Deepfakes In Healthcare: Reviewing the Transformation Potential and its Challenges
Keywords:
Artificial intelligence, deepfakes, generative adversarial networks healthcare, medical imaging, variational autoencodersAbstract
Artificial Intelligence has become an integral aspect of the medical sector. The healthcare industry has benefited greatly from deep learning models that are based on machine learning, particularly from their propensity to handle massive volumes of data constantly. "Deepfake" is one of the most exciting Artificial Intelligence (AI) technological advancements in deep learning. The technique known as "deepfake" uses deep learning models to generate synthetic images, sound, or videos. Conventional medical procedures have limits concerning patient engagement, access, as well as training effectiveness. Deepfake technology has the potential to transform healthcare delivery, but it also poses ethical and practical concerns. The review article goes into the intricate narrative of artificial intelligence deepfakes in the healthcare sector, thoroughly reviewing and summarizing both the promising avenues they promise and the inherent problems involved with their application. It also addresses two potentially lucrative approaches to the generation of deepfakes i.e., variational autoencoders or VAEs and Generative Adversarial Networks or GANs. Collaboration among healthcare providers, technological professionals, as well as lawmakers is vital for crafting moral frameworks that guarantee responsible development and leverage the beneficial assets of deepfakes.
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