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Generative AI is driving new possibilities by addressing challenges around data availability, quality, and patient privacy. Models like GANs, VAEs, and Transformers-based architecture go beyond transforming datau2014they generate new insights, opening doors to advancements in areas such as medical imaging, drug discovery, and patient record synthesis.
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Gen AI models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Transformer-based architectures can generate new data by learning patterns from existing datasets, driving advancements in data synthesis and innovation
1 GENERATIVE ADVERSARIAL NETWORKS (GANs) ◦Consist of two neural networks, a generator and a discriminator, which engage in a zero-sum game ◦The generator tries to create data similar to the real data (e.g., synthetic medical images), while the discriminator attempts to distinguish between real and generated data ◦Technical Application: GANs have been used for synthetic image generation to create variations of MRI and CT scans, enabling more robust training of diagnostic models
2 VARIATIONAL AUTOENCODERS (VAEs) ◦Encode input data into a latent space representation, adding a constraint to ensure smooth interpolation between points in the latent space ◦Used to generate new data points that are slight variations of the original data (useful for anomaly detection) ◦Technical Application: VAEs can be used in drug discovery to create new chemical structures that resemble known compounds but have slightly altered features, potentially leading to new drug candidates
3 TRANSFORMERS (e.g., GPT FOR TEXT GENERATION) ◦Utilize self-attention mechanisms to process sequences of data, making them highly effective for tasks such as electronic health record (EHR) synthesis and patient journey modelling ◦Technical Application: Transformers can be employed to generate synthetic patient notes, enabling the training of natural language processing (NLP) models without the need for real patient data
