Journal of Medicine, Surgery, and Public Health最新文献

{"title":"Artificial intelligence and big data for pharmacovigilance and patient safety","authors":"Muhammad Aasim Shamim, Muhammad Aaqib Shamim, Pankaj Arora, Pradeep Dwivedi","doi":"10.1016/j.glmedi.2024.100139","DOIUrl":"10.1016/j.glmedi.2024.100139","url":null,"abstract":"<div><div>Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.</div><div>The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.</div><div>Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.</div><div>Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.</div><div>Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.</div><div>The integration of AI and big data in pharmacovigilance has the potenti","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for medicine, surgery, and public health","authors":"Jagdish Khubchandani,&nbsp;Srikanta Banerjee,&nbsp;Robert Andrew Yockey,&nbsp;Kavita Batra","doi":"10.1016/j.glmedi.2024.100141","DOIUrl":"10.1016/j.glmedi.2024.100141","url":null,"abstract":"<div><div>Artificial Intelligence (AI) has rapidly transformed many sectors, including medicine, surgery, and public health. While AI has a multitude of unique characteristics that differ from the existing and most commonly used healthcare technologies worldwide, the discussion and publications on AI in healthcare have grown exponentially within the past few years. Despite its transformative potential, AI poses several challenges and there are unanswered questions related to the value and impact of AI on consumers, healthcare providers, and health systems. This editorial explores the growing applications of AI and its potential impacts on key entities in the field of healthcare and public health. Also, through this editorial, the journal editors highlight the urgent need for high-quality and real-world setting-based research on the value of AI in healthcare and public health. Finally, as AI will undoubtedly and significantly continue to impact healthcare consumers and systems, the editors are seeking submissions with rigorous and empirical evidence for AI’s impact on health services consumers and providers, and clinical care facilities or public health organizations. The editors believe that unless scholars worldwide generate robust evidence on the value and impact of AI in healthcare, providing the highest benefits of AI to health services consumers will remain an elusive goal.</div></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and the Dehumanization of Patient Care","authors":"Adewunmi Akingbola,&nbsp;Oluwatimilehin Adeleke,&nbsp;Ayotomiwa Idris,&nbsp;Olajumoke Adewole,&nbsp;Abiodun Adegbesan","doi":"10.1016/j.glmedi.2024.100138","DOIUrl":"10.1016/j.glmedi.2024.100138","url":null,"abstract":"<div><p>The integration of artificial intelligence (AI) into healthcare is rapidly transforming patient care, offering numerous advantages in diagnostics, efficiency, and clinical decision-making. However, this technological shift raises significant concerns about the potential erosion of the doctor-patient relationship, a cornerstone of effective medical practice. AI’s increasing role risks depersonalizing healthcare, as the emphasis on data-driven decisions may overshadow the empathy, trust, and personalized care traditionally provided by human clinicians. The \"black-box\" nature of AI algorithms further exacerbates this issue, as the lack of transparency in AI decision-making processes can undermine patient trust. Additionally, AI systems trained on biased datasets may inadvertently widen health disparities, particularly for underrepresented populations. While AI has the potential to streamline routine tasks and reduce the burden on healthcare providers, it is essential to ensure that these advancements do not come at the cost of the human connection vital to patient care. To address these challenges, future research and development should focus on creating AI systems that enhance, rather than replace, the compassionate aspects of healthcare. This balanced approach is crucial to preserving the integrity of the doctor-patient relationship while harnessing the benefits of AI, ultimately ensuring that technological progress aligns with the core values of medical practice.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000914/pdfft?md5=707efee72649c5150fa05ce58c065d61&pid=1-s2.0-S2949916X24000914-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence And Cancer Care in Africa","authors":"Adewunmi Akingbola ,&nbsp;Abiodun Adegbesan ,&nbsp;Olajide Ojo ,&nbsp;Jessica Urowoli Otumara ,&nbsp;Uthman Hassan Alao","doi":"10.1016/j.glmedi.2024.100132","DOIUrl":"10.1016/j.glmedi.2024.100132","url":null,"abstract":"<div><p>AI's potential to revolutionize oncology through enhanced diagnostics, treatment planning, and patient monitoring is well-documented globally. However, in Africa, its adoption has been slower, albeit steadily progressing. This commentary explores the integration of artificial Intelligence in cancer care across Africa, assessing its current state, challenges and future directions. It highlights significant AI innovations in cancer diagnostics, such as DataPathology, PapsAI, MinoHealth, and Hurone AI, which utilize AI for tissue analysis, cervical cell imaging, disease forecasting, and remote patient monitoring. Despite these advancements, several challenges impede AI's full integration into African healthcare systems. Key issues include data privacy and security, algorithm bias, and insufficient regulatory frameworks. The review emphasizes the necessity of robust data protection policies, representative datasets to mitigate biases, and clear guidelines for AI deployment tailored to the African context. Emerging AI technologies in Africa, such as AI-enhanced telemedicine, mobile health applications, predictive analytics, and virtual tumor boards, show promise in overcoming geographic and resource limitations. These innovations can facilitate remote consultations, continuous patient monitoring, and multidisciplinary collaborations, thereby improving cancer care accessibility and outcomes. Conclusively, recommendations for enhancing AI integration in African cancer care, including investing in data infrastructure, capacity building for healthcare professionals, and fostering international collaborations are discussed. Addressing ethical and regulatory challenges is crucial to ensure responsible and effective use of AI technologies. By leveraging AI, Africa can significantly improve cancer care delivery, reduce mortality rates, and enhance patient quality of life.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000859/pdfft?md5=7dfc5fec196e71461d03d515f44efe55&pid=1-s2.0-S2949916X24000859-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141990684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The rising threat of counterfeit GLP-1 receptor agonists: Implications for public health","authors":"Abdur Rehman,&nbsp;Abdulqadir J. Nashwan","doi":"10.1016/j.glmedi.2024.100136","DOIUrl":"10.1016/j.glmedi.2024.100136","url":null,"abstract":"<div><p>The rising demand for GLP-1 receptor agonists (GLP-1RAs), effective treatments for type 2 diabetes and obesity, has inadvertently led to a proliferation of counterfeit versions. This letter to the editor highlights the significant public health challenges posed by counterfeit GLP-1RAs, including severe risks to patient safety, economic impacts, and the erosion of public trust in the healthcare system. Counterfeit GLP-1RAs often contain incorrect dosages, harmful ingredients, or entirely lack the active ingredients, leading to ineffective treatment and potentially life-threatening complications such as hyperglycemia and cardiovascular issues. The economic burden of counterfeit drugs is also considerable, with healthcare systems incurring substantial costs in managing complications from these illegitimate medications, including hospitalizations and increased surveillance efforts. The drivers of this counterfeit drug problem include regulatory gaps, inadequate enforcement, and the expanding market demand due to rising rates of diabetes and obesity. In conclusion, the proliferation of counterfeit GLP-1RAs represents a critical threat to global health, underscoring the need for comprehensive measures to safeguard the integrity of the pharmaceutical supply chain and ensure patient safety. Addressing this issue requires a multifaceted approach that integrates regulatory oversight, technological innovation, and public education to mitigate the risks posed by counterfeit drugs and restore public trust in the healthcare system.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000896/pdfft?md5=0be073421461f3d89291c6db187dd7ad&pid=1-s2.0-S2949916X24000896-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Scalpels to Algorithms: The Risk of Dependence on Artificial Intelligence in Surgery","authors":"Abiodun Adegbesan,&nbsp;Adewunmi Akingbola,&nbsp;Olusola Aremu,&nbsp;Olajumoke Adewole,&nbsp;John Chukwuemeka Amamdikwa,&nbsp;Uchechukwu Shagaya","doi":"10.1016/j.glmedi.2024.100140","DOIUrl":"10.1016/j.glmedi.2024.100140","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is transforming surgery, advancing robotic-assisted procedures, preoperative risk prediction, and intraoperative decision-making. However, increasing reliance on AI raises concerns, particularly regarding the potential deskilling of surgeons and overdependence on algorithmic recommendations. This over-reliance risks diminishing surgeons' skills, increasing surgical errors, and undermining their decision-making autonomy. The \"black-box\" nature of many AI systems also presents ethical challenges, as surgeons may feel pressured to follow AI-driven recommendations without fully understanding the underlying logic. Additionally, AI biases from inadequate datasets can result in misdiagnoses and worsen healthcare disparities. While AI offers immense promise, a cautious approach is vital to prevent overdependence. Ensuring that AI enhances rather than replaces human skills in surgery is critical to maintaining patient safety. Ongoing research, ethical considerations, and robust legal frameworks are essential for guiding AI's integration into surgical practice. Surgeons must receive comprehensive training to incorporate AI into their work without compromising clinical judgment. This letter emphasizes the need for clear guidelines, thorough surgeon training, and transparent AI systems to address the risks associated with AI dependence. By taking these steps, healthcare systems can harness the benefits of AI while preserving the essential human aspects of surgical care.</div></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for Pediatric Emergency Medicine","authors":"Mohammed Alsabri ,&nbsp;Nicholas Aderinto ,&nbsp;Marina Ramzy Mourid ,&nbsp;Fatima Laique ,&nbsp;Salina Zhang ,&nbsp;Noha S. Shaban ,&nbsp;Abdalhakim Shubietah ,&nbsp;Luis L. Gamboa","doi":"10.1016/j.glmedi.2024.100137","DOIUrl":"10.1016/j.glmedi.2024.100137","url":null,"abstract":"<div><p>Pediatric Emergency Medicine (PEM) addresses the unique needs of children in emergencies. This subspecialty faces significant challenges, including the need for specialized training, patient crowding, and the demand for timely and accurate management. Artificial Intelligence (AI) presents promising solutions by enhancing diagnostic precision and operational efficiency. This review examines current trends and prospects of AI in PEM, focusing on its applications, benefits, challenges, and transformative potential. The review highlights AI’s role in overcoming PEM challenges and its future opportunities. Key AI applications in PEM include early sepsis detection, improving triage accuracy, predicting injuries, and supporting diagnostics. AI models show significant potential in forecasting clinical outcomes, optimizing resource management, and improving patient care. Despite these benefits, challenges remain, including the need for specialized training for physicians and the integration of AI systems into clinical practice. Yet, AI holds considerable promise for advancing PEM through enhanced diagnostic tools, more efficient patient management, and improved clinical decision support. Continued advancements and collaborations between AI researchers and pediatric emergency practitioners are essential to fully realize AI’s potential in this field.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000902/pdfft?md5=c25608f5f3bd62a26321340e3e2b4894&pid=1-s2.0-S2949916X24000902-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The promise of artificial intelligence and internet of things in oral cancer detection","authors":"Amol S. Dhane","doi":"10.1016/j.glmedi.2024.100130","DOIUrl":"10.1016/j.glmedi.2024.100130","url":null,"abstract":"<div><p>The significance of artificial intelligence (AI) and the internet of things (IoT) in improving oral cancer detection is critically assessed in this letter. Oral cancer is a major worldwide health concern that is frequently detected at a late stage, resulting in a poor prognosis. AI techniques, in particular machine learning and deep learning models, show great promise for accurately assessing digital images and histopathology slides, assisting physicians in risk assessment and early identification. Furthermore, real-time monitoring and surveillance are made possible by IoT-enabled devices, which gather important patient data for the early identification of indications of oral cancer. Furthermore, the performance and efficacy of diagnosis have been improved by developments in image processing algorithms, which helps to avoid delayed diagnosis. Big data analytics and the application of salivary biomarkers enhance early detection initiatives. To battle oral cancer, a variety of AI and IoT strategies are being investigated, in addition to other AI uses. Although encouraging developments, application in clinical practice will not be successful unless issues with validation, standardization, data privacy and regulatory compliance are resolved. Working together, healthcare stakeholders can promote innovation, validate techniques and get over current obstacles. To reduce the prevalence of oral cancer, future directions include the creation of multimodal imaging methods and their incorporation into population-based screening initiatives. We can move closer to early detection, individualized therapy and prevention of oral cancer by utilizing AI and IoT, which will ultimately improve patient outcomes.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000835/pdfft?md5=a566ff140095815c83fe54567839c4e1&pid=1-s2.0-S2949916X24000835-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of artificial intelligence in enhancing nurses' work-life balance","authors":"Moustaq Karim Khan Rony ,&nbsp;Daifallah M. Alrazeeni ,&nbsp;Fazila Akter ,&nbsp;Latifun Nesa ,&nbsp;Dipak Chandra Das ,&nbsp;Muhammad Join Uddin ,&nbsp;Jeni Begum ,&nbsp;Most. Tahmina Khatun ,&nbsp;Md. Abdun Noor ,&nbsp;Sumon Ahmad ,&nbsp;Sabren Mukta Tanha ,&nbsp;Tuli Rani Deb ,&nbsp;Mst. Rina Parvin","doi":"10.1016/j.glmedi.2024.100135","DOIUrl":"10.1016/j.glmedi.2024.100135","url":null,"abstract":"<div><p>Nursing, a cornerstone of healthcare, is a profession characterized by its dedication to patient well-being. However, the demanding nature of nursing often takes a toll on work-life balance. This commentary investigates how artificial intelligence (AI) could significantly impact the healthcare sector, particularly by enhancing the work-life balance of nurses. It highlights how AI can greatly lessen administrative tasks, improve clinical decision-making, and support remote patient monitoring, ultimately helping nurses achieve a more balanced work-life dynamic. The advancement of AI in healthcare presents a strong opportunity to improve nurses' work-life balance. Our comprehensive conceptual framework illustrates how AI can transform nursing practice, offering nurses newfound efficiency and flexibility. By responsibly integrating AI technologies, healthcare institutions can empower nurses to excel in their roles while enjoying a more sustainable work-life equilibrium. This commentary serves as a roadmap for embracing the potential of AI, not as a replacement for nurses, but as a valuable ally in fostering a better future for both nurses and the patients they serve.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000884/pdfft?md5=8a9d33afadf21b22bcaef0ba4a53449d&pid=1-s2.0-S2949916X24000884-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for hearing loss prevention, diagnosis, and management","authors":"Jehad Feras AlSamhori ,&nbsp;Abdel Rahman Feras AlSamhori ,&nbsp;Rama Mezyad Amourah ,&nbsp;Yara AlQadi ,&nbsp;Zina Wael Koro ,&nbsp;Toleen Ramzi Abdallah Haddad ,&nbsp;Ahmad Feras AlSamhori ,&nbsp;Diala Kakish ,&nbsp;Maya Jamal Kawwa ,&nbsp;Margaret Zuriekat ,&nbsp;Abdulqadir J. Nashwan","doi":"10.1016/j.glmedi.2024.100133","DOIUrl":"10.1016/j.glmedi.2024.100133","url":null,"abstract":"<div><p>This paper explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML), on diagnosing and treating hearing loss, which affects over 5% of the global population across all ages and demographics. AI encompasses various applications, from natural language processing models like ChatGPT to image recognition systems; however, this paper focuses on ML, a subfield of AI that can revolutionize audiology by enhancing early detection, formulating personalized rehabilitation plans, and integrating electronic health records for streamlined patient care. The integration of ML into audiometry, termed \"computational audiology,\" allows for automated, accurate hearing tests. AI algorithms can process vast data sets, provide detailed audiograms, and facilitate early detection of hearing impairments. Research shows ML's effectiveness in classifying audiograms, conducting automated audiometry, and predicting hearing loss based on noise exposure and genetics. These advancements suggest that AI can make audiological diagnostics and treatment more accessible and efficient. The future of audiology lies in the seamless integration of AI technologies. Collaborative efforts between audiologists, AI experts, and individuals with hearing loss are essential to overcome challenges and leverage AI's full potential. Continued research and development will enhance AI applications in audiology, improving patient outcomes and quality of life worldwide.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000860/pdfft?md5=0ce34d0abea03ea27c334d59f1f1c016&pid=1-s2.0-S2949916X24000860-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}