AIMC Topic: Confidentiality
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De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.
BMC medical informatics and decision making
Feb 17, 2025
BACKGROUND: De-identification of clinical notes is essential to utilize the rich information in unstructured text data in medical research. However, only limited work has been done in removing personal information from clinical notes in Korea.
Vertical federated learning based on data subset representation for healthcare application.
Computer methods and programs in biomedicine
Feb 12, 2025
BACKGROUND AND OBJECTIVE: Artificial intelligence is increasingly essential for disease classification and clinical diagnosis tasks in healthcare. Given the strict privacy needs of healthcare data, Vertical Federated Learning (VFL) has been introduce...
The good, the bad, and the ugly: Ethical considerations regarding artificial intelligence assistance in administrative physician tasks.
Clinics in dermatology
Feb 11, 2025
Artificial intelligence is a powerful tool that can potentially transform the diagnostic, therapeutic, and administrative practice of dermatology. Physicians are expected to complete electronic health record documentation in a timely fashion, prepare...
Is more data always better? On alternative policies to mitigate bias in Artificial Intelligence health systems.
Bioethics
Feb 9, 2025
The development and implementation of Artificial Intelligence (AI) health systems represent a great power that comes with great responsibility. Their capacity to improve and transform healthcare involves inevitable risks. A major risk in this regard ...
Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data.
Sensors (Basel, Switzerland)
Jan 25, 2025
The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare mana...
Towards practical and privacy-preserving CNN inference service for cloud-based medical imaging analysis: A homomorphic encryption-based approach.
Computer methods and programs in biomedicine
Jan 21, 2025
BACKGROUND AND OBJECTIVE: Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive us...
High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.
BMC medical ethics
Jan 15, 2025
BACKGROUND: Considering the disruptive potential of AI technology, its current and future impact in healthcare, as well as healthcare professionals' lack of training in how to use it, the paper summarizes how to approach the challenges of AI from an ...
Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain.
Scientific reports
Jan 7, 2025
The motivation for this article stems from the fact that medical image security is crucial for maintaining patient confidentiality and protecting against unauthorized access or manipulation. This paper presents a novel encryption technique that integ...
Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges.
International journal of medical informatics
Dec 30, 2024
INTRODUCTION: Artificial Intelligence is in the phase of health care, with transformative innovations in diagnostics, personalized treatment, and operational efficiency. While having potential, critical challenges are apparent in areas of safety, tru...
Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.
Computer methods and programs in biomedicine
Dec 28, 2024
BACKGROUND: Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promisin...
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