A Reassessment of Generative AI for Healthcare and Future Work Environments

Abstract

Compared to conventional AI models, which forecast outcomes based on available data, generative AI (GAI) signifies a substantial departure. Rather, GAI generates fresh data that closely resembles its training set, making it possible to produce synthetic data and lifelike content and assist with creative tasks. GAI has great potential in the analysis of unstructured data in the medical field, including diagnostic imaging, clinical notes, and medical records. Technology is said to generate data that could help develop better diagnosis and treatment with improved imaging, documentation, and customized medical insights for patients. To create diverse data that would respect patient confidentiality while it is being built, it may generate synthetic data to enhance the previously existing datasets. The goal is to overcome the challenges and ethical dilemmas arising from the deployment of GAI in medicine while unlocking the actual potential of this technology to have an effect on better care for the patient, as well as on the medical infrastructure. Good data on the specific aspects of Parkinson's disease are not easily forthcoming because this illness is often symptomatically synonymous with other types of motor dysfunction. Generative adversarial networks (GANs) can help to overcome the issues related to data availability. For the purposes of this study, the model conditional tabular generative adversarial network (CTGAN) is used to illustrate how synthetic data can be derived from real data. In the experimental part, the data points of attributes between the actual data and its corresponding synthetic data are used to compare the current data with its equivalent synthetic data collection. The analysis indicates that the absolute log means and standard deviations of each set of numerical data along with the heatmaps and principal components of Parkinson's disease and synthetic data are pretty similar. The chapter also includes an exhaustive literature review addressing the use of GAI for healthcare purposes.

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