Protecting Data Privacy in the Age of AI-Enabled Ophthalmology

Tom, Elysse S; Keane, Pearse Andrew; Blazes, Marian; Pasquale, Louis R.; Chiang, Michael F.; Lee, Aaron; Lee, Cecilia S.

doi:10.1167/tvst.9.2.36

Cited by 45 publications

(30 citation statements)

References 35 publications

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“…This novel technology also faces other challenging issues regarding privacy protection. Eventual relationships between healthcare companies and academic research data may elevate the risk of malicious privacy violations [175].…”

Section: Solutions Limitations and Future Directionsmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

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Section: Solutions Limitations and Future Directionsmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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“…Data privacy and access is a critical issue and one in which policy and process must be developed to ensure the safe acquisition and transfer of data in the healthcare sector. The exchange of patient data among healthcare institutions, academic research, and industry can declassify sensitive information and allow malicious data breaches [ 17 ]. Efforts must be taken to limit the risk of such data breaches.…”

Section: Discussionmentioning

confidence: 99%

A Qualitative Thematic Analysis of Addressing the Why: An Artificial Intelligence (AI) in Healthcare Symposium

Borgstadt¹,

Kalpas²,

Pond³

2022

Cureus

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Healthcare managers and clinicians are inefficient in the processes of workflows and documentation. The inefficiency is due in part by increasing demands of insurance companies, regulatory demands from the government, and human error. Artificial intelligence (AI) can improve healthcare processes by decreasing variability, thus improving patient and physician experience and patient outcomes. This project brings together a panel of five experts to discuss problems in medicine and some of the tools available through AI and technology to address these problems. The symposium modeled a "flipped classroom" format. The first five 20-minute modules were uploaded to a web-based platform for viewing in advance of the 60-minute moderated roundtable (Zoom, Zoom Video Communications, San Jose, CA, USA). The following themes emerged after reviewing the transcribed data: data privacy and access (N=3, number of times identified); process improvement (N=2); physician experience (N=1); value in data (N=2); and bias in healthcare and AI (N=3). For AI to become implemented on a large scale in healthcare, many areas will need continued discussion and research, including a continued look into how AI can add value to workflow and knowledge augmentation. In addition, standards for the implementation of AI and a methodical approach to the analysis of the effectiveness of algorithms coupled with training of healthcare professionals in the language of AI algorithms will be helpful to ensure that AI is integrated safely.

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“…Data privacy protection in the age of data science and artificial intelligence has drawn a lot of attention, especially in the medical field [19]. One major way of attacking privacy is membership inference attack [20], which aims to determine whether data from a target patient is included in the study.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs

Sun,

Plawinski,

Subramaniam

et al. 2021

Preprint

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Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX ® (secukinumab) Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.

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Protecting Data Privacy in the Age of AI-Enabled Ophthalmology

Abstract: This work is licensed under a Creative Commons Attribution 4.0 International License.

Cited by 45 publications

References 35 publications

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

A Qualitative Thematic Analysis of Addressing the Why: An Artificial Intelligence (AI) in Healthcare Symposium

A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs

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