The possible benefits and limits of artificially generated data in the context of healthcare analytics.
By Pooja Toshniwal PahariaOct 11 2023Reviewed by Lily Ramsey, LLM In a recent perspective article published in npj Digital Medicine, researchers discussed the possible benefits and limits of artificially generated data in the context of healthcare analytics.
About the perspective In the present perspective, researchers reviewed synthetic data usage, applications, challenges, and limitations in the health sector. Further, synthetic data may be utilized to assess the influence of policies on health outcomes, including morbidity, community assistance, and doctor conduct. Clinical difficulties involving several people and pandemics such as the coronavirus disease 2019 might benefit from synthetic data.
Limitations and challenges of synthetic data use The artificially generated information is useful for risk assessment in clinical scenarios. However, it also has drawbacks, such as modeling inaccuracy, poor interpretability, and a lack of effective tools for verifying data quality. In cases where XAI approaches fail to evaluate data correctness and representativeness, robust auditing procedures are required. Machine learning-based models and advanced statistical approaches can effectively assess the similarities between real-world and synthetic datasets, improving data representativeness.
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