Applications of generative adversarial networks (GANs) in healthcare
K. Deeba, D. Vathana, D. Vanusha, J. Ramaprabha · 2024
Generative adversarial networks (GANs) are one of the powerful tools for various problems in healthcare, which can find the applications for the treatment or prognosis. At the core of GANs is a novel architecture comprising two components: the feeder is the suspensor, which generates artificial data, and the adversary is the Discriminator, which evaluates this data after comparison to real-world examples. By means of GANs’ adversarial learning systems, the networks are ready to produce data that with time becomes more and more similar to data from real sources. This way, these systems can be applied in many different healthcare areas. Data generation based on artificial intelligence (AI) provides solutions to crucial problems of data administration such as a lack of datasets for rare diseases, the issue of sharing one&s;s private data, and the necessity of high-quality, different datasets for AI training. GANs assist in the generation of the clinical images, for instance, MRI, CT scans, and X-rays, which ensure image enhancement in terms of resolution and quality. Also, using GANs to perform data augmentation is an effective way to provide a sufficient amount of data that can be used for training the models. That capability is of the highest significance in medical imaging, and where you get a big impact on diagnostic accuracy it&s;s due to the detailed and varied images availability. In addition, it contributes to diagnostic and prognostic approaches to medical problems by providing synthetic phenomena to help in training diagnostic models, predicting disease progression, and assessing the robustness of these models directly. This determination is particularly important in the creation of robust tools for diagnosis that display sufficient flexibility to suit different clinical backgrounds and patient groups. For all the positives, the application of GANs in healthcare encounters obstacles, namely, uneven data in generative models, meaning domain expertise is highly sought after, and ethical issues around patient data use and control. It is important that the pitfalls are either dealt with or sidestepped to realize the maximum potential of GANs in medicine. GANs look forward to the future of personalized medicine and precision healthcare in which they could become valuable tools to identify and implement tailored interventions and treatments for the unique profiles of each patient. The effect of genomics on the diagnostic pathway, treatment decision-making, and the entire patient care process could be tremendous, helping in more accurate diagnosis, more precise treatment, and thus better patient health. Along the research line, AI, particularly GANs, is set to drive the direction into numerous healthcare adoptions where medical diagnoses, treatment planning, and the management of patients will be reshaped in the future.