Afleveringen
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The document is a review exploring the expanding role of artificial intelligence (AI) and robotics in medicine. It analyzes current applications in diagnosis, surgery, personalized medicine, nursing, and rehabilitation, highlighting advancements like AI algorithms in radiology and robotic surgical systems. The review also addresses the barriers to technology integration, along with ethical and legal issues. Furthermore, the document discusses opportunities for future research and innovation, such as bone organoids and bispecific antibodies, to further enhance healthcare. This paper provides a comprehensive understanding of the transformative impact of AI and robotics on healthcare.
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This research paper analyzes the top 100 most cited articles related to artificial intelligence in medicine between 1950 and 2019. The authors identified key trends and characteristics within this body of literature, noting a prevalence of non-clinical, experimental studies. Medical informatics and radiology were the most represented fields, while oncology showed promise in clinical AI integration. Despite cardiovascular disease's high mortality rate, it lacked significant representation in AI research. The study highlights the need for more clinical studies to facilitate the integration of AI into practical medical applications.
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This Congressional Research Service report, dated December 30, 2024, offers a wide view of artificial intelligence use in healthcare. It details AI techniques, like machine learning and natural language processing, and applications spanning diagnosis, patient engagement, and administrative tasks. The report highlights recent federal actions, including Executive Order 14110 and agency efforts by HHS divisions like the FDA and OCR, to regulate AI in healthcare. It brings up key challenges, such as data access, bias, transparency, and privacy, that may slow progress. Furthermore, the report addresses harmonizing AI regulation and dealing with the environmental impact of AI.
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The provided text is a comprehensive survey article exploring the use of Explainable Artificial Intelligence (XAI) in drug discovery and development. The article addresses the increasing need for transparency in complex AI and machine learning models used in the healthcare industry. It covers various XAI methods, their application in processes such as target identification and toxicity prediction, and discusses the challenges and limitations of XAI techniques. The survey also emphasizes the ethical considerations and future research directions for XAI in the field. Ultimately, the article aims to provide a deep understanding of how XAI can transform drug discovery by making AI-driven predictions more interpretable and trustworthy.
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The article examines the integration of artificial intelligence (AI) and robotics in surgery. It highlights how machine learning and predictive analytics enhance surgical precision, personalize treatment, and improve patient outcomes. The paper explores the evolution of surgical robotics and early AI applications in medicine, focusing on AI's role in decision-making, precision, and safety. It discusses technologies like computer vision, reinforcement learning, and natural language processing, with successful implementations of AI surgery, such as the Da Vinci Surgical System. The review also addresses ethical concerns related to patient safety, data privacy, bias in AI models, and regulatory challenges. The article concludes that the synergy between AI and robotics is revolutionizing surgery, leading to safer and more efficient personalized care, but the adoption of these technologies must address ethical considerations to ensure equitable healthcare delivery.
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This FDA guidance offers recommendations for manufacturers regarding marketing submissions for medical devices incorporating artificial intelligence (AI). It outlines a total product lifecycle (TPLC) approach, emphasizing transparency and addressing potential biases in AI-enabled devices. The guidance details necessary documentation and information for FDA review, covering device description, user interface, risk assessment, data management, model development, validation, cybersecurity, and public submission summaries. Appendices provide further insights into transparency design, performance validation, usability, and model card examples. The document aims to promote safe, effective, and high-quality AI-enabled medical devices by aligning with software-related consensus standards and encouraging ongoing performance monitoring. The core focus is assisting manufacturers in meeting regulatory expectations and ensuring device safety and effectiveness through comprehensive documentation and adherence to best practices.
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Cypris's report investigates the transformative role of artificial intelligence (AI) in medical device manufacturing. It highlights the substantial investments and market growth driven by AI's ability to improve diagnostics, personalize treatments, and streamline medical processes. The report analyzes funding distribution, patent activity (featuring key players like Siemens and Baidu), and trending research, emphasizing technologies such as AI-driven image analysis, blockchain for data management, and wearable sensors. Crucially, the study suggests manufacturers should invest in digital infrastructure and partnerships to fully leverage AI's potential. Cypris aims to provide R&D teams with insights to create innovative medical devices and navigate this rapidly evolving technological landscape. Ultimately, the document seeks to inform and encourage medical device manufacturers to embrace AI to meet the dynamic needs of the healthcare industry.
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This document offers a review of the landscape surrounding the use of artificial intelligence (AI) in medical devices, highlighting the definitions, recommendations, and regulations shaping its implementation. It examines the complexities of defining AI in the medical context and surveys existing regulatory initiatives, consensus recommendations, and standards proposed by various international organizations. The piece emphasizes the need for common standards in the clinical evaluation of high-risk AI applications to promote transparency and evidence-based medicine. The authors explore existing gaps in current guidelines and the need for clarity as a result of the fast pace of AI advancement in medical tools, to ensure the safe and effective deployment of AI within healthcare. It looks into EU laws that may impact how AI medical systems can be used, or how much information can or must be disclosed. The article concludes by calling for practical, evidence-based standards that consider clinical risks and promote international regulatory convergence.
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This OECD report examines medical associations' perspectives on integrating artificial intelligence (AI) into healthcare. The study, conducted through a survey and interviews, explores both the potential benefits of AI in addressing workforce shortages and improving healthcare efficiency, and the associated risks, such as ethical concerns, liability issues, and data privacy challenges. Key findings reveal that while medical associations largely see AI as beneficial, significant concerns remain about responsible implementation, the need for increased digital literacy, and the establishment of clear ethical and legal frameworks. The report concludes with recommendations for skill development, workforce adaptation, and the safe management of AI in healthcare systems
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Investigate how integrating telemedicine and artificial intelligence (AI) can improve healthcare access in rural areas. Telemedicine, using technology for remote consultations, expands access to care, while AI enhances diagnostics, treatment planning, and patient monitoring. The authors explore the synergistic potential of these technologies, examining implementation strategies, addressing challenges like data security, and considering policy implications. The study highlights the need for infrastructure development, provider training, and robust cybersecurity measures for successful implementation. Finally, the authors discuss future directions, including advancements in telemedicine technology and AI capabilities, to further improve rural healthcare.
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Explore the transformative potential of artificial intelligence (AI) in healthcare. These studies examine AI's impact on operational efficiency, focusing on improved resource allocation, predictive analytics, and automated workflows. Ethical considerations, including data privacy, bias mitigation, and transparency, are also central themes. The research highlights successful AI applications in radiology, patient safety, and nursing, while acknowledging the need for robust regulatory frameworks and ongoing ethical evaluations to ensure responsible AI implementation in healthcare. Furthermore, the role of various stakeholders, such as healthcare professionals, patients, and regulatory bodies, in shaping the future of AI in healthcare is discussed
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This research paper investigates the use of wearable technology for health monitoring and diagnostics. A desktop research methodology, reviewing existing studies, reveals a gap in understanding the long-term effectiveness and equitable access of wearables. The paper explores the Technology Acceptance Model (TAM), Health Belief Model (HBM), and Unified Theory of Acceptance and Use of Technology (UTAUT) as frameworks for future research. It emphasizes the need for user-centered design, robust data privacy, and integration of wearables into healthcare systems to maximize their impact. Recommendations include addressing challenges related to device accuracy, user adherence, and the digital divide.
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Explore the application of artificial intelligence (AI) in mental healthcare, examining its potential to improve access, accuracy of diagnoses, and treatment personalization while acknowledging ethical concerns around bias, privacy, and the dehumanization of care. Another study investigates the role of religious organizations in trauma support, particularly concerning gun violence, highlighting their unique advantages in community engagement and long-term healing. Finally, a separate paper uses microsatellite markers to analyze genetic diversity among cattle and buffalo breeds.
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This article examines the ethical and legal implications of using artificial intelligence (AI) in medicine. It explores the potential benefits of AI in various medical applications, such as diagnosis and treatment, while also highlighting potential challenges like algorithmic bias, economic disruption to healthcare systems, and the need for interdisciplinary collaboration to address these issues. The authors advocate for a human-centered approach to AI development and implementation, emphasizing transparency, explainability, and the importance of considering broader societal impacts. They support this with a systematic literature review and analysis of existing and proposed legislation in both the European Union and Brazil. Ultimately, the article stresses the necessity of moving beyond a solely legal perspective to achieve responsible AI integration in healthcare.
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This research review article examines the transformative applications of artificial intelligence (AI) in the pharmaceutical industry. AI-powered tools are accelerating drug discovery by optimizing processes like target identification, compound selection, and synthesis route prediction. The integration of AI is also revolutionizing drug development by improving clinical trial design, personalizing treatment regimens based on patient data, and enhancing drug formulation and delivery. The authors discuss both the remarkable advancements and the challenges, such as ethical considerations and regulatory hurdles, associated with implementing AI in pharmaceutical processes. Finally, the paper provides numerous examples of AI's current use in pharmaceutical companies and considers future implications for healthcare.
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This viewpoint paper examines the expanding use of generative AI in healthcare, focusing on its potential benefits across various applications like medical diagnostics and drug discovery. The authors highlight significant privacy and security risks associated with these AI systems, particularly concerning the handling of sensitive patient data. The paper categorizes generative AI applications in healthcare and analyzes security threats throughout their life cycle. Finally, it proposes recommendations for mitigating these risks through improved risk assessment protocols and the development of specific metrics for evaluating AI trustworthiness and responsibility.
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This research paper examines the security and privacy challenges within e-health systems, exploring their evolution from paper-based records to advanced AI-driven systems. The authors discuss the increasing cyber threats targeting these systems, including hacking and ransomware attacks, and analyze vulnerabilities in cloud computing, EHRs, and the IoMT. The paper then explores AI and machine learning techniques for threat detection and prevention, emphasizing privacy-preserving methods like federated learning and differential privacy. Finally, it looks at future research directions, including quantum-resistant encryption and ethical AI development, to build more secure and resilient healthcare infrastructures.
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This editorial announces the launch of NEJM AI, a new journal focused on the responsible development and application of artificial intelligence in healthcare. The author highlights the rapid growth of AI in medicine, particularly large language models, emphasizing the critical need for rigorous clinical evaluation, ideally through randomized controlled trials, to ensure safety and efficacy. The journal aims to foster a multidisciplinary discussion promoting transparency and patient-centered approaches. NEJM AI will also utilize other media like podcasts and seminars to achieve its goals of improving healthcare through AI. Ultimately, the journal seeks to ethically integrate AI into medicine while upholding patient autonomy and high standards of care.
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This research paper examines data security challenges in AI-enabled medical device software. The authors explore various threats, including data breaches, adversarial attacks (data poisoning and evasion attacks), cyberattacks, and insider threats. They also highlight the difficulties posed by a lack of skilled cybersecurity personnel and the complexity of existing security standards. The paper concludes by emphasizing the need to address these challenges to ensure the trustworthiness and safe adoption of AI in healthcare.
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