Machine learning and artificial intelligence are currently being heralded as a way to address societal challenges, including healthcare. Vasant Honavar, professor and Edward Frymoyer Chair of Information Sciences and Technology, was recently featured as the author of the guest editorial for the Indian Journal of Ophthalmology to discuss the topic of machine learning in critical care settings.
Honavar’s editorial, Machine Learning in Clinical Care: Quo Vadis?, explains that despite the early doubt of artificial intelligence (AI) and machine learning (ML) in improving clinical care during the 1970s, evidence shows technological advancements are allowing for the collection of data outside of healthcare settings to prompt better health outcomes today.
Evidence also shows that Machine Learning may improve diagnoses and prognoses leaving a promising potential for ML in clinical care. However, clinicians need to be wary of biases and attain a comprehensive-understanding of ML algorithms function in order to effectively advance clinical care.
Honavar says that research is needed to enhance the explainability of ML before it can be fully trusted, and ethical consideration is necessary too for clinicians wanting to use ML. Despite such challenges, ML is still of growing interest in the progression of the medical field.