Ability Of Artificial Intelligence Research Articles

Culinary Innovation

The purpose of this paper is to propose artificial intelligence technologies as an innovative tool for designing recipes in culinary artistry. Since artificial intelligence technologies are widely used to generate content, we investigated the creation of recipes and consumers' acceptance of the sensory attributes of the final food product. Without additional guidance, the artificial intelligence program asked to create three recipes using the same ingredients (strawberry, chicken, and vine leaves). To test the overall liking and acceptance of the recipes, the hedonic test is applied to 118 untrained panelists. The results revealed that the overall acceptability scores for all three products are acceptable (≥ 6.0), with the lowest score of 6.58 ± 1.61. Artificial intelligence has shown that it can be an efficient tool for professional chefs to shorten the creativity process and design unimaginable combinations. This research is a pioneering study that measures the reliability and ability of artificial intelligence in creating recipes, which is a culinary creativity process. With the hedonic tasting results, this study also shows the consumers' acceptance of new dishes.

Research on artificial intelligence literacy level and its influencing factors of high school students

With the continuous and deep integration of artificial intelligence technology and school education, students' artificial intelligence educational literacy has become an important part of students' development, and the cultivation and improvement of students' artificial intelligence educational literacy has attracted extensive attention from the government and society. In order to accurately grasp the development status of artificial intelligence literacy of primary and secondary school students in China, and to provide decision-making basis for comprehensively and efficiently improving teachers' intelligent education literacy, this study builds a framework of intelligent education literacy of primary and secondary school teachers with intelligent education awareness, knowledge, skills and ethics based on in-depth comparative analysis of relevant standards, frameworks and existing achievements at home and abroad. The questionnaire of teachers' intelligent educational literacy was compiled to carry out empirical research, and the intelligent educational literacy of primary and secondary school teachers was measured. The study found that students have a high level of artificial intelligence awareness, artificial intelligence affection and ethics, but they still need to improve their artificial intelligence ability and knowledge.

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders.

To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.

Emerging Trends in AI-Driven Health Tech: A Comprehensive Review and Future Prospects

Purpose: The purpose of this research is to explore the integration of artificial intelligence (AI) in healthcare, specifically within the realm of health informatics (HI). The study aims to understand the impact of AI on patient treatment, research, and operational processes within healthcare systems. Additionally, it seeks to address the challenges posed by the increasing volume of unorganized and unstructured data generated by AI technologies in healthcare.
 Materials and Methods: This research employs a comprehensive analysis approach, utilizing complex health information systems, clinical images, and intricate language. It examines the current utilization of AI in healthcare, focusing on its effects on patient and clinician involvement in healthcare decision-making. The assessment emphasizes key skill areas of Health Informatics, including IT, health information systems, security and privacy, telemedicine, m-Health, consumer health informatics, and clinical informatics.
 Findings: The study identifies several significant findings regarding the role of AI in healthcare. It highlights how AI technologies contribute to the generation of unstructured data, posing challenges for research and analysis. Additionally, the research underscores AI's ability to enhance personalized medical guidance, identify complex illnesses, forecast negative health occurrences, and improve patient outcomes. Moreover, it discusses AI's impact on social media and mobile apps, emphasizing its potential to gather valuable insights from online sources for a deeper understanding of patient needs and behaviors.
 Implications to Theory, Practice and Policy: Based on the findings, the research suggests several recommendations for future research and progress in the field of AI usage in healthcare. These recommendations may include further exploration of AI applications in healthcare decision-making, addressing challenges related to unstructured data, enhancing security and privacy measures, and leveraging AI for improving patient outcomes and clinician engagement. Additionally, the study emphasizes the importance of ongoing research and development to maximize the potential benefits of AI in healthcare.

Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation.

Artificial intelligence (AI) has emerged as a powerful tool in various medical fields, including plastic surgery. This study aims to evaluate the performance of ChatGPT, an AI language model, in elucidating historical aspects of plastic surgery and identifying potential avenues for innovation. A comprehensive analysis of ChatGPT's responses to a diverse range of plastic surgery-related inquiries was performed. The quality of the AI-generated responses was assessed based on their relevance, accuracy, and novelty. Additionally, the study examined the AI's ability to recognize gaps in existing knowledge and propose innovative solutions. ChatGPT's responses were analysed by specialist plastic surgeons with extensive research experience, and quantitatively analysed with a Likert scale. ChatGPT demonstrated a high degree of proficiency in addressing a wide array of plastic surgery-related topics. The AI-generated responses were found to be relevant and accurate in most cases. However, it demonstrated convergent thinking and failed to generate genuinely novel ideas to revolutionize plastic surgery. Instead, it suggested currently popular trends that demonstrate great potential for further advancements. Some of the references presented were also erroneous as they cannot be validated against the existing literature. Although ChatGPT requires major improvements, this study highlights its potential as an effective tool for uncovering novel aspects of plastic surgery and identifying areas for future innovation. By leveraging the capabilities of AI language models, plastic surgeons may drive advancements in the field. Further studies are needed to cautiously explore the integration of AI-driven insights into clinical practice and to evaluate their impact on patient outcomes. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Artificial Intelligence in Biology

This article provides an in-depth analysis of the use of Artificial Intelligence (AI) in various aspects of biology, including healthcare, agriculture, and environmental monitoring. It highlights AI's ability to mimic human intelligence and analyze large datasets for predictions and tasks. The article also discusses its integration into Chinese medicine, where AI-guided diagnostic and therapeutic systems optimize clinical treatments and health management. AI is also used in disease management, analyzing data on diseases and pests, predicting their impact on ecosystems, and implementing preventative measures. The article also highlights the role of integrated information systems in environmental monitoring. Artificial intelligence (AI) has significant potential in healthcare research and chemical discoveries. Pharmaceutical companies are using AI to improve drug development by utilizing computational biology and machine learning systems to predict molecular behavior and the likelihood of finding a useful drug. This saves time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources for drug development. Strong AI systems can analyze extensive data sets in pharmaceutical and medical research. This review focuses on integrating knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in cancer precision drug discovery.

AI Assisting in Mental Health

Abstract: This research paper explores the transformative impact of artificial intelligence (AI) in the field of mental health counselling, aiming to enhance the effectiveness and accessibility of support services. As the demand for mental health care continues to rise globally, there is a growing need for innovative solutions to bridge the gap between the increasing demand for counselling and the limited availability of human counsellors. Our study focuses on the integration of AI technologies to assist mental health counsellors in various aspects of their practice. Through an extensive review of existing literature, we analyse the potential benefits and challenges associated with implementing AI in counselling settings. The paper highlights AI's ability to augment traditional counselling approaches by offering timely and personalised interventions, improving the overall efficiency of therapeutic processes. Drawing on case studies and pilot programs, this research presents empirical evidence supporting the positive outcomes and user acceptance of AI-assisted counselling interventions. Additionally, the paper discusses potential limitations, such as the need for continuous refinement of algorithms and the importance of addressing concerns related to data security and privacy.

The future of accounting: Predictions on automation and AI integration

As we stand on the brink of a transformative era, the future of accounting is intricately linked with the rapid evolution of automation and artificial intelligence (AI). This review delves into predictions regarding the integration of these technologies into the accounting landscape. The impending future is characterized by the increasing reliance on automation to streamline routine tasks, enhance efficiency, and mitigate the risk of errors in accounting processes. Automation is poised to revolutionize traditional bookkeeping, data entry, and reconciliation, allowing accountants to redirect their focus towards higher-value analytical tasks. The integration of robotic process automation (RPA) is anticipated to facilitate seamless collaboration between human expertise and machine precision. In parallel, artificial intelligence is set to become a cornerstone of accounting practices, offering advanced capabilities for data analysis, pattern recognition, and predictive modeling. AI's ability to interpret complex financial data and discern meaningful insights positions it as a valuable tool for decision support. Predictive analytics powered by AI is expected to revolutionize forecasting, enabling accountants to make strategic decisions based on data-driven foresight. Furthermore, the rise of AI-driven chatbots and virtual assistants is predicted to redefine client interactions and support services. These technologies are anticipated to enhance customer experience by providing instant responses to queries, facilitating smoother communication, and offering personalized financial guidance. However, the integration of automation and AI in accounting raises important considerations. Ethical concerns, data security, and the potential displacement of certain job functions are pivotal aspects that demand careful examination. Striking a balance between technological advancements and preserving ethical standards will be a critical challenge in the future accounting landscape. In conclusion, the future of accounting is shaped by the convergence of automation and AI, promising a paradigm shift in how financial information is processed, analyzed, and utilized. While automation and AI bring forth unprecedented opportunities for efficiency and insight, their implementation necessitates a thoughtful approach to ensure ethical conduct and address the evolving role of accountants in this transformative journey.

Enhancing the quality of teaching and learning through ChatGPT and similar large language models: Challenges, future prospects, and ethical considerations in education

The integration of Artificial Intelligence (AI) into the field of education has ushered in a transformative shift in the creation and dissemination of assignments. This research delves into the implications and complexities surrounding the incorporation of AI-enhanced ChatGPT submission in the realm of education. AI-enhanced ChatGPT submissions offer a multitude of benefits. First and foremost, they offer educators a convenient means to produce a wide array of content, encompassing quizzes, essay prompts, and problem sets, thus saving both time and effort. AI has the capacity to customize assignments according to the specific needs of individual students, ensuring personalized learning experiences. Additionally, AI's ability to analyze student performance data can aid educators in pinpointing areas where students might be facing challenges, allowing them to adapt their teaching methods accordingly. Nonetheless, this innovation also raises substantial concerns. The authenticity and quality of AI-generated submission may come under scrutiny, potentially giving rise to issues of plagiarism and academic integrity. It is crucial to ensure that AI-generated content aligns with educational objectives and standards. Before disseminating AI-generated submission to students, educators must meticulously curate and review them. Furthermore, the role of teachers in the educational process extends beyond merely assigning tasks. They provide mentorship, guidance, and feedback that surpass the capabilities of ChatGPT. This human touch is indispensable for nurturing critical thinking, fostering creativity, and enhancing emotional intelligence. Ethical considerations also play a pivotal role. Both students and educators must comprehend how AI-generated assignments are created, ensuring transparency in the process. Safeguards must be in place to safeguard data privacy, and measures need to be taken to address bias in AI algorithms. Hence, it is imperative for teachers and educational institutions to critically evaluate the place of AI in education and determine the circumstances under which AI-generated assignments can be integrated to enrich the educational landscape.

Artificial Intelligence: A Game-Changer for Mental Health Care.

Starting from the escalating global burden of mental health disorders, exacerbated by the COVID-19 pandemic, the article examines the potential of artificial intelligence (AI) to revolutionize mental health care. With nearly one in five adults facing mental health issues and suicide ranking as a leading cause of death among the young, the strained mental health system seeks innovative solutions. The text discusses the rapid evolution of AI, particularly in image analysis for early physical health diagnoses, and its promising applications in mental health, including predictive analytics for various disorders. AI's ability to analyze written language, speech characteristics, and physiological signals from wearables offers avenues for remote monitoring and early prognosis. Despite the need to address ethical considerations, particularly biases in data sets and concerns about potential patient detachment, the article advocates for AI as a complementary tool rather than a replacement for human involvement in mental health services. Overall, the article emphasizes the transformative potential of AI in enhancing diagnostics, monitoring, and treatment strategies for mental health disorders.

Klasifikasi Gender Berdasarkan Gambar Menggunakan Metode Deep Learning Pada MATLAB

In the present era, machine intelligence, also known as Artificial Intelligence (AI), is demanded not only to execute specific commands but also to recognize, analyze, or even make decisions, thereby providing desired outputs. By harnessing the power of AI, it is anticipated that desired outcomes will be more accurate and goal achievement will be optimized, minimizing losses. With the capabilities of AI in mind, a research study has been conducted on AI's ability to analyze and make decisions based on specific data. In this study, data in the form of images of men and women were utilized. The objective of this research is to analyze the ability of AI, particularly in gender classification. The method employed in designing this system is Deep Learning, with GoogLeNet as the Convolutional Neural Network utilized. In testing, the data accuracy ranged from 61.8% to 100% for the system without training algorithm options and from 97.5% to 100% for the system with training algorithm options. Testing was also carried out on a smaller set of training data and grayscale images, yielding lower accuracy ranges. From this research, it can be concluded that the quantity of training data, image preprocessing, and training algorithm options are crucial indicators for enhancing prediction accuracy.

Prevention of Artificial Intelligence (AI) Misuse in Online Medical Education

Aim: This study aims to assess the capabilities of artificial intelligence (AI) in answering online Continuing Medical Education (CME) courses to find the resistant to AI-misuse strategies. Materials and Methods: The study evaluated 30 CME online courses from popular American (ACCME), European (EACCME), and German Medical Association accredited online platforms, including Medscape, eaccme.uems.eu, Springer Nature, der-niedergelassene-arzt, and Aerzteblatt. ChatGPT Version 4.0 with integrated plugins for interactive AI chats with documents, web access to scientific databases, and interactive AI chats with videos was used to answer the CME evaluation questions. A special scoring system, referred to as "complexity score," was introduced in the study. This system has two main objectives: first, to assess strategies that prevent the misuse of AI in medical online education; second, to measure the effort that physicians must invest to answer CME questions using AI. Results: AI was used to answer a total of 248 questions, divided into three categories: ACCME accredited courses: 7 credits; EACCME accredited courses: 9.5 credits; German CME courses: 28 credits. AI successfully completed the quiz in 90% of cases (27 courses) and showed an accuracy rate of 86%. 213 out of 248 questions were correctly answered: 38 out of 48 ACCME questions; 85 out of 100 EACCME questions; 90 out of 100 CME questions. The outcome "AI error" was significantly associated only with a higher number of questions in the quiz: p-value 0.01. However, this predictor had no impact on the AI's ability to successfully complete the entire quiz. The AI failure rate was significantly associated with learning materials based on new studies without open access: p-value 0.02 and the need to view all learning materials to gain access to the quiz: p-value 0.02. A higher complexity score of the course was significantly associated with AI failure rates: p-value 0.0034. Conclusion: This study has shown that AI can successfully answer medical quiz questions even without access to learning materials. Therefore, the best strategy to prevent the misuse of AI in CME online training is to align human learning with AI feeding. Access to the quiz should only be possible after a complete review of the learning materials. This could be achieved by setting a fixed time or through multiple slides with separate access to each slide and subsequent quiz access.

Artificial intelligence's ability to process images

Penetration of technology into different fields and their dominion over them after a while is undoubtable. New technologies lure our souls and the dependency on them is inextricable once having tasted the fruit. We as a human easily addict to the convenient and comforting songs. As well as when they do our work for us in a more preferable and presentable way. This work was done to illustrate the artificial intelligence use in image processing. Its capabilities and issues are the key factors of the concerns of the work. Therefore, the work outlines the basic ideas behind artificial intelligence and its use in different areas and making accent on the capabilities of the technology when using tools powered by artificial intelligence to understand the meaning of the provided picture. Possible solutions are also described.

Google Bard Generated Literature Review: Metaverse

Literature review articles aim to present studies in a field to researchers in a regular, systematic and meaningful way. It is often a very difficult process to reach all the studies in the field to be reviewed or to identify and evaluate the important ones. With this study, the use of Artificial Intelligence robots and moreover generative artificial intelligence in literature review processes is evaluated. Google Bard was used to detect artificial intelligence's ability to generate a literature review article. First, Bard was asked questions to write about some of the Metaverse-related topics in this article. Some of the texts in the study were generated entirely by the answers given to the questions asked by Bard. In addition, 10 articles on Metaverse published in the last three years (2021, 2022 and 2023) were collected by searching Google Scholar with the word "Metaverse". Afterwards, these studies were interpreted by Bard. Bard was told to paraphrase the summary parts of the related studies and the produced texts were shared in the study. All produced texts were checked through ithenticate and the results were evaluated. In addition, the texts were evaluated semantically. Additionally, a comparison with the capabilities of OpenAI ChatGPT is given. The results are promising; However, it was observed that the plagiarism matching rate of the paraphrased texts was higher when compared to the answers given to the questions. This article is an experiment to show that the collection and expression of knowledge can be accelerated with the help of artificial intelligence. It is considered that the relevant tools will be used more and more effectively in academic literature in the future.

Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients.

Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI's ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images. AI, used in this study, may serve as a practical skin cancer screening aid. While it does have technical and diagnostic limitations, its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.

How AI Improves Telemedicine through Improving Data Management in Healthcare

The ability of artificial intelligence (AI) to greatly increase the efficacy and efficiency of any task carried out has contributed to its rise in popularity in recent years. These days, the healthcare industry uses AI more often because of the growth in data and complexity. This study, which examined artificial intelligence (AI) in the healthcare industry, sought to fully grasp how AI enhances telemedicine by optimising data management. The results indicate that AI has helped to improve fragmented data organisation and data management. This enhancement really helped to increase the efficacy and efficiency of every process. The results also show that the use of artificial intelligence affected record accessibility, promoting telemedicine and enhancing precision and care quality. This study adds significant insights to inform the direction of healthcare technology advancement by demonstrating AI's enormous potential to optimise information management and care supply.

AI-Based Computational Model in Sustainable Transformation of Energy Markets

The ability of artificial intelligence (AI) to process large amounts of data, analyze complex patterns, and make predictions is driving innovation in the energy sector and transformation of energy markets. It helps optimize operations, improve efficiency, reduce costs, and accelerate the transition to cleaner and more sustainable energy sources. AI is playing an increasingly important role in transforming energy markets in various aspects of the industry in different ways, including smart grids and energy management, renewable energy integration, energy forecasting and trading, demand response and load management, energy efficiency and conservation, maintenance and asset management, energy storage optimization, carbon emission reduction, market analytics and risk management, exploration and production, regulatory compliance, and safety. The aim of this article is to discuss our own AI-based computational model in sustainable transformation of energy markets and to lay the foundations for further harmonious development based on a computational (AI/ML-based) models, with particular reference to current limitations and priority directions for further research. Such an approach may encourage new research for the practical application of AI algorithms in critical domains of the energy sector.

Advancements and Challenges in the Application of Artificial Intelligence in Surgical Arena: A Literature Review.

This literature review delves into the transformative potential of artificial intelligence (AI) in the field of surgery, exploring its evolution, applications, and technological advancements. AI, with its ability to mimic human intelligence, presents a paradigm shift in surgical practices. The review critically analyzes a broad range of research, encompassing machine learning, deep learning, natural language processing, and their diverse applications in preoperative planning, surgical simulation, intraoperative guidance, and postoperative analysis. Ethical, legal, and regulatory considerations, as well as challenges and future directions, are also explored. The study underscores AI's ability to revolutionize surgical visualization and its role in shaping the future of healthcare.

Performance of AI-aided mammography in breast cancer diagnosis: Does breast density matter?

BackgroundOne of the top four malignancies affecting women worldwide is breast cancer. Breast density is a risk factor for breast cancer on its own and also a limiting factor for the sensitivity of screening mammography. Tools of artificial intelligence (AI) can help radiologists to make decisions, potentially reducing perceptual and interpretation errors, or as a way to prioritize exams based on the likelihood of malignancy.ObjectivesThe purpose of this study was to assess the impact of breast density on the performance of AI in mammography (MG) for the diagnosis of breast malignancy.MethodsIn total, 110 patients with pathologically proven breast cancer participated in this retrospective study. These patients had full field digital mammography, and the mammogram pictures were exported to the AI software system. Heat maps displaying the location of discovered lesions then highlighted the affected area or areas and also provided abnormality scores indicating the probability of malignancy (POM). The results of the histopathological analysis were correlated with the breast density and AI category.ResultsThe artificial intelligence software gave a breast density score to each patient as well as POM scoring. Both the software and the radiologist agreed on the breast density in 80.00% (N = 88) of the patients. Upon correlation of AI results to the BI-RADS given by radiologist, demonstrated statistically very significant correlation (P value 0.001), indicating that the likelihood of error is less than one in a thousand. Upon correlating the pathology results with the AI abnormality score, the AI showed sensitivity of 93.64% as it detected 103 true positive lesions. AI showed 100% sensitivity in both ACR A and ACR B, and 94.74%, 76.47% in ACR C, ACR D, respectively. False negative results represented 5.26% in ACR C group and the highest with 23.53% in ACR D group of patients. The P value was found less than 0.001. Pearson correlation coefficient was calculated (R = 0.27) which was interpreted as a weak correlation between the decrease in sensitivity of AI and the breast density.ConclusionsOur study showed that there is a slight link between increasing breast density and a relative decline in AI's ability to detect malignant lesions, suggesting that AI can detect breast cancer effectively in breasts of different parenchymal densities, with its effectiveness being highest in breasts with lower parenchymal density.

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders.

Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.