Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

• Published in: IEEE reviews in biomedical engineering Vol. 16; pp. 53 - 69
• Main Authors: Isgut, Monica, Gloster, Logan, Choi, Katherine, Venugopalan, Janani, Wang, May D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; artificial intelligence algorithm development; Cleaning process; Computer applications; Control methods; Coronaviruses; COVID-19; COVID-19 pandemic; Data Accuracy; Data collection; data harmonization; Data integrity; Data quality; deep learning; Health care; Humans; Medical diagnostic imaging; Missing data; noise and artifact; Pandemics; Quality control; Standardization; tabular errors; Tabulation
• ISSN: 1937-3333; 1941-1189; 1941-1189
• DOI: 10.1109/RBME.2022.3216531

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.