Artificial Intelligence has made remarkable progress in the field of healthcare, especially with the use of Machine Learning (ML). The basis of machine learning is the data fed into the algorithms, which is then used to deliver healthcare solutions.
However, disparities exist in the research data and the datasets collected are at times exclusionary of certain sections of society. For example, health care providers in the U.S. were known to use an algorithm that privileged the extra care of white patients over black patients.
If the developers of machine-learning technologies fail to check their biases regarding race and sex, the healthcare solutions provided will not equitable. To eliminate such partiality, strategies need to be employed from the conceptualization stage itself. It is crucial ML algorithms consider a diverse variety of patients, cases, and backgrounds to ensure the accuracy and fairness of results. Otherwise, the lack of diverse data will continue to perpetuate inequalities existing in healthcare and society at large.