Deep learning in digital health with chatgpt: a study on efficient code generation

Abstract

Abstract Background/Introduction Artificial Intelligence (AI) plays a significant role in the healthcare industry by providing innovative solutions to many health-related problems. Deep learning (DL) is a critical component in the development of AI-powered solutions; however, extensive knowledge is required to implement DL processes such as data preparation, model design, model training, and model evaluation. This has limited the widespread adoption of AI in the medical field, particularly in scenarios where non-technical professionals possess limited coding proficiency. Purpose This study evaluates the feasibility of using ChatGPT, a large language model developed by OpenAI, as an assistive tool for non-technical professionals to develop DL applications. The objective is to reduce the time and effort required in source code creation, while simplifying the overall process of application development. Methods ChatGPT was utilised to generate relevant source code for the creation of a DL application for echocardiogram view classification. Natural language input prompts were fed through the ChatGPT interface to produce modular scripts that fulfilled the purposes of video frame extraction, image pre-processing, dataset splitting, model training, and model evaluation. Input prompts were specified as a series of logical steps which explained the desired functionality, along with the necessary software libraries to be integrated. Results Experiments on code generation involved iteratively modifying input prompts through the rewording of sentences, reordering of steps, and usage of different libraries, to obtain working source code from ChatGPT responses. Results demonstrated that ChatGPT can generate fully usable programming scripts from natural language inputs, without the need for any modifications, to accomplish the various tasks involved in the DL process flow. Experiments were also conducted on locally sourced dobutamine stress echocardiogram videos, to verify script functionality and output reliability. The scripts successfully extracted, pre-processed, and split the video frames into training, validation, and testing sets. These datasets were then employed in the training and evaluation of a convolutional neural network which achieved an accuracy of above 80% when classifying seven different echocardiogram views. Conclusion ChatGPT has the potential to significantly reduce the time and effort required in developing DL applications. The successful implementation of the programming scripts suggest that ChatGPT can function as an assisting tool for non-technical professionals. Its ability to generate operational source code and streamline the development process could facilitate greater adoption of AI in healthcare and other industries. Future works shall explore the usage of ChatGPT in generating solutions to more heart-related use cases.Code generation and DL process flow