Naskar Sweet, Sharma Suraj, Kuotsu Ketousetuo, Halder Suman, Pal Goutam, Saha Subhankar, Mondal Shubhadeep, Biswas Ujjwal Kumar, Jana Mayukh, Bhattacharjee Sunirmal
Department of Pharmaceutics, Institute of Pharmacy, Kalyani, West Bengal, India.
Department of Pharmaceutics, Sikkim Professional College of Pharmaceutical Sciences, Sikkim, India.
J Drug Target. 2025 Jun;33(5):717-748. doi: 10.1080/1061186X.2024.2448711. Epub 2025 Jan 9.
A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.
计算机科学中一个名为人工智能（AI）的重要领域已成功应用于复杂生物数据的分析以及从数据集中提取大量关联信息，以用于各种生物医学用途。人工智能因其以下特点在生物医学研究中引起了极大关注：（i）通过早期诊断和检测提供更好的患者护理；（ii）优化工作流程；（iii）减少医疗差错；（v）降低医疗成本；（vi）降低发病率和死亡率；（vii）提高性能；（viii）提高精准度；（ix）提高时间效率。定量指标对于评估人工智能的应用、提供见解、做出明智决策以及衡量人工智能驱动举措的影响至关重要，从而提高透明度、问责制和整体影响力。人工智能在生物医学领域的应用面临着伦理和隐私问题、缺乏认识、技术不可靠以及专业责任等挑战。本文简要讨论了人工智能技术，其中包括虚拟筛选（VS）、深度学习（DL）、机器学习（ML）、隐马尔可夫模型（HMM）、神经网络（NN）、生成模型（GM）、分子动力学（MD）和构效关系（SAR）模型。该研究探讨了人工智能在生物医学领域的应用，强调了其提高预测准确性、治疗效果、诊断效率、加快决策速度、个性化治疗策略以及精确医疗干预的作用。
