Quetiapine is an atypical antipsychotic commonly used to manage psychotic and bipolar disorders. While quetiapine overdose is often associated with sedation, tachycardia, and QT interval prolongation on the ECG, severe hypotension and corrected QT interval (QTc) prolongation are relatively rare. There is limited information available regarding the safety of quetiapine overdose, particularly in the pediatric population. Here, we present the case of a 15-year-old girl who ingested quetiapine in a suicide attempt. A 15-year-old girl who ingested 1200 mg of quetiapine (22.6 mg/kg) in a suicide attempt. The overdose led to multiple severe symptoms, including tachycardia, agitation, hypotension, loss of consciousness, and QTc prolongation. To effectively eliminate quetiapine, we utilized a combination of hemoperfusion (HP) and continuous venovenous hemodiafiltration (CVVHDF) therapy. According to recent literature, this is the first reported pediatric case of severe quetiapine poisoning successfully treated with the combined use of HP and CVVHDF. In this report, we compare the clinical presentation with previous cases of quetiapine overdose in both pediatric and adult populations, review current treatment recommendations, and introduce a novel therapeutic approach for managing quetiapine poisoning.
{"title":"A case report of combined hemoperfusion and hemodiafiltration utilization in pediatric severe Quetiapine poisoning","authors":"Ufuk Yükselmiş , Merve Akçay , Omar Alomari , Müge Kömürcüoğlu Yılmaz","doi":"10.1016/j.glmedi.2024.100147","DOIUrl":"10.1016/j.glmedi.2024.100147","url":null,"abstract":"<div><div>Quetiapine is an atypical antipsychotic commonly used to manage psychotic and bipolar disorders. While quetiapine overdose is often associated with sedation, tachycardia, and QT interval prolongation on the ECG, severe hypotension and corrected QT interval (QTc) prolongation are relatively rare. There is limited information available regarding the safety of quetiapine overdose, particularly in the pediatric population. Here, we present the case of a 15-year-old girl who ingested quetiapine in a suicide attempt. A 15-year-old girl who ingested 1200 mg of quetiapine (22.6 mg/kg) in a suicide attempt. The overdose led to multiple severe symptoms, including tachycardia, agitation, hypotension, loss of consciousness, and QTc prolongation. To effectively eliminate quetiapine, we utilized a combination of hemoperfusion (HP) and continuous venovenous hemodiafiltration (CVVHDF) therapy. According to recent literature, this is the first reported pediatric case of severe quetiapine poisoning successfully treated with the combined use of HP and CVVHDF. In this report, we compare the clinical presentation with previous cases of quetiapine overdose in both pediatric and adult populations, review current treatment recommendations, and introduce a novel therapeutic approach for managing quetiapine poisoning.</div></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"4 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529264","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}
Pub Date : 2024-10-16DOI: 10.1016/j.glmedi.2024.100149
Najim Z. Alshahrani
In the digital age, social media influencers (SMIs) have emerged as a significant force in altering public attitudes and behaviors, notably in the health sector. While SMIs can provide useful insights on crucial health issues such as diet and mental health, they also raise concerns about their varying levels of health competence and potential commercial biases. This dichotomy poses a challenge: can SMIs effectively contribute to better health outcomes, or do they risk encouraging harmful behavior? This letter investigates this dynamic by synthesizing findings from three cross-sectional studies undertaken in Saudi Arabia, with the goal of providing policymakers with actionable insights on how to improve public health while navigating the inherent risks.
{"title":"The role of social media influencers on health behaviors in Saudi Arabia","authors":"Najim Z. Alshahrani","doi":"10.1016/j.glmedi.2024.100149","DOIUrl":"10.1016/j.glmedi.2024.100149","url":null,"abstract":"<div><div>In the digital age, social media influencers (SMIs) have emerged as a significant force in altering public attitudes and behaviors, notably in the health sector. While SMIs can provide useful insights on crucial health issues such as diet and mental health, they also raise concerns about their varying levels of health competence and potential commercial biases. This dichotomy poses a challenge: can SMIs effectively contribute to better health outcomes, or do they risk encouraging harmful behavior? This letter investigates this dynamic by synthesizing findings from three cross-sectional studies undertaken in Saudi Arabia, with the goal of providing policymakers with actionable insights on how to improve public health while navigating the inherent risks.</div></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"4 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529265","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}
The resurgence of Mpox (formerly known as Monkeypox) in Africa, marked by a 160 % increase in cases and a 19 % rise in deaths in 2024 compared to the previous year, is driven by the emergence of a more virulent clade 1b variant. This resurgence, declared a Public Health Emergency of International Concern by the World Health Organization, highlights the persistent challenges in global health equity, particularly in vaccine distribution, public health infrastructure, and surveillance. Drawing from historical lessons, including vaccine inequity during the COVID-19 pandemic and delayed responses in past outbreaks, this paper outlines critical strategies for addressing the current crisis. These strategies include strengthening vaccine equity and access, enhancing community-level surveillance, promoting research and development, implementing comprehensive public health campaigns, and addressing environmental factors that facilitate outbreaks. The paper emphasizes the need for international solidarity and support, proposing the establishment of a global accord to ensure equitable sharing of resources during health emergencies and to prevent low- and middle-income countries from being left behind.
{"title":"Emergence of a more virulent clade of Mpox in Africa: Learning from history and charting a path forward","authors":"Isaac Iyinoluwa Olufadewa, Ruth Ifeoluwa Oladele , Oluwatayo Ayobami Olajide, Harrison Toluwanimi Adetunji, Godwin Edoseawe Okoduwa, Toluwase Ayobola Olufadewa, Miracle Ayomikun Adesina","doi":"10.1016/j.glmedi.2024.100134","DOIUrl":"10.1016/j.glmedi.2024.100134","url":null,"abstract":"<div><p>The resurgence of Mpox (formerly known as Monkeypox) in Africa, marked by a 160 % increase in cases and a 19 % rise in deaths in 2024 compared to the previous year, is driven by the emergence of a more virulent clade 1b variant. This resurgence, declared a Public Health Emergency of International Concern by the World Health Organization, highlights the persistent challenges in global health equity, particularly in vaccine distribution, public health infrastructure, and surveillance. Drawing from historical lessons, including vaccine inequity during the COVID-19 pandemic and delayed responses in past outbreaks, this paper outlines critical strategies for addressing the current crisis. These strategies include strengthening vaccine equity and access, enhancing community-level surveillance, promoting research and development, implementing comprehensive public health campaigns, and addressing environmental factors that facilitate outbreaks. The paper emphasizes the need for international solidarity and support, proposing the establishment of a global accord to ensure equitable sharing of resources during health emergencies and to prevent low- and middle-income countries from being left behind.</p></div>","PeriodicalId":100804,"journal":{"name":"Journal of Medicine, Surgery, and Public Health","volume":"3 ","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949916X24000872/pdfft?md5=e2e72311dc4ea75fbc2e13a74cd18f31&pid=1-s2.0-S2949916X24000872-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117543","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}
Pub Date : 2024-08-01DOI: 10.1016/j.glmedi.2024.100139
Muhammad Aasim Shamim, Muhammad Aaqib Shamim, Pankaj Arora, Pradeep Dwivedi
<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
{"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}
Pub Date : 2024-08-01DOI: 10.1016/j.glmedi.2024.100141
Jagdish Khubchandani, Srikanta Banerjee, Robert Andrew Yockey, Kavita Batra
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.
{"title":"Artificial intelligence for medicine, surgery, and public health","authors":"Jagdish Khubchandani, Srikanta Banerjee, Robert Andrew Yockey, 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}
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.
{"title":"Artificial Intelligence and the Dehumanization of Patient Care","authors":"Adewunmi Akingbola, Oluwatimilehin Adeleke, Ayotomiwa Idris, Olajumoke Adewole, 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}
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.
{"title":"Artificial Intelligence And Cancer Care in Africa","authors":"Adewunmi Akingbola , Abiodun Adegbesan , Olajide Ojo , Jessica Urowoli Otumara , 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}
Pub Date : 2024-08-01DOI: 10.1016/j.glmedi.2024.100136
Abdur Rehman, Abdulqadir J. Nashwan
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.
{"title":"The rising threat of counterfeit GLP-1 receptor agonists: Implications for public health","authors":"Abdur Rehman, 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}
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.
{"title":"From Scalpels to Algorithms: The Risk of Dependence on Artificial Intelligence in Surgery","authors":"Abiodun Adegbesan, Adewunmi Akingbola, Olusola Aremu, Olajumoke Adewole, John Chukwuemeka Amamdikwa, 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}
Pub Date : 2024-08-01DOI: 10.1016/j.glmedi.2024.100137
Mohammed Alsabri , Nicholas Aderinto , Marina Ramzy Mourid , Fatima Laique , Salina Zhang , Noha S. Shaban , Abdalhakim Shubietah , Luis L. Gamboa
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.
{"title":"Artificial Intelligence for Pediatric Emergency Medicine","authors":"Mohammed Alsabri , Nicholas Aderinto , Marina Ramzy Mourid , Fatima Laique , Salina Zhang , Noha S. Shaban , Abdalhakim Shubietah , 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}
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