Parvin Nargish, Joo Sang Woo, Jung Jae Hak, Mandal Tapas K
School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Nanomaterials (Basel). 2025 Jun 10;15(12):895. doi: 10.3390/nano15120895.
Multimodal artificial intelligence (AI) is driving a paradigm shift in modern biomedicine by seamlessly integrating heterogeneous data sources such as medical imaging, genomic information, and electronic health records. This review explores the transformative impact of multimodal AI across three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine. In the realm of biomaterials, AI facilitates the design of patient-specific solutions tailored for tissue engineering, drug delivery, and regenerative therapies. Advanced tools like AlphaFold have significantly improved protein structure prediction, enabling the creation of biomaterials with enhanced biological compatibility. In diagnostics, AI systems synthesize multimodal inputs combining imaging, molecular markers, and clinical data-to improve diagnostic precision and support early disease detection. For precision medicine, AI integrates data from wearable technologies, continuous monitoring systems, and individualized health profiles to inform targeted therapeutic strategies. Despite its promise, the integration of AI into clinical practice presents challenges such as ensuring data security, meeting regulatory standards, and promoting algorithmic transparency. Addressing ethical issues including bias and equitable access remains critical. Nonetheless, the convergence of AI and biotechnology continues to shape a future where healthcare is more predictive, personalized, and responsive.
多模态人工智能（AI）通过无缝整合医学成像、基因组信息和电子健康记录等异构数据源，正在推动现代生物医学的范式转变。本综述探讨了多模态人工智能在三个关键领域的变革性影响：生物材料科学、医学诊断和个性化医疗。在生物材料领域，人工智能有助于设计针对组织工程、药物递送和再生疗法的个性化解决方案。像AlphaFold这样的先进工具显著提高了蛋白质结构预测能力，从而能够创造出具有更高生物相容性的生物材料。在诊断方面，人工智能系统综合成像、分子标记和临床数据等多模态输入，以提高诊断精度并支持疾病早期检测。对于精准医疗，人工智能整合来自可穿戴技术、连续监测系统和个性化健康档案的数据，为靶向治疗策略提供依据。尽管前景广阔，但将人工智能整合到临床实践中仍面临诸多挑战，如确保数据安全、符合监管标准以及提高算法透明度。解决包括偏差和公平获取在内的伦理问题仍然至关重要。尽管如此，人工智能与生物技术的融合继续塑造着一个医疗保健更具预测性、个性化和响应性的未来。
