Expending the power of artificial intelligence in preclinical research: an overview

Abstract

Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs, interpret and learn from such feedbacks, and display related and flexible independent actions that help the entity reach a specific aim over a period of time. By extending its health-care applications continuously, the ultimate AI target is to use machine simulation of human intelligence processes such as learning, reasoning, and self-correction, to mimic human behaviour. AI is extensively used in diverse sectors of medicine, including clinical trials, drug discovery and development, understanding of target-disease associations, disease prediction, imaging, and precision medicine. In this review, we firstly describe the limitations and challenges of the AI tools and techniques utilized in medicine, followed by current uses and applications of AI in the translational field, highlighting the cardio-renal preclinical models with potential to contribute to future clinical research.

The Role of Artificial Intelligence in the Diagnosis of Neoplastic Diseases: A Systematic and Bibliometric Review

“…Thus, we can conclude that this research has been conducted by analyzing prestigious journals. The similarity with the author Jakob Nikolas Kather [55] is appreciated. In his research paper, he explains that the most cited journals are in Q1, referring to the quartiles.…”

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges

“…AI‐driven computational tools have become game‐changers in the realm of preclinical studies for new drug discovery. These innovative tools leverage the power of AI and computational methods to accelerate and enhance various aspects of preclinical research (Diaconu et al, 2022; Honek, 2017; Olson et al, 2000; Vijayan et al, 2022). …”

“…Consequently, these tools significantly contribute to the identification of promising drug candidates and aid in the development of novel and effective drugs, ultimately leading to improved patient outcomes.1.2.2 | Pre clinical studies and new drug discovery AI-driven computational tools have become game-changers in the realm of preclinical studies for new drug discovery. These innovative tools leverage the power of AI and computational methods to accelerate and enhance various aspects of preclinical research(Diaconu et al, 2022;Honek, 2017;Olson et al, 2000;Vijayan et al, 2022).1. Target identification: Through sophisticated AI algorithms, these tools can analyze vast and complex biological data sets, such as genomic and proteomic data, to identify potential drug targets.…”