چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | پریناز نژادمختاری |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | مرکز تحقیقات ریز فناوری دارویی |
| کد مقاله | 87823 |
| عنوان فارسی مقاله | نقش هوش مصنوعی (AI) در پیشبرد تحویل داروهای سرطان و پزشکی ترمیمی: نوآوریها و چالشها |
| عنوان لاتین مقاله | The Role of Artificial Intelligence (AI) in Advancing Cancer Drug Delivery and Regenerative Medicine: Innovations and Challenges |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | دومین کنگره بین المللی هوش مصنوعی در علوم پزشکی |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | تهران |
| سال انتشار/ ارائه شمسی | 1404 |
| سال انتشار/ارائه میلادی | 2025 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1404/02/24 الی 1404/02/26 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://aims.smums.ac.ir |
| آدرس علمی (Affiliation) نویسنده متقاضی | Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| پریناز نژادمختاری | اول |
| میراحمد مظلومی | دوم |
| سارا الماسی نژاد | سوم |
| عنوان | متن |
|---|---|
| کلمات کلیدی | ArtificialIntelligence, CancerDrugDeliverySystems, RegenerativeMedicine, PersonalizedTherapies, MachineLearningAlgorithms |
| خلاصه مقاله | ABSTRACT Background and aims: Artificial Intelligence (AI) employs advanced algorithms and computational models to replicate human cognitive functions such as learning, pattern recognition, and decision-making. In cancer drug delivery and regenerative medicine, AI is revolutionizing how therapies are developed, optimized, and administered. By leveraging large-scale data analysis, AI enables the design of targeted drug delivery systems and the development of personalized regenerative treatments, ultimately improving patient outcomes. This study examines the transformative potential of AI in these domains while also addressing the ethical and technical challenges associated with its implementation. Method: This systematic review was conducted using databases such as PubMed, Scopus, and Google Scholar to identify relevant studies published between 2020 and 2025. Keywords included 'AI in cancer drug delivery,' 'AI in regenerative medicine,' 'smart drug delivery systems,' and 'AI-driven tissue engineering.' In the end, among the 60 articles obtained, 18 articles have been evaluated with the checklist of review articles. Results: AI has significantly advanced drug delivery by enabling developing smart systems that can exactly control the release of therapeutics, optimize dosing, and target specific tissues or cells. Machine learning algorithms analyze pharmacokinetic and pharmacodynamic data to predict drug behavior and improve formulation design. In regenerative medicine, AI facilitates the creation of biomaterials, scaffolds, and tissue engineering strategies tailored to individual patients. However, the integration of AI in these fields is not without challenges. Ethical concerns, such as data privacy and algorithmic bias, are prominent, particularly when patient-specific data is used. Additionally, the complexity of AI systems raises questions about transparency, accountability, and the potential for unintended consequences in clinical applications. Conclusion: AI is a powerful tool for advancing cancer drug delivery and regenerative medicine, offering unprecedented opportunities for precision and personalization. However, its successful implementation requires addressing ethical, regulatory, and technical challenges. Researchers and clinicians must collaborate to ensure that AI-driven innovations are safe, equitable, and effective. By overcoming these challenges, AI has the potential to transform healthcare, enabling more efficient drug delivery systems and groundbreaking regenerative therapies that improve patients' quality of life. |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| 7ddc8cdce4a6628bc53c297dec623cca.jpg | 1404/03/21 | 425924 | دانلود |
| Abstract (Parinaz).pdf | 1404/03/21 | 105558 | دانلود |
