Vaghasiya Jatin, Khan Mahim, Milan Bakhda Tarak
Northeastern University, 360 Huntington Ave, Boston, MA 02115, United States.
Health Biotechnology Division, Pakistan Institute of Engineering and Applied Sciences, National Institute for Biotechnology and Genetic Engineering College, (NIBGE-C, PIEAS), Faisalabad, Punjab 38000, Pakistan.
Int J Med Inform. 2025 Mar;195:105768. doi: 10.1016/j.ijmedinf.2024.105768. Epub 2024 Dec 18.
Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies across various industries, including healthcare, biotechnology, and vaccine development. These technologies offer immense potential to improve project management efficiency, decision-making, and resource utilization, especially in complex tasks such as vaccine development and healthcare innovations.
A systematic meta-analysis was conducted by reviewing studies from databases like PubMed, IEEE Xplore, Scopus, Web of Science, EMBASE, and Google Scholar until September 2024. The analysis focused on the application of AI and ML in project management for vaccine development, biotechnology, and broader healthcare innovations using the PICO framework to guide study selection and inclusion. Statistical analyses were performed using Review Manager 5.4 and Comprehensive Meta-Analysis (CMA) software.
The meta-analysis reviewed 44 studies examining the integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, biotechnology, and vaccine development project management. Results demonstrated significant improvements in efficiency, resource allocation, decision-making, and risk management. AI/ML applications notably accelerated vaccine development, from candidate identification to clinical trial optimization, and improved predictive modeling for efficacy and safety. Subgroup analysis revealed variations in effectiveness across healthcare sectors, with the highest pooled effect sizes observed in infectious disease control (1.2; 95 % CI: 0.85-1.50) compared to medical imaging (0.85; 95 % CI: 0.75-0.95). Studies employing AI techniques demonstrated a pooled effect size of 0.83 (95 % CI: 0.78-1.08). Despite the observed high heterogeneity (I = 99.04 %) and moderate-to-high risks of bias, sensitivity analyses confirmed the robustness of the findings. Overall, AI/ML integration offers transformative potential to enhance project management and vaccine development, driving innovation and efficiency in these critical fields.
AI and ML technologies show significant potential to transform project management practices in healthcare, biotechnology, and vaccine development by enhancing efficiency, predictive analytics, and decision-making capabilities. Their integration paves the way for more innovative, data-driven solutions that can adapt to evolving challenges in these fields.
人工智能（AI）和机器学习（ML）已成为包括医疗保健、生物技术和疫苗开发在内的各个行业的变革性技术。这些技术具有巨大潜力，可提高项目管理效率、决策能力和资源利用率，尤其是在疫苗开发和医疗保健创新等复杂任务中。
通过检索截至2024年9月的PubMed、IEEE Xplore、Scopus、Web of Science、EMBASE和谷歌学术等数据库中的研究进行系统的荟萃分析。该分析聚焦于人工智能和机器学习在疫苗开发、生物技术及更广泛的医疗保健创新项目管理中的应用，使用PICO框架指导研究选择和纳入。使用Review Manager 5.4和综合荟萃分析（CMA）软件进行统计分析。
荟萃分析审查了44项研究，这些研究探讨了人工智能和机器学习在医疗保健、生物技术和疫苗开发项目管理中的整合情况。结果表明，在效率、资源分配、决策和风险管理方面有显著改善。人工智能/机器学习应用显著加速了疫苗开发，从候选疫苗识别到临床试验优化，并改善了疗效和安全性的预测模型。亚组分析揭示了不同医疗保健领域的效果差异，与医学成像（0.85；95%CI：0.75 - 0.95）相比，传染病控制领域观察到的合并效应量最高（1.2；95%CI：0.85 - 1.50）。采用人工智能技术的研究显示合并效应量为0.83（95%CI：0.78 - 1.08）。尽管观察到高度异质性（I = 99.04%）和中到高偏倚风险，但敏感性分析证实了研究结果的稳健性。总体而言，人工智能/机器学习的整合为加强项目管理和疫苗开发提供了变革潜力，推动了这些关键领域的创新和效率提升。
人工智能和机器学习技术通过提高效率、预测分析和决策能力，在改变医疗保健、生物技术和疫苗开发项目管理实践方面显示出巨大潜力。它们的整合为更具创新性、数据驱动的解决方案铺平了道路，这些解决方案能够适应这些领域不断演变的挑战。
