Drawing Connections: Artificial Intelligence to Address Complex Health Challenges

Patrick J Seitzinger, J. Kalra
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Abstract

Pattern recognition is a cornerstone of clinical care and public health practice. Historically, advances in medicine have relied on the ability of humans to detect patterns and make inferences. Modern healthcare challenges involve vast amounts of data and a level of complexity that require additional support to understand. The advancement of Artificial Intelligence has expanded our capability to detect, understand, and address patterns that were previously beyond our grasp. Artificial Intelligence has the capability to analyze otherwise insurmountable quantities of data in order to bring meaning and clarity to patterns that were previously deemed random or unintelligible. We, therefore, aim to charter a strategic path forward for innovative applications of Artificial Intelligence technology to understand and address pressing complex health challenges. The modelling capabilities of Artificial Intelligence have allowed for the simulation of potential viral mutations, as well as the development of therapeutic agents. The predictive analyses provided by Artificial Intelligence allow for a more holistic yet precise understanding of the aging process and the progression of disease, thereby allowing the extent and timing of treatments to be optimized. It has brought a new lens through which to identify malignant cells on imaging and to decode parts of the human genome previously labelled as sequences of unknown significance. On a global scale, Artificial Intelligence has given us the opportunity to better understand and anticipate the effects of climate change on health including the effects on displacement and the potential spillover and spread of new zoonotic infection diseases. We suggest how Artificial Intelligence is beginning to re-conceptualize our understanding of health and disease. The implementation of Artificial Intelligence is a pivotal time in developmental of other modern era of medical practice and public health strategies. Appropriate utilization of these new tools requires innovative thinking, critical appraisal, and tactful resource allocation to ensure issues are addressed in a timely and feasible manner.
绘制连接:人工智能解决复杂的健康挑战
模式识别是临床护理和公共卫生实践的基石。从历史上看,医学的进步依赖于人类检测模式和推断的能力。现代医疗保健挑战涉及大量数据和一定程度的复杂性,需要额外的支持才能理解。人工智能的进步扩大了我们检测、理解和处理以前无法掌握的模式的能力。人工智能有能力分析否则无法克服的大量数据,以便为以前被认为是随机或难以理解的模式带来意义和清晰度。因此,我们的目标是为人工智能技术的创新应用开辟一条战略道路,以理解和解决紧迫的复杂健康挑战。人工智能的建模能力已经允许模拟潜在的病毒突变,以及治疗剂的开发。人工智能提供的预测分析可以更全面、更精确地了解衰老过程和疾病的进展,从而优化治疗的范围和时间。它带来了一种新的视角,通过它可以在成像上识别恶性细胞,并解码以前被标记为未知意义序列的人类基因组部分。在全球范围内,人工智能使我们有机会更好地了解和预测气候变化对健康的影响,包括对流离失所的影响以及新的人畜共患传染病的潜在溢出和传播。我们建议人工智能如何开始重新定义我们对健康和疾病的理解。人工智能的实施是其他现代医疗实践和公共卫生战略发展的关键时期。适当利用这些新工具需要创新思维、批判性评估和机智的资源分配,以确保问题得到及时和可行的解决。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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