Position Paper: Building Trust in Synthetic Data for Clinical AI

• URL: http://arxiv.org/abs/2502.02076v1
• Date: Tue, 04 Feb 2025 07:53:23 GMT
• Title: Position Paper: Building Trust in Synthetic Data for Clinical AI
• Authors: Krishan Agyakari Raja Babu, Supriti Mulay, Om Prabhu, Mohanasankar Sivaprakasam,
• Abstract summary: This paper argues that fostering trust in synthetic medical data is crucial for its clinical adoption. We present empirical evidence from brain tumor segmentation to demonstrate that the quality, diversity, and proportion of synthetic data directly impact trust in clinical AI models.
• Score: 0.3937354192623676
• License:
• Abstract: Deep generative models and synthetic medical data have shown significant promise in addressing key challenges in healthcare, such as privacy concerns, data bias, and the scarcity of realistic datasets. While research in this area has grown rapidly and demonstrated substantial theoretical potential, its practical adoption in clinical settings remains limited. Despite the benefits synthetic data offers, questions surrounding its reliability and credibility persist, leading to a lack of trust among clinicians. This position paper argues that fostering trust in synthetic medical data is crucial for its clinical adoption. It aims to spark a discussion on the viability of synthetic medical data in clinical practice, particularly in the context of current advancements in AI. We present empirical evidence from brain tumor segmentation to demonstrate that the quality, diversity, and proportion of synthetic data directly impact trust in clinical AI models. Our findings provide insights to improve the deployment and acceptance of synthetic data-driven AI systems in real-world clinical workflows.

Related papers

• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
  We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models. Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
  arXiv  Detail & Related papers  (2024-10-23T00:31:17Z)
• Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation [1.7769033811751995]
  We present Masked Clinical Modelling (MCM), a framework inspired by masked language modelling. MCM is designed for both data synthesis and conditional data augmentation. We evaluate this prototype on the WHAS500 dataset using Cox Proportional Hazards models.
  arXiv  Detail & Related papers  (2024-10-22T08:38:46Z)
• Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation [8.567656208979475]
  We introduce a novel Retrieval-Reasoning framework that leverages large language models to generate synthetic clinical trials. Experiments conducted on real clinical trials from the urlClinicalTrials.gov database demonstrate that our synthetic data can effectively augment real datasets. Our findings suggest that LLMs for synthetic clinical trial generation hold promise for accelerating clinical research and upholding ethical standards for patient privacy.
  arXiv  Detail & Related papers  (2024-10-16T11:46:32Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Synthetic Data in Radiological Imaging: Current State and Future Outlook [3.047958668050099]
  Key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances.
  arXiv  Detail & Related papers  (2024-05-08T18:35:47Z)
• Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection [13.445454471355214]
  We propose a statistical approach for synthetic data generation applicable in classification problems. We assess the utility and privacy implications of synthetic data generated by Kernel Density Estimator and K-Nearest Neighbors sampling (KDE-KNN) within a real-world context.
  arXiv  Detail & Related papers  (2024-04-25T14:26:53Z)
• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• Fidelity and Privacy of Synthetic Medical Data [0.0]
  The digitization of medical records ushered in a new era of big data to clinical science. The need to share individual-level medical data continues to grow, and has never been more urgent. enthusiasm for the use of big data has been tempered by a fully appropriate concern for patient autonomy and privacy.
  arXiv  Detail & Related papers  (2021-01-18T23:01:27Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.