Leveraging Artificial Intelligence and Real World Data for Novel Insights in Healthcare.

This issue aims to showcase how machine learning (ML) and Artificial Intelligence (AI) approaches can be used with Real World Data (RWD) sources to generate novel insights to benefit healthcare. This could be AI/ML applications with RWD for improved clinical decision making across screening for disease risk factors, diagnosis, prognosis, treatment optimisation and prediction of treatment adverse effects. Papers for this special issue must demonstrate the added value of combining AI/ML approaches with RWD. Computer modelling and simulation approaches that use RWD to inform clinical trial design as well as support or strengthen evidence from clinical trials, would also be within scope.RWD are defined as data relating to patient health status or delivery of health care collected outside of a clinical study. Sources of RWD could include electronic healthcare records (EHR) defined as structured, digital collections of patient level medical data, primary and secondary care records, disease registries, and administrative data on births and deaths. RWD may include free text (unstructured) clinical notes and imaging data. Other sources of RWD include patient reported outcomes (PRO) data and data which are collected outside of a clinical study setting such as through wearable devices, specialised/secure websites, or tablets.The following types of submissions will be welcome:• Literature reviews to identify the current state of the art and the opportunities for future research developments relating to the use of AI/ML with RWD• Methodological papers with proof-of-concept or exemplar studies to showcase the value of an innovative AI/ML methodological approach that can be used with RWD• Methodological papers on handling of biases when using AI/ML approaches with RWD with demonstrator case studies• Original research using AI/ML with RWD to uncover novel insights in any clinical area in any of the following aspects: identification of new disease risk factors, diagnosis, discovery of new disease phenotypes, disease progression, treatment response (effectiveness and safety), treatment optimisation, repurposing of licensing drug treatments for other indicationsContributions to framework or standards development for research using AI/ML approaches with RWD, including evaluation frameworks to assess research outputs. Topic editors: Puja Myles, Sinead Brophy, Susan Hodgson Source: https://www.frontiersin.org/research-topics/79926/leveraging-artificial-intelligence-and-real-world-data-for-novel-insights-in-healthcare