Akin to other industries, the healthcare sector is also undergoing the process of embracing artificial intelligence (AI) and machine learning, which provides a potential revolution in operations, patient care, and security. Creators of AI tools focus on AI and machine learning to leverage these technologies to improve the healthcare and other realms.
The datasets of the healthcare sector are relatively smaller when compared to other consumer and business applications. Unlike AI tools of other sectors, healthcare AI tools depend on data sets having orders of magnitude much smaller and therefore demands AI developers to possess a deeper understanding of the data and industry knowledge since data interpretation and coding mistakes are amplified in smaller data sets. Additionally, the real world applicability should be a priority.
Scalability is one of the biggest challenges faced by machine learning adoption across the healthcare industry. To be precise, an algorithm may function faultlessly in a limited clinical setting or controlled academic but translating this to a real-world application can give rise to numerous complications. There is a high possibility for AI tools to not work properly in a regular hospital where numerous patients possess medical records that are incomplete if it had been trained by using data from a research hospital. Processing speed and data cleanliness can be major barriers external to the clean environment for research applications. It is a necessity for the healthcare organizations to make sure that AI tools are trained and validated with representative populations.