Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics

人工智能 计算机科学 分子诊断学 个性化医疗 注意事项 精密医学 机器学习 医学物理学 医学 生物信息学 病理 生物
作者
Michael McRae,Kritika Srinivasan Rajsri,Timothy M. Alcorn,John T. McDevitt
出处
期刊:Sensors [MDPI AG]
卷期号:22 (17): 6355-6355 被引量:10
标识
DOI:10.3390/s22176355
摘要

We are beginning a new era of Smart Diagnostics-integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.
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