have transformed various domains in medicine, aiding in complex tasks and clinical decision-making, with OpenAI's GPT-4, GPT-3.5, Google's Bard, and Anthropic's Claude among the most widely used. While GPT-4 has demonstrated superior performance in some studies, comprehensive comparisons among these models remain limited. Recognizing the significance of the National Board of Medical Examiners (NBME) exams in assessing the clinical knowledge of medical students, this study aims to compare the accuracy of popular LLMs on NBME clinical subject exam sample questions., Methods: The questions used in this study were multiple-choice questions obtained from the official NBME website and are publicly available. Questions from the NBME subject exams in medicine, pediatrics, obstetrics and gynecology, clinical neurology, ambulatory care, family medicine, psychiatry, and surgery were used to query each LLM. The responses from GPT-4, GPT-3.5, Claude, and Bard were collected in October 2023. The response by each LLM was compared to the answer provided by the NBME and checked for accuracy. Statistical analysis was performed using one-way analysis of variance (ANOVA)., Results: A total of 163 questions were queried by each LLM. GPT-4 scored 163/163 (100%), GPT-3.5 scored 134/163 (82.2%), Bard scored 123/163 (75.5%), and Claude scored 138/163 (84.7%). The total performance of GPT-4 was statistically superior to that of GPT-3.5, Claude, and Bard by 17.8%, 15.3%, and 24.5%, respectively. The total performance of GPT-3.5, Claude, and Bard was not significantly different. GPT-4 significantly outperformed Bard in specific subjects, including medicine, pediatrics, family medicine, and ambulatory care, and GPT-3.5 in ambulatory care and family medicine. Across all LLMs, the surgery exam had the highest average score (18.25/20), while the family medicine exam had the lowest average score (3.75/5).  Conclusion: GPT-4's superior performance on NBME clinical subject exam sample questions underscores its potential in medical education and practice. While LLMs exhibit promise, discernment in their application is crucial, considering occasional inaccuracies. As technological advancements continue, regular reassessments and refinements are imperative to maintain their reliability and relevance in medicine., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Abbas et al.)

Artificial intelligence (AI) has become a revolutionary influence in the field of ophthalmology, providing unparalleled capabilities in data analysis and pattern recognition. This narrative review delves into the crucial role that AI plays, particularly in the context of anterior segment diseases with a genetic basis. Corneal dystrophies (CDs) exhibit significant genetic diversity, manifested by irregular substance deposition in the cornea. AI-driven diagnostic tools exhibit promising accuracy in the identification and classification of corneal diseases. Importantly, chat generative pre-trained transformer (ChatGPT)-4.0 shows significant advancement over its predecessor, ChatGPT-3.5. In the realm of glaucoma, AI significantly contributes to precise diagnostics through inventive algorithms and machine learning models, surpassing conventional methods. The incorporation of AI in predicting glaucoma progression and its role in augmenting diagnostic efficiency is readily apparent. Additionally, AI-powered models prove beneficial for early identification and risk assessment in cases of congenital cataracts, characterized by diverse inheritance patterns. Machine learning models achieving exceptional discrimination in identifying congenital cataracts underscore AI's remarkable potential. The review concludes by emphasizing the promising implications of AI in managing anterior segment diseases, spanning from early detection to the tailoring of personalized treatment strategies. These advancements signal a paradigm shift in ophthalmic care, offering optimism for enhanced patient outcomes and more streamlined healthcare delivery., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Venkatapathappa et al.)

Background: Cochlear implantation is a critical surgical intervention for patients with severe hearing loss. Postoperative care is essential for successful rehabilitation, yet access to timely medical advice can be challenging, especially in remote or resource-limited settings. Integrating advanced artificial intelligence (AI) tools like Chat Generative Pre-trained Transformer (ChatGPT)-4 in post-surgical care could bridge the patient education and support gap., Aim: This study aimed to assess the effectiveness of ChatGPT-4 as a supplementary information resource for postoperative cochlear implant patients. The focus was on evaluating the AI chatbot's ability to provide accurate, clear, and relevant information, particularly in scenarios where access to healthcare professionals is limited., Materials and Methods: Five common postoperative questions related to cochlear implant care were posed to ChatGPT-4. The AI chatbot's responses were analyzed for accuracy, response time, clarity, and relevance. The aim was to determine whether ChatGPT-4 could serve as a reliable source of information for patients in need, especially if the patients could not reach out to the hospital or the specialists at that moment., Results: ChatGPT-4 provided responses aligned with current medical guidelines, demonstrating accuracy and relevance. The AI chatbot responded to each query within seconds, indicating its potential as a timely resource. Additionally, the responses were clear and understandable, making complex medical information accessible to non-medical audiences. These findings suggest that ChatGPT-4 could effectively supplement traditional patient education, providing valuable support in postoperative care., Conclusion: The study concluded that ChatGPT-4 has significant potential as a supportive tool for cochlear implant patients post surgery. While it cannot replace professional medical advice, ChatGPT-4 can provide immediate, accessible, and understandable information, which is particularly beneficial in special moments. This underscores the utility of AI in enhancing patient care and supporting cochlear implantation., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Aliyeva et al.)

Introduction Uncontrolled hypertension significantly contributes to the development and deterioration of various medical conditions, such as myocardial infarction, chronic kidney disease, and cerebrovascular events. Despite being the most common preventable risk factor for all-cause mortality, only a fraction of affected individuals maintain their blood pressure in the desired range. In recent times, there has been a growing reliance on online platforms for medical information. While providing a convenient source of information, differentiating reliable from unreliable information can be daunting for the layperson, and false information can potentially hinder timely diagnosis and management of medical conditions. The surge in accessibility of generative artificial intelligence (GeAI) technology has led to increased use in obtaining health-related information. This has sparked debates among healthcare providers about the potential for misuse and misinformation while recognizing the role of GeAI in improving health literacy. This study aims to investigate the accuracy of AI-generated information specifically related to hypertension. Additionally, it seeks to explore the reproducibility of information provided by GeAI. Method A nonhuman-subject qualitative study was devised to evaluate the accuracy of information provided by ChatGPT regarding hypertension and its secondary complications. Frequently asked questions on hypertension were compiled by three study staff, internal medicine residents at an ACGME-accredited program, and then reviewed by a physician experienced in treating hypertension, resulting in a final set of 100 questions. Each question was posed to ChatGPT three times, once by each study staff, and the majority response was then assessed against the recommended guidelines. A board-certified internal medicine physician with over eight years of experience further reviewed the responses and categorized them into two classes based on their clinical appropriateness: appropriate (in line with clinical recommendations) and inappropriate (containing errors). Descriptive statistical analysis was employed to assess ChatGPT responses for accuracy and reproducibility. Result Initially, a pool of 130 questions was gathered, of which a final set of 100 questions was selected for the purpose of this study. When assessed against acceptable standard responses, ChatGPT responses were found to be appropriate in 92.5% of cases and inappropriate in 7.5%. Furthermore, ChatGPT had a reproducibility score of 93%, meaning that it could consistently reproduce answers that conveyed similar meanings across multiple runs. Conclusion ChatGPT showcased commendable accuracy in addressing commonly asked questions about hypertension. These results underscore the potential of GeAI in providing valuable information to patients. However, continued research and refinement are essential to evaluate further the reliability and broader applicability of ChatGPT within the medical field., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Almagazzachi et al.)

The use of Artificial Intelligence (AI) in medicine has attracted a great deal of attention in the medical literature, but less is known about how to assess the uncertainty of individual predictions in clinical applications. This paper demonstrates the use of Conformal Prediction (CP) to provide insight on racial stratification of uncertainty quantification for breast cancer risk prediction. The results presented here show that CP methods provide important information about the diminished quality of predictions for individuals of minority racial backgrounds.

The recent integration of the latest image generation model DALL-E 3 into ChatGPT allows text prompts to easily generate the corresponding images, enabling multimodal output from ChatGPT. We explored the feasibility of DALL-E 3 for drawing a 12-lead ECG and found that it can draw rudimentary 12-lead electrocardiograms (ECG) displaying some of the parameters, although the details are not completely accurate. We also explored DALL-E 3's capacity to create vivid illustrations for teaching resuscitation-related medical knowledge. DALL-E 3 produced accurate CPR illustrations emphasizing proper hand placement and technique. For ECG principles, it produced creative heart-shaped waveforms tying ECGs to the heart. With further training, DALL-E 3 shows promise to expand easy-to-understand visual medical teaching materials and ECG simulations for different disease states. In conclusion, DALL-E 3 has the potential to generate realistic 12-lead ECGs and teaching schematics, but expert validation is still needed., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Zhu et al.)

Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Daher et al.)