Abstract
An important safety problem in healthcare is diagnostic error, with major errors being found in 10%-20% of autopsies. This suggests that an average of 60,000 patients die annually in the United States from error during diagnosis. Medical diagnosis is a critical task that must be performed efficiently and accurately to ensure adequate patient treatment. Factors including lack of communication, doctor inexperience, and lack of time with patients contribute to these errors. With the complexity and rise of data in healthcare, machine learning (ML) technology has the potential to transform many aspects of patient care including diagnosing diseases. There are studies suggesting that artificial intelligence (AI) can perform as well as or better than humans at key healthcare tasks such as identifying a malignant tumor. Google’s ML applications in healthcare detected breast cancer with 89% accuracy, which is an 11% increase compared to currently used diagnostic methods. ML is a common form of AI that ‘learns’ by training models with data and can be used for tasks like personalized treatment, medical imaging, and robotic surgery. In 2021, about 90% of hospitals in the United States had some sort of AI strategy in place, with 75% of healthcare executives believing that AI initiatives are crucial for hospital success. However, out of that 90%, only 33% of hospitals had systems past the pilot stage in place due to lack of infrastructure or financial means. Lack of access to certain AI technologies, such as robotic surgical systems, may be detrimental to hospitals and patients seeking treatment. Automated medical imaging, a type of AI technology, is becoming more common since it is cost-effective for hospitals. Thus, the focus of this project is to develop a ML model for automated imaging of the heart. However, access to robotic surgery, another type of AI technology, varies greatly between hospitals. Therefore, the disparity in access to robotic surgery technology between demographic groups in the United States will be analyzed to discuss the need for stronger robotic surgery initiatives in hospitals.
