Privacy in the Age of Medical Big Data

By W. Price, I. Cohen. Nature Medicine, 2019

1,248 citations recorded by the source corpus.

Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle data breaches. We close by sketching possible ways forward for the regulatory system. The increased amount of health care data collected brings with it ethical and legal challenges for protecting the patient while optimizing health care and research.

Subfields: Artificial Intelligence, Health Informatics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/9e3816be8cf4821d74e258de10ee471382936a30

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Citations in the corpus

Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “Policymakers and regulatory authorities play a crucial role in ensuring AI fairness through establishing comprehensive guidelines, standards, and regulations that govern the devel‑ opment and deployment of AI in healthcare [95, 113–115].”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “A last challenge to overcome is data availability, in part because stringent medical privacy laws often preclude data sharing (Price and Cohen, 2019).”
How to Design AI for Social Good: Seven Essential Factors (2020, background): “Tensions may arise between different thresholds of consent (Price and Cohen 2019).”
Preparing Medical Imaging Data for Machine Learning (2020, background): “Care must also be taken not to inadvertently mix data sets, because doing so increases the individual risk of reidentification through cross-linking of nonrelated data points (32).”
Deep learning in histopathology: the path to the clinic (2021, background): “Aside from the danger of data breaches, collecting large amounts of data may enable researchers to make connections that were previously not possible, potentially putting patient privacy at risk even if data are collected in an anonymized manner 114,139 .”
Artificial intelligence for digital and computational pathology (2023, background): “SSL is becoming the norm for pre-training feature extractors, with a growing body of work showing that SSL strategies can provide more disentangled, robust and generalizable representations than supervised learning methods 39–41 .”
Swarm Learning for decentralized and confidential clinical machine learning (2021, background): “…in oncology, success has been reported in machine-learning-based tumour detection 3,37 , sub-typing 38 , and outcome prediction 39 , but progress is hindered by the limited size of datasets 19 , with current privacy regulations 5,9,10 making it less appealing to develop centralized AI systems.”
Ethical and legal challenges of artificial intelligence-driven healthcare (2020, neutral): “But this may not adequately protect patients because of the possibility of data triangulation—to re-identify data thought to be de-identified under the statute through the combination of multiple datasets ([94], pp.”
Synthetic data generation for tabular health records: A systematic review (2022, background): “When they are contravened an “invasion of privacy” or “violation of privacy” occurs causing a disclosure of personal information that users intend to keep private from an entity which is not authorised to obtain the data [24, 25, 23].”
Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic (2020, background): “Research shows a high correlation between the use of big data that includes private identifiable information in the effectiveness of these epidemiological investigations [105].”