AIMC Topic: Confidentiality

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[Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data].

Sheng li xue bao : [Acta physiologica Sinica]
As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medi...

Securing a Generative AI-Powered Healthcare Chatbot.

Studies in health technology and informatics
In Generative Artificial Intelligence (AI), Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama, significantly impact healthcare by aiding in patient care, medical research, and administrative tasks. AI-powered chatbots offer real-time...

Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline th...

Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.

Studies in health technology and informatics
INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exceptio...

How Data Infrastructure Deals with Bias Problems in Medical Imaging.

Studies in health technology and informatics
The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data...

Quality Assessment of Brain MRI Defacing Using Machine Learning.

Studies in health technology and informatics
Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially com...

Secure Extraction of Personal Information from EHR by Federated Machine Learning.

Studies in health technology and informatics
Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges wit...

Methods and Algorithms of Ensuring Data Privacy in AI-Based Healthcare Systems and Technologies.

Studies in health technology and informatics
This project seeks to devise novel algorithms and techniques leveraged in healthcare to guarantee data privacy in AI-powered systems. To bolster its credibility, the study review presents various modern approaches and technologies used to preserve da...

[ARTIFICIAL INTELLIGENCE AND MEDICAL ETHICS].

Harefuah
Artificial intelligence has burst into our lives with great vigor in recent years. We encounter it in all areas of life, as well as in the field of medicine. The article refers to medical ethics in two areas: One field is medicine based on Mega Data ...

Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias.

North Carolina medical journal
Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, an...