Penhaskashi Jaden, Danesh Jonah, Naeim Arya, Golshirazi Josh, Hedvat Justin, Chiappelli Francesco
Division of West Valley Dental Implant Center, Encino, California 91316.
University of California, Los Angeles.
Bioinformation. 2025 Feb 28;21(2):105-109. doi: 10.6026/973206300210105. eCollection 2025.
The integration of Artificial Intelligence (AI) in to the field of medicine is offering a new-age of updated diagnostics, prediction and treatment across multiple fields, addressing systemic disease including viral infections and cancer. The fields of Oral Pathology, Dermatology, Psychiatry and Cardiology are shifting towards integrating these algorithms to improve health outcomes. AI trained on biomarkers ( salivary cf DNA) has shown to uncover the genetic linkage to disease and symptom susceptibility. AI-enhanced imaging has increased sensitivity in cancer and lesion detection, as well as detecting functional abnormalities not clinically identified. The integration of AI across fields enables a systemic approach to understanding chronic inflammation, a central driver in conditions like cardiovascular disease, diabetes and neuropsychiatric disorders. We propose that through the use of imaging data with biomarkers like cytokines and genetic variants, AI models can better trace the effects of inflammation on immune and metabolic disruptions. This can be applied to the pandemic response, where AI can model the cascading effects of systemic dysfunctions, refine predictions of severe outcomes and guide targeted interventions to mitigate the multi-systemic impacts of pathogenic diseases.
人工智能（AI）融入医学领域正在开启一个新时代，为多个领域带来更新的诊断、预测和治疗方法，应对包括病毒感染和癌症在内的全身性疾病。口腔病理学、皮肤病学、精神病学和心脏病学领域正在转向整合这些算法，以改善健康状况。基于生物标志物（唾液cfDNA）训练的人工智能已显示出能够揭示疾病的遗传联系和症状易感性。人工智能增强成像提高了癌症和病变检测的灵敏度，还能检测出临床上未发现的功能异常。跨领域整合人工智能能够采用系统方法来理解慢性炎症，而慢性炎症是心血管疾病、糖尿病和神经精神疾病等病症的核心驱动因素。我们认为，通过将成像数据与细胞因子和基因变异等生物标志物相结合，人工智能模型能够更好地追踪炎症对免疫和代谢紊乱的影响。这可应用于应对大流行，人工智能能够模拟系统功能障碍的连锁反应，完善对严重后果的预测，并指导针对性干预措施，以减轻致病疾病的多系统影响。
