Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.

Vida Abedi,Aparna Tompe,Jiang Li,Alireza Vafaei Sadr,Venkatesh Avula,Ramin Zand,Asma Poursoroush,Seyed-Mostafa Razavi,Ayesha Khan

doi:10.3390/jcm10235710

Abstract

The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions—from heart failure to stroke—has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.

Highlights

We have seen that the field of artificial intelligence (AI) is omnipresent in both cardio and cerebrovascular fields, targeting different stages of patient management (Figure 2)
Future cerebrovascular studies can expand follow-up periods to better understand the long-term outcomes in the patients. Both cardio- and cerebrovascular fields can benefit from collaborative efforts to increase data diversity, patient representation, and integration of different data modalities, e.g., imaging biomarkers and genetic information
The limitations in AI-based models are mostly centered on the lack of sufficient patient representation, balanced cohorts, and biases introduced by cohort definitions or selection of variables, as well as the exclusion of a certain group of patients

Summary

Introduction

Artificial intelligence (AI) focuses on how computers learn from large and complex datasets by mimicking the human thought process. AI has the potential to accelerate the field of precision medicine by helping practitioners to calculate the risk, guide the treatment, predict the outcome, and close the care gap using scalable computational resources and advanced algorithms applied to a growing body of data and knowledge. AI can be designed to improve clinical care and increase efficiency in drug discovery [1]. Designed and implemented electronic health record (EHR)-AI embedded tools and applications can save valuable time and assist practitioners with critical decision-making at the point of care.

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