Simpson Maree Donna, Qasim Haider Saddam
School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW 4118, Australia.
School of Computer Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia.
Pharmacy (Basel). 2025 Mar 7;13(2):41. doi: 10.3390/pharmacy13020041.
Over the past five years, the application of artificial intelligence (AI) including its significant subset, machine learning (ML), has significantly advanced pharmaceutical procedures in community pharmacies, hospital pharmacies, and pharmaceutical industry settings. Numerous notable healthcare institutions, such as Johns Hopkins University, Cleveland Clinic, and Mayo Clinic, have demonstrated measurable advancements in the use of artificial intelligence in healthcare delivery. Community pharmacies have seen a 40% increase in drug adherence and a 55% reduction in missed prescription refills since implementing artificial intelligence (AI) technologies. According to reports, hospital implementations have reduced prescription distribution errors by up to 75% and enhanced the detection of adverse medication reactions by up to 65%. Numerous businesses, such as Atomwise and Insilico Medicine, assert that they have made noteworthy progress in the creation of AI-based medical therapies. Emerging technologies like federated learning and quantum computing have the potential to boost the prediction of protein-drug interactions by up to 300%, despite challenges including high implementation costs and regulatory compliance. The significance of upholding patient-centred care while encouraging technology innovation is emphasised in this review.
在过去五年中，包括其重要子集机器学习（ML）在内的人工智能（AI）应用显著推动了社区药房、医院药房及制药行业环境中的制药流程。众多著名的医疗机构，如约翰·霍普金斯大学、克利夫兰诊所和梅奥诊所，在医疗服务中使用人工智能方面都取得了可衡量的进展。自实施人工智能（AI）技术以来，社区药房的药物依从性提高了40%，错过的处方续配减少了55%。据报道，医院实施人工智能后，处方分发错误减少了多达75%，药物不良反应检测能力提高了多达65%。许多企业，如Atomwise和Insilico Medicine，声称他们在基于人工智能的药物治疗研发方面取得了显著进展。尽管存在实施成本高和合规监管等挑战，但联邦学习和量子计算等新兴技术有潜力将蛋白质-药物相互作用的预测提高多达300%。本综述强调了在鼓励技术创新的同时坚持以患者为中心的护理的重要性。
