Generative adversarial networks and synthetic patient data: current challenges and future perspectives

Arora, Anmol; Arora, Ananya

doi:10.7861/fhj.2022-0013

Cited by 81 publications

(23 citation statements)

References 17 publications

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“…However, generating clinical images alone is insufficient; labelling the data enhances its utility. 36 Since in our application the number of possible polyp types is fixed and known, we opted to use the naïve approach of unconditional image generation utilizing an ensemble of DDPMs so as to fix the ground truth labels for images generated by each DDPM.…”

Section: Generative Model For Data Augmentationmentioning

confidence: 99%

Enhancing colorectal cancer diagnosis through generative models and vision-based tactile sensing: a Sim2Real study

Kapuria,

Ikoma,

Chinchali

et al. 2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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Towards the goal of developing an informed, intuitive, and generalized artificial intelligence model for the earlystage diagnosis of colorectal cancer (CRC), in this work, we present a generative model-based technique to improve the training and generalization performance of machine learning classification algorithms. Through this approach, we address the challenge of acquiring sizable and well-balanced datasets within the clinical domain. Our methodology involves training generative models on already available medical data, learning the latent representations, and finally generating new synthetic samples to be used for downstream tasks. We train dedicated UNet2D-based Denoising Diffusion Probabilistic Models (DDPMs) using our custom dataset, which consists of textural images captured by our novel Vision-based Tactile Sensor (VS-TS), called Hysense. These UNet2D DDPMs are employed to generate synthetic images for each potential class. To thoroughly study the effectiveness of using synthetic images during training, we compared the performance of multiple classification models, ranging from simple to state-of-the-art approaches, with our evaluation focusing solely on real images. Specifically for our dataset, we also extend the use of dedicated UNet2D DDPMs to generate synthetic images of not just possible classes, but also other features that may be present in the image, such as whole or partial contact of sensor with polyp phantoms. Through our experimental analyses, we demonstrated that the utilization of generative models to enrich existing datasets with synthetic images leads to improved classification performance and a reduction in model biases.

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Section: Generative Model For Data Augmentationmentioning

confidence: 99%

Enhancing colorectal cancer diagnosis through generative models and vision-based tactile sensing: a Sim2Real study

Kapuria,

Ikoma,

Chinchali

et al. 2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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“…Synthetic data have driven forward innovations within the healthcare space. Synthetic data undergird many current initiatives in medical education [125,126], clinical training [127,128], epidemiology research [129,130] and disease prevention [131,132]. Cancer researchers now use synthetic data resources to bolster their work including precision medicine [133] and palliative care [134].…”

Section: Synthetic Datamentioning

confidence: 99%

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

McDonnell

2023

JCM

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Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

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“…In recent years, the field of healthcare has witnessed remarkable advancements in artificial intelligence and natural language processing (NLP), leading to the emergence of innovative tools that are reshaping modern medicine. Chat Generative Pretrained Transformer (ChatGPT, available at https://chat.openai.com/) is a generative language model-based chatbot developed by Open AI in San Francisco (San Francisco, California, USA), which uses a deep learning language model, trained on an enormous amount of data set [1]. The latest version of the chatbot is called ChatGPT-PLUS or ChatGPT-4 and has an improved ability to understand and generate natural text, particularly in complex and nuanced scenarios [2].…”

Section: Introductionmentioning

confidence: 99%

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

Nedbal,

Naik,

Castellani

et al. 2023

Current Opinion in Urology

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Purpose of review ChatGPT has emerged as a potentially useful tool for healthcare. Its role in urology is in its infancy and has much potential for research, clinical practice and for patient assistance. With this narrative review, we want to draw a picture of what is known about ChatGPT's integration in urology, alongside future promises and challenges. Recent findings The use of ChatGPT can ease the administrative work, helping urologists with note-taking and clinical documentation such as discharge summaries and clinical notes. It can improve patient engagement through increasing awareness and facilitating communication, as it has especially been investigated for uro-oncological diseases. Its ability to understand human emotions makes ChatGPT an empathic and thoughtful interactive tool or source for urological patients and their relatives. Currently, its role in clinical diagnosis and treatment decisions is uncertain, as concerns have been raised about misinterpretation, hallucination and out-of-date information. Moreover, a mandatory regulatory process for ChatGPT in urology is yet to be established. Summary ChatGPT has the potential to contribute to precision medicine and tailored practice by its quick, structured responses. However, this will depend on how well information can be obtained by seeking appropriate responses and asking the pertinent questions. The key lies in being able to validate the responses, regulating the information shared and avoiding misuse of the same to protect the data and patient privacy. Its successful integration into mainstream urology needs educational bodies to provide guidelines or best practice recommendations for the same.

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Generative adversarial networks and synthetic patient data: current challenges and future perspectives

Cited by 81 publications

References 17 publications

Enhancing colorectal cancer diagnosis through generative models and vision-based tactile sensing: a Sim2Real study

Enhancing colorectal cancer diagnosis through generative models and vision-based tactile sensing: a Sim2Real study

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

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