مرزهای اخلاقی و فنی در پزشکی شخصیسازیشده بر پایه هوش مصنوعی: یک مرور سیستماتیک از نوآوری و ژنومیکس مبتنی بر بیمار
کد: G-1623
نویسندگان: Shadi Saedpanah * ℗, Kimia Ghasemian
زمان بندی: زمان بندی نشده!
برچسب: تشخیص و درمان سرطان
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background and aims: Artificial intelligence (AI) transforms personalized medicine by integrating genomic and epigenetic data into therapies. However, the rapid adoption of these technologies presents ethical and technical challenges. Key concerns include algorithmic bias, unequal data access, and opaque decision-making, all of which threaten patient trust and equity in healthcare. This review examines the interplay between technological advancements and ethical considerations in AI-driven personalized medicine, identifying critical challenges and proposing frameworks for responsible innovation that prioritize patient welfare and equity. Method: A literature review was conducted across PubMed, Scopus, and Web of Science from January 2020 to March 2025 using key terms like “AI ethics,” “genomic bias,” and “precision medicine.” The criteria focused on peer-reviewed articles that explored ethical and technical aspects of AI in genetic applications. Out of 1,170 screened articles, 89 met the eligibility requirements. Data extraction followed PRISMA 2020 guidelines, and thematic analysis identified ethical issues and technical constraints. Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT). Results: Technical barriers primarily involved the limited generalizability of AI models, with 68% of studies reporting performance disparities exceeding 30% in non-European genomic cohorts. Ethical challenges were prevalent, including inadequately informed consent protocols for data reuse (71%), algorithmic bias that exacerbated health inequities (57%), and the commercial exploitation of patient data (43%). Innovations such as federated learning enhanced data privacy in 62% of clinical implementations, while explainable AI (XAI) tools fostered greater clinician trust in 78% of trials. Paradoxically, only 24% of studies engaged patient stakeholders in the AI design phases, underscoring a significant gap in patient-centricity. Conclusion: The evolution of AI-driven personalized medicine requires an ethical, transformative approach. Governance frameworks must emphasize auditability, inclusivity, and patient empowerment. Developers should utilize diverse training datasets and involve varied populations to address biases. Policymakers need to establish transparency standards for AI in healthcare, while researchers should focus on long-term societal effects. Interdisciplinary collaboration is essential to ensure AI promotes equitable, patient-centered healthcare while safeguarding safety and trust.
کلمات کلیدی
Artificial Intelligence, Precision Medicine, Ethics, Genomics
