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Most new drug approvals are based on data from large randomized clinical trials (RCTs). However, there are sometimes contradictory conclusions from seemingly similar trials and generalizability of conclusions from these trials is limited. These considerations explain, in part, the gap between conclusions from data of RCTs and those from registries termed real world data (RWD). Recently, real-world evidence (RWE) from RWD processed by artificial intelligence has received increasing attention. We describe the potential of using RWD in haematology concluding RWE from RWD may complement data from RCTs to support regulatory decisions., Competing Interests: Declaration of competing interest RPG is a consultant to BeiGene Ltd., Fusion Pharma LLC, La Jolla NanoMedical Inc., Mingsight Parmaceuticals Inc. and CStone Pharmaceuticals; advisor to Antegene Biotech LLC, Medical Director, FFF Enterprises Inc.; partner, AZAC Inc.; Board of Directors, Russian Foundation for Cancer Research Support; and Scientific Advisory Board: StemRad Ltd: F Passamonti received honoraria for lectures and advisory boards from Novartis, GSK, Bristol-Myers Squibb/Celgene, Sierra Oncology, AbbVie, Janssen, Roche, AOP Orphan, Karyopharma, Kyowa Kirin and MEI, Sumitomo., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology., Methods: A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data., Results: We generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients., Conclusion: Synthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific use and value of real data, thus accelerating precision medicine in hematology and the conduction of clinical trials.

Most national health-care systems approve new drugs based on data of safety and efficacy from large randomized clinical trials (RCTs). Strict selection biases and study-entry criteria of subjects included in RCTs often do not reflect those of the population where a therapy is intended to be used. Compliance to treatment in RCTs also differs considerably from real world settings and the relatively small size of most RCTs make them unlikely to detect rare but important safety signals. These and other considerations may explain the gap between evidence generated in RCTs and translating conclusions to health-care policies in the real world. Real-world evidence (RWE) derived from real-world data (RWD) is receiving increasing attention from scientists, clinicians, and health-care policy decision-makers - especially when it is processed by artificial intelligence (AI). We describe the potential of using RWD and AI in Hematology to support research and health-care decisions., (Copyright © 2021 Elsevier Ltd. All rights reserved.)