فراتر از کارایی: بررسی خطرات پنهان و نگرانی های اخلاقی هوش مصنوعی در توسعه دارویی
کد: G-1265
نویسندگان: Mohammad Shokati Sayyad * ℗
زمان بندی: زمان بندی نشده!
برچسب: کشف و طراحی دارو
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background and aims: Integrating artificial intelligence (AI) in pharmaceutical development has significantly enhanced drug discovery by utilizing machine learning algorithms to analyze complex biological datasets. This study aims to thoroughly investigate the ongoing challenges to AI adoption in pharmaceutical sciences. Methods: This study employed a comprehensive literature review to investigate the current state of AI in pharmaceutical development. Databases including PubMed, Scopus, and Web of Science were systematically searched using keywords such as "artificial intelligence," "pharmaceutical development," "drug discovery," and "machine learning." Studies published between 2020 and 2025 were prioritized to ensure alignment with contemporary trends. The results were categorized into these main areas using an analysis of the retrieved literature. Results: Our analysis revealed several critical challenges in AI adoption for pharmaceutical development. These include data quality issues affecting model accuracy, particularly for underrepresented populations; algorithmic biases perpetuating health disparities; outdated regulatory frameworks; ethical concerns regarding privacy and fairness; lack of transparency in AI decision-making; integration challenges with traditional methods; and potential exacerbation of healthcare resource disparities. These findings underscore the need for a comprehensive approach to address AI integration challenges in pharmaceutical development, emphasizing the importance of improving data quality, ensuring algorithmic fairness, updating regulatory guidelines, and addressing ethical concerns to maximize the benefits of AI while mitigating its risks. Conclusion: While AI offers substantial benefits for accelerating drug discovery processes through improved efficiency and accuracy, it also introduces significant risks that must be addressed proactively. Improving data quality through standardization efforts and ensuring algorithmic fairness are crucial steps forward. Moreover, fostering collaboration between regulators and industry stakeholders will help navigate emerging ethical dilemmas effectively by adapting regulatory frameworks for better oversight of AI-driven decisions. Ultimately, addressing these challenges will enhance outcomes while ensuring safety without compromising equity. Future research should focus on developing standardized protocols for AI validation in drug development to address regulatory challenges.
کلمات کلیدی
Artificial Intelligence, Pharmaceutical Development, Machine
