Machine Learning and Artificial Intelligence in Evidence Generation and Evidence Synthesis

Abstract

INTRODUCTION Suboptimal decision-making in healthcare contributes to decreased patient outcomes (health status and/or quality of life), increased healthcare costs, inequitable access to care, patient dissatisfaction and legal and ethical issues.[1] To mitigate these negative consequences, healthcare organisations and professionals should prioritise evidence-based practices, promote interdisciplinary collaboration, invest in on-going education and training and implement quality improvement initiatives to optimise decision-making. Evidence-based medicine (EBM) is a knowledge driven framework, while machine learning (ML) and artificial intelligence (AI) provide data-driven knowledge. Bridging the gap between these approaches requires a balanced approach that addresses ethical, legal, biased and transparent aspects during the process of evidence generation, evidence gap analysis and evidence synthesis. Integrating reliable processes in EBM decision-making is essential for clinical practice.[2,3] Transforming evidence into algorithmic clinical guidelines helps standardise practice and improve treatment outcomes. Ethical challenges e.g., informed consent, transparency, fairness of algorithms used in AI and privacy and security of data. This can be addressed by leveraging scientific advancements from ML and AI tools.[4] In evidence-based practice slow evidence generation process[5] makes its adoption challenging.[6] The time lag between generating evidence and effectively implementing it in clinical settings can be as long as 17 years.[7] Studies have shown that citizens only receive half of the recommended healthcare solutions available under EBM.[8] The healthcare industry is witnessing a revolutionary change from emerging technologies, this progress requires high-throughput sophisticated algorithms enabling practitioners to analyse evidence more quickly and efficiently.[9] With patients increasingly relying on social media and smart devices for health information and self-management, it is crucial to consider the role of AI and ML in evidence-based practice in today’s tech-driven world,[10] where social media can influence the healthcare seeking behaviour among patient. In the rapidly evolving landscape of research and evidence-based practice, AI has emerged as a game-changer. Its transformative potential is evident in various stages of the research process, from evidence generation, to evidence gap analysis and evidence synthesis. As the scientific community strives to push the boundaries of knowledge, AI offers unprecedented opportunities to augment research capabilities, accelerate progress and bridge critical gaps.[11] The emergence of e-Health records has generated vast data in healthcare sectors. More than 10 million consultations have happened on e-Sanjeevani alone in India, which is based on e-Health records used for telemedicine. Use of AI in this vast data opens the opportunities to improve clinical decision-making and bridge gaps in knowledge. ML has capacity to handle this vast data and addressing the challenges in decision making. However, the adoption of ML in healthcare is still limited, and its full benefits remain unrealised. Integration between ML and EBM faces epistemological, methodological and ethical challenges. EBM’s dominance in clinical sciences poses significant hurdles to the adoption of data science.[11] Individual patient database has been considered as the gold standard form of meta-analysis can make use of AI and ML tool easily. MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN EVIDENCE-GENERATION The process of evidence generation is resource-intensive, time-consuming and ethically challenged in terms of informed consent, transparency, privacy and security of data. However, ML and AI-powered tools and algorithms can significantly enhance and streamline this process. ML algorithms assist in identifying research questions, robust study designing and optimising data collection methodologies liberates res