ABSTRACT
From the very start of the last decade, Deep Learning has made many witnesses of its disruptive power in almost every field, and Healthcare is no exception. It all started with Deep Learning ability to collect and process extensive collections of medical record data for better and optimized results. It built upon the reputation for being able to deal with many time-consuming and strenuous tasks with the help of Electronic Health Record systems, which stored all the patient-related data digitally. It now became much easier to keep track of the data and organize it systematically. In the computer vision domain, AI showed promise by enabling systems with the ability to detect disease more effectively, like diagnosing and imaging. Some noteworthy applications were cancer detection, diabetes detection, and radiology assistants. Natural Language Processing-based products include the use of chat and voice-based agents who could help in the process screening of the patients by asking a series of questions at much faster rates compared to traditional methodologies. It can give doctors more time to work on a more complicated part such as surgeries and other treatments.
The drug discovery process was also much more manageable as Deep Learning enabled scientists and researchers to study various chemical structures and experiments more efficiently. Using the Crystal Structure Prediction process, new medicine could be proposed in some days’ time as compared to weeks or months using previous technologies. Doctors even make use of AI-enabled surgery assistants that can provide better precisions in specific tasks way above a human’s level. However, even with all these applications, AI cannot be treated as the go-to solution for any problem. The main reason for this is AI’s ability to explain the reason it arrived at a result of the interpretability of the models. If a Chabot detects individual patterns with a person during its interaction with reasonable confidence, should we proceed to the next stages like cure or treatment? It cannot be answered with proper justification and the reason being the lack of human ability to decode the underlying cause that resulted in a specific output.
The AI-based models work as good as a clear and precise representation of the data is available to them; representation is still a significant issue to be resolved. There are no sure ways to represent the data and get good results every time. So, it can be agreed that fully autonomous AI-based medical applications are still far in the future. Even with the shortcomings, Deep Learning is aiding many medical sectors effectively.
