About this Research Topic
Decision-making is at the core of every industry. Artificial intelligence (AI) augments decision-making by enhancing human thinking and doing. Although AI has a great potential to navigate complex and high-dimensional data, its’ use has been limited to knowledge domains. The inertia of the application of AI to human-defined categorical knowledge is inherited with the scientific method. From the emergence of the scientific method that established how knowledge is to be acquired and organized, science is based on the collection of observable and measurable evidence through experimentation.
It is currently organized on historical categories. In addition, technological constraints have always dictated the quality, the quantity and the granularity of information. Thus, the classification and categorization of problems have often followed the results of technological advances; we have already started classifying diseases with genomics enabled from the sequencing technology, and diagnose with digital biomarkers empowered from sensors and smart devices. Therefore, the advancement of artificial intelligence and statistics has resulted in fast and accurate answers but limited to domain knowledge questions asked to the data, fragmented and focused in historical categorical fields. Cardiology still persists as a field nevertheless the updated genomic and metabolic molecular definition of disease.
In network fields like medicine and business, where interdependences can result in impactful butterfly effects in the health of a person or the dynamics of a company, organization or country, AI has been selectively queried based on fragmented and designated data, often inheriting bias. We propose the use of integrated intelligence (I2) as an application of artificial intelligence to domain-free data in order to support decisions and predict outcomes in healthcare and business.
We invite authors to submit original research and review articles that advance and address challenges in integrated intelligence, where AI is questioned with a scalable scope, is trained on more inclusive information data types and is tested beyond disciplines such as social-health-business, molecular-phenotypic, environmental-medical.
Keywords: integrated intelligence, artificial intelligence, decision making, social health business, healthcare
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