Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review

Abstract

Modern medicine, both in clinical practice and research, has become more and more based on data, which is changing equally in type and quality with the advent and development of healthcare digitalization. The first part of the present paper aims to present the steps through which data, and subsequently clinical and research practice, have evolved from paper-based to digital, proposing a possible future of this digitalization in terms of potential applications and integration of digital tools in medical practice. Noting that digitalization is no more a possible future, but a concrete reality, there is a strong need for a new definition of evidence-based medicine, which must take into account the progressive integration of artificial intelligence (AI) in all decisionmaking processes. So, leaving behind the traditional research concept of human intelligence versus AI, poorly adaptable to real-world clinical practice, a Human and AI hybrid model, seen as a deep integration of AI and human thinking, is proposed as a new healthcare governance system. The second part of our review is focused on some of the major challenges the digitalization process has to face, particularly privacy issues, system complexity and opacity, and ethical concerns related to legal aspects and healthcare disparities. Analyzing these open issues, we aim to present some of the future directions that in our opinion should be pursued to implement AI in clinical practice.

Exploring the current and prospective role of artificial intelligence in disease diagnosis

“…Between 1950 and 1960, AI found its early application in digitising patient health records into electronic health records, facilitating the documentation of a broad range of information including diagnostic procedures, therapeutic interventions, and laboratory biomarkers. This digital transformation in medical literature paved the way for the development of databases such as PubMed Central (PMC) by the National Library of Medicine (NLM), revolutionising data retrieval and clinical informatics for research purposes 9 – 11 .…”

“…These data require AI applications and machine-learning algorithms for comprehensive analysis and integration. However, it should be noted that machine learning constitutes a shallower facet of AI than deep learning 11 , 12 . From the 1970s to the 2000s, AI continued to evolve in various phases.…”

“…The foundation of CNNs, referred to as LeNet, was established in 1998. However, owing to computational limitations and insufficient training of new datasets, its progress slowed down until the 2010s when full fruition was achieved 11 , 14 . Following the resolution of these limitations and drawbacks, deep learning has gained prominence in disease prognosis, imaging, and prediction of drug response.…”