Challenges facing AI in healthcare
Digitization and consolidation of data
The garbage-in-garbage-out concept underpins the majority, if not all, AI initiatives. Without the vast amount of data supplied into the Artificial intelligence systems, obtaining results is almost impossible. This is why it is critical to acquire high-quality healthcare data — a process has become more complicated over the years. The challenge is related to the scattered and disorganized nature of health data, dispersed across several data systems throughout the world. Patients routinely switch insurance companies and healthcare organizations, making data collection difficult. Due to inadequate data quality and fragmented data systems, digitization and consolidation of this data are complex in certain nations(Jin et al., 2020). The situation is marginally better in the United States, where efforts are underway to accelerate the digitalization of medical systems. The quality of digital information, on the other hand, is not optimal. According to eClinical works, one of the largest record-keeping software companies in the system had a weakness in its design that placed patients in danger. The healthcare industry’s enhanced accuracy and efficiency are ensured with updated and digitized record systems. That is why stakeholders should optimize medical data digitalization and consolidation. This is the only method to provide the AI with data to correct analysis and optimize procedures.