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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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This article examines the significant challenges in applying artificial intelligence (AI) to clinical healthcare. Key obstacles include the inherent limitations of machine learning, logistical hurdles in implementation, and the need for robust regulatory frameworks. The authors emphasize the importance of rigorous clinical evaluation, using metrics relevant to real-world practice and patient outcomes, to ensure AI systems are both safe and effective. Furthermore, they highlight the need to address algorithmic bias and improve the interpretability of AI models to foster trust and wider adoption. Ultimately, the successful integration of AI in healthcare hinges on overcoming these challenges to realize its transformative potential.