NLP uncovers racial bias in documenting ob/gyn imaging findings

Natural language processing (NLP), a form of AI, can uncover racial bias in documenting radiology-confirmed ob/gyn findings, a study published April 21 in Clinical Imaging reported. Researchers led by Shoshana Haberman, MD, PhD, from the Maimonides Medical Center in Brooklyn, NY, found that NLP-derived data incorporated into a platform for ultrasound or MRI-confirmed uterine fibroids during pregnancy showed that Black patients were more likely to have an obstetrical diagnosis entered late into their patient charts or have missing documentation of the diagnosis. “All providers should be committed to quality documentation of clinically relevant diagnoses and quality of care, without regard to race,” Haberman and co-authors wrote. Racial bias can be found in medical records, leading to potentially flawed documentation. The researchers highlighted that finding out whether there are racial disparities in documentation and in quality of care is important for improving performance. NLP allows for free text-based clinical documentation to be integrated in ways that facilitate data analysis, data interpretation, and formation of individualized medical and obstetrical care. For their study, Haberman and colleagues identified all births between 2019 and 2021 carrying the radiology-confirmed diagnosis of fibroid uterus in pregnancy 5 cm or larger. These were detected by either pelvic ultrasound or MRI The researchers used an NLP platform and compared it to non-NLP derived data ...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Womens Imaging Artificial Intelligence Source Type: news