The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies

da Silva Globalization and Health (2024) 20:44 https://doi.org/10.1186/s12992-024-01049-5 Globalization and Health Open Access D E B AT E The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies Renan Gonçalves Leonel da Silva1* Abstract The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as selfdriving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years. *Correspondence: Renan Gonçalves Leonel da Silva Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. da Silva Globalization and Health (2024) 20:44 Page 2 of 19 Keywords Artificial intelligence, Autonomous experimentation systems, Self-driving lab, Research and development (R&D), Drug discovery, Biomedical research, Health, Innovation, Emerging economies Background In January 2023, news reverberated across media outlets dedicated to breakthroughs innovations in biotechnology and in the healthcare sector. It announced the initiation of clinical trials for a protein kinase inhibitor INS018_055 – the first anti-fibrotic small molecule inhibitor with promising anti-tumor relevance, designed through the assistance of artificial intelligence (AI). INS018_055 was developed by Insilico Medicine, a generative AI-driven clinical-stage biotechnology company. The discovery of INS018_055 was achieved by a team of researchers from Canada, China, and the United States within the span of less than a month, with results published in Chemical Sciences [1]. According to a press release from Genetic Engineering & Biotechnology News (2023) the study “applied AlphaFold [an AI program which performs predictions of protein structure developed by DeepMind, a subsidiary of Alphabet] to an end-to-end AI-powered drug discovery platform (Pharma.AI) that includes a biocomputational engine (PandaOmics) and a generative chemistry platform (Chemistry42), to identify a new drug for a novel target for the treatment of the most common form of primary liver cancer, hepatocellular carcinoma.” [2]. The news of INS018_055’s success circulated globally, highlighting it as a promising result of integrating AI in biomedical research and drug discovery. The AI-generated protein illustrates the potential of the so-called autonomous experimentation (AE) systems to enhance and accelerate the discovery of advanced biochemical entities and responsive bionanomaterials of interest in clinical studies and biopharmaceutical industry. Also known as autonomous laboratories, self-driven laboratories, or materials acceleration platforms, AE systems are digital platforms capable of running a large number of chemical experiments autonomously. AE are assisted by machine learning (ML) and other robust computational tools with a high level of precision, accuracy and resilience. Those systems can perform in days what scientists would take years to achieve, as proven by the example of INS018_055. Instead of manually replicating experiments and trial-and-error activities, AE systems build robust datasets and run experiments without the physical and intellectual limitations of humans. It reduces the risk for subjective interpretations of findings, due to data robustness and ML-driven hypothesis tests [3–5]. Due to its efficiency in accelerating discovery and rationalizing the use of scarce material resources for R&D activity, AE is expected to have a significant impact on biomedical research. Specifically, areas such as chemical engineering and materials sciences, bioengineering and drug discovery, and molecular systems engineering, are propelling a dynamic pipeline of technologies and solutions of interest for the healt (...truncated)