artificial intelligence

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Diabetic retinopathy (DR), a diabetes complication, causes blindness by damaging retinal blood vessels. While deep learning has advanced DR diagnosis, many models face issues like inconsistent performance, limited datasets, and poor interpretability, reducing their clinical utility.
This research aimed to develop and evaluate a deep learning structure combining Convolutional Neural Networks (CNNs) and transformer architecture to improve the accuracy, reliability, and generalizability of DR detection and severity classification.
This computational experimental study leverages CNNs to extract local features and transformers to capture long-range dependencies in retinal images. The model classifies five types of retinal images and assesses four levels of DR severity. The training was conducted on the augmented APTOS 2019 dataset, addressing class imbalance through data augmentation techniques. Performance metrics, including accuracy, Area Under the Curve (AUC), specificity, and sensitivity, were used for metric evaluation. The model’s robustness was further validated using the IDRiD dataset under diverse scenarios.
The model achieved a high accuracy of 94.28% on the APTOS 2019 dataset, demonstrating strong performance in both image classification and severity assessment. Validation on the IDRiD dataset confirmed its generalizability, achieving a consistent accuracy of 95.23%. These results indicate the model’s effectiveness in accurately diagnosing and assessing DR severity across varied datasets. 
The proposed Artificial intelligence (AI)-powered diagnostic tool improves diabetic patient care by enabling early DR detection, preventing progression and reducing vision loss. The proposed AI-powered diagnostic tool offers high performance, reliability, and generalizability, providing significant value for clinical DR management.

It has been more than six decades since Artificial Intelligence (AI) was introduced to outperform humans in accuracy and speed. Ever since, many algorithms have been developed that gained success in fulfilling the claims of AI. Their high speed and accuracy have made them perfect candidates for substituting humans in many settings. Even though AI has changed the face of many industries, its application in some others is still a subject of debate. Besides AI, which, over decades, has changed the face of industries for good, there is another phenomenon that has changed it: the novel coronavirus (COVID-19) pandemic. Unlike AI, this pandemic has changed every aspect of human life. Due to its fast spread through human contact, stay-inshelter orders have been placed to slow the person-to-person transmission. This is a source of concern for industries as many of them may fade away due to the pandemic. However, there is one industry at risk of burning out rather than fading away: the healthcare industry. Limited resources, on the one hand, and increased demand, on the other hand, have made the healthcare industry one of the main victims of the pandemic. Emergency departments are flooded with patients, yet non-emergent medical services have been nearly shut down. Therefore, solutions are sought to help both lighten the burden on emergency departments and facilitate providing non-emergent medical services. AI and automated systems can be the key to such solutions. They have proved their efficacy in many instances in the healthcare industry, from emergency department triage to assisting surgeries. Thus far, their widespread use has been halted due to legal and ethical debates. However, the COVID-19 pandemic can be a turning point in the integration of AI into the healthcare system, just as improving AI integration with healthcare can be a turning point in this pandemic. Herein, we overview how AI can help deliver non-emergent medical services during the pandemic and possibly thereafter

Cholecystitis, inflammation of the gallbladder, has created a significant burden globally. The prevailing paradigm of awaiting irreversible damage before surgically removing the gallbladder is not only traumatic and risky but also fails to address the underlying causes. However, exponential growth in scientific insights now promises a shift towards precision prevention and cure.

Pathophysiological mechanisms involved in cystic duct obstruction by gallstones cause gallbladder stasis, direct mucosal injury, vascular compromise, leukocyte infiltration, and secondary infection. Advanced imaging, multiomics profiling, and machine learning unpack early molecular events in lithogenesis and inflammation missed by traditional techniques. Revolutionary endoluminal interventions, anti-inflammatory pharmaceuticals, antibiotic-eluting stents and medications targeting root lithogenic pathways offer alternatives to surgery for drainage restoration and stone prevention. Lifestyle optimization guided by nutrigenomics and pharmacogenomics increasingly personalizes risk factor modification. Ongoing exponential technological gains in nanomedicine, artificial intelligence integration, and minimally invasive techniques promise further preemptive, curative, non-surgical paradigms addressing pathogenesis at the molecular source.

Analysis revealed previously unidentified molecular signatures in early-stage cholecystitis, enabling intervention before irreversible damage occurs. Personalized risk factor modification guided by nutrigenomic and pharmacogenomic profiling demonstrated significant preventive potential. Integration of artificial intelligence with nanomedicine technologies showed promising results in predicting and preventing stone formation. Novel non-surgical interventions achieved 73% success in restoring drainage and preventing stone formation, with significantly lower complication rates compared to traditional cholecystectomy.

Synergizing breakthroughs across domains now position cholecystitis management for a monumental shift from reactive treatment on macroscopic changes towards proactive precision prevention, cure, and health maintenance by comprehending and controlling the underlying molecular cascades. This transformation promises dramatic improvements in patient safety, quality of life, mortality and healthcare economics.

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.

 The health insurance sales network is one of the essential pillars of the health insurance industry, and redefining this role can significantly impact the performance and effectiveness of health insurance companies. The main objective of this research is to identify and analyze the challenges and opportunities involved in redefining the role of the health insurance sales network in the era of digital transformation using artificial intelligence (AI). 

To explore the principles of using AI for managing the health insurance sales network, a scoping review technique was employed. A comprehensive search strategy was implemented in the PubMed, Scopus, and Google Scholar databases until the end of August 2024, using keywords related to AI, health insurance, and sales networks. The inclusion criteria encompassed peer-reviewed articles in English that focused on AI applications in health insurance sales, while the exclusion criteria involved studies not directly related to health insurance or AI. The inclusion criteria required peer-reviewed, fully identified studies with abstracts and full texts published by August 2024. The exclusion criteria included unavailable full texts, irrelevant or duplicate articles, and low-quality content. The study selection process followed PRISMA guidelines, including an initial screening of titles and abstracts, followed by a full-text review. A flow diagram illustrating the selection process is provided in the full text.

The results were categorized into seven main areas: The application of AI in identifying potential health insurance customers, automation of health risk assessment, targeting new health insurance products, segmentation of policyholders, predicting customer churn, estimating customer lifetime value (CLV), and predicting healthcare costs. The analysis also considered the limitations of the included studies, such as potential biases in AI algorithms and challenges related to data privacy.

The findings indicate that AI, in addition to increasing efficiency and reducing costs, enhances relationships between health insurance companies and policyholders. Given the specific complexities of medical data and the need for personalized solutions in health insurance, AI can play a crucial role in the digital transformation of this industry. Therefore, investing in AI technologies is essential for health insurance companies to maintain leadership in today's competitive market and provide higher-quality healthcare services to policyholders.

The rise of standardized self-assessment tools and AI-driven preoperative evaluations lacks full acknowledgment of their limitations. A male patient with gynecomastia developed a hematoma post-liposuction and subcutaneous mastectomy, revealing undisclosed von Willebrand Disease. Thorough in-person anamnesis surpasses self-administered questionnaires by capturing implicit cues and deeper patient insights.

Today, artificial intelligence (AI) technology has become one of the key tools in many fields. One of the special tools in the field of AI is AI chatbots, which have benefited from advances in deep learning, natural language processing, transformers, and large language models (LLM)[1]. These chatbots based on LLMs are designed to mimic interactive conversations, whereby the user enters a potentially complex request, and the chatbot provides a human-like response. Since their inception, these chatbots have been utilized for various applications, including answering questions, creating explanations and summaries, translating between languages, and various other tasks involving natural languages [2]. The first models recognized and accepted in this regard were ChatGPT, including GPT-3.5 and GPT-4 models developed by OpenAI, followed by other LLM-based chatbots such as Google Bard and Meta LLaMA.One of the significant uses of these chatbots is to facilitate access to health information. These chatbots can automatically answer users' questions and provide appropriate medical information based on their needs [3]. Preliminary evaluations have shown that large language models have strong semantic and syntactic understanding in many natural languages and can perform natural language processing tasks in response to health-related questions effectively [4]. However, the fundamental question is whether this technology is a valuable opportunity to improve access to health information or a threat that could pose potential problems.

Artificial intelligence (AI) is transforming various medical fields, including pathology, radiology, cardiology, and surgery, by offering innovative therapeutic and interventional solutions. AI is revolutionizing medical research and has become vital in advancing medical technology. This study assessed the role of artificial intelligence in the anesthesiology field.

We search studies in the online databases such as PubMed, Scopus and google scholar. Related records were included in this study.

Its role in enhancing patient care is particularly reassuring, as it allows healthcare professionals to offer superior services through the integration of AI in medicine. Moreover, AI's indispensable role in the progress of the anesthesia field, having made significant contributions across various anesthetic applications since its early adoption, further reinforces this reassurance. Embracing AI in healthcare not only enhances treatment outcomes but also sets the stage for a healthier future. AI applications include screening symptoms, predicting adverse actions, personalizing medication dosages, and streamlining record-keeping, all of which aim to enhance patient outcomes. While integrating AI offers exciting opportunities, challenges such as data quality, technical limitations, ethical considerations, and high costs must be addressed. Inaccurate data can compromise patient safety, and insufficient security measures can lead to privacy breaches. Moreover, legal accountability for AI errors in patient management is complex.

The future is promising, with AI capable of enhancing training and education. It can modernize anesthesia education through chronic pain management tools and realistic training simulations, ultimately preparing the next generation of healthcare providers.

Gene therapy can be employed to treat several disorders, including cancer. Globally, women are more frequently diagnosed with breast cancer than any other cancer type, underscoring the necessity for innovative strategies. Algorithms driven by artificial intelligence can enhance the gene therapy process for breast cancer by analyzing vast data sets, identifying intricate patterns, and classifying those patterns. This project aims to perform a literature evaluation focusing on the therapeutic uses of artificial intelligence in gene therapy for breast cancer.

For the aim of this study, data was gathered by reading previously published articles and searching the PubMed database for phrases that were relevant to the question being investigated.

The AI-driven algorithm analyzes complex molecular pathways in the human body, replicates the knowledge of scientists and physicians in clinical research, and simulates biological processes related to gene regulation, thereby improving the effectiveness of gene vectors, managing gene and drug delivery parameters, and modeling cellular behavior. This method diminishes medical errors and promotes early disease identification and drug efficacy forecasting, thereby providing patients with optimal results from advanced treatments like gene therapy with minimal side effects.

Over the period of the past decade, a multitude of efforts have been made to deploy various gene therapy procedures for breast cancer patients, to achieve the highest possible level of efficacy while minimizing the risk of adverse consequences. As a result, artificial intelligence is considered to be a powerful tool for improving early diagnosis and efficient gene therapy for breast cancer.

Diabetes Mellitus (DM) stands as one of the most widespread non-infectious diseases globally. Although diagnosis of diabetes is possible with the fasting plasma glucose test after 12-hour fast, once diabetes is diagnosed, it cannot be reversed. Therefore, it is crucial to identify early indicators for predicting diabetes. Presently, DM can be discerned through various methods involving the analysis of human facial features. One method for facial recognition in diabetes relies on experimental evidence, with its accuracy contingent on the skill and expertise of the physician. Another approach involves diagnosis based on facial morphological features. These morphological changes may be attributed to oxidative stress, damage of blood vessels and collagen, edema and craniofacial abnormalities stemming from hyperglycemia. While cephalometric analysis remains the gold standard for diagnosing skeletal craniofacial morphology, it is a costly and technique-sensitive procedure. Facial recognition based on Artificial Intelligence (AI) has proven to be a valuable tool in the diagnosis and screening of diabetes. Its combination of simplicity, accuracy, and cost-effectiveness makes it a promising addition to the healthcare landscape, ultimately contributing to advancements in pre-clinical diagnosis and leading to enhanced patient outcomes. Given the rapid global increase in diabetes, the importance of early detection of diabetes and the limited information about the role of facial recognition in this regard, this study assesses diabetes through facial features using AI approaches.

Dysregulation of thyroid function, manifesting as hyperthyroidism or hypothyroidism, can profoundly impact an individual's overall health. In this context, artificial intelligence (AI) applications have the potential to revolutionize diagnostic approaches, treatment strategies, and patient monitoring.

This study comprehensively reviews the latest literature on AI applications in thyroid functional and autoimmune disorders.

An online search was conducted on databases using search queries crafted with MeSH terms related to AI and thyroid disorders. After screening, studies aligned with our research focus were selected for this narrative review.

Multiple studies have explored the use of AI technologies, including machine learning (ML) and deep learning (DL), to enhance laboratory workflows for thyroid function tests (TFT) and improve the accuracy of TFT interpretation by incorporating clinical data. In imaging, DL-based models have demonstrated the potential to assist less experienced radiologists in interpreting scintigraphy and ultrasound images. Artificial intelligence has also provided valuable insights into identifying diagnostic genes for thyroid-related autoimmune disorders and understanding the effects of environmental factors, such as chemicals, on thyroid gland function. Some ML models have been developed to predict the risk of hypothyroidism following radioiodine therapy (RAI). Furthermore, AI has shown promise in personalized levothyroxine dose adjustments, predicting treatment responses, and accurately diagnosing complications such as thyroid-associated ophthalmopathy (TAO). Finally, ML-based models forecasting the risk of suicide attempts in patients with major depressive disorder (MDD) and predicting pregnancy outcomes, such as gestational diabetes mellitus (GDM) and preterm delivery, based on TFT results, appear beneficial in addressing these significant health issues.

The current state of AI in diagnosing and treating thyroid function disorders is promising, with applications primarily focused on improving diagnostic accuracy, consistency, and personalized treatment approaches. However, challenges remain that prevent these models from fully substituting professionals. Addressing these challenges is crucial to ensure AI effectively contributes to the management of patients with thyroid diseases.

The integration of Artificial intelligence (AI) in biotechnology presents significant ethical challenges that must be addressed to ensure responsible innovations. Key concerns include data privacy and security, as AI systems often handle sensitive genetic and health information, necessitating robust regulations to protect individuals' rights and maintain public trust. Algorithmic bias poses another critical issue; AI can reflect existing biases in training data, leading to inequitable healthcare outcomes. Transparency in AI decision-making is essential, as "black box" models hinder trust, especially in drug discovery and genetics. Ethical implications of genetic manipulation require careful scrutiny to define the limits of human intervention. Additionally, societal impacts must be considered to ensure equitable distribution of AI benefits, preventing the exacerbation of disparities. Engaging diverse stakeholders, including ethicists and policymakers, is vital in aligning these technologies with societal values. Ultimately, prioritizing ethics will allow us to harness AI and biotechnology's potential while safeguarding human rights and promoting equity.