Literature DB >> 30617331

Privacy in the age of medical big data.

W Nicholson Price1,2,3, I Glenn Cohen4,5,6.   

Abstract

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.

Entities:  

Mesh:

Year:  2019        PMID: 30617331      PMCID: PMC6376961          DOI: 10.1038/s41591-018-0272-7

Source DB:  PubMed          Journal:  Nat Med        ISSN: 1078-8956            Impact factor:   87.241


  33 in total

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Journal:  Am J Bioeth       Date:  2005       Impact factor: 11.229

2.  Developing the Sentinel System--a national resource for evidence development.

Authors:  Rachel E Behrman; Joshua S Benner; Jeffrey S Brown; Mark McClellan; Janet Woodcock; Richard Platt
Journal:  N Engl J Med       Date:  2011-01-12       Impact factor: 91.245

3.  Sociogenetic Risks - Ancestry DNA Testing, Third-Party Identity, and Protection of Privacy.

Authors:  Thomas May
Journal:  N Engl J Med       Date:  2018-06-20       Impact factor: 91.245

4.  The research-treatment distinction: a problematic approach for determining which activities should have ethical oversight.

Authors:  Nancy E Kass; Ruth R Faden; Steven N Goodman; Peter Pronovost; Sean Tunis; Tom L Beauchamp
Journal:  Hastings Cent Rep       Date:  2013 Jan-Feb       Impact factor: 2.683

5.  Distinguishing QI projects from human subjects research: ethical and practical considerations.

Authors:  Mehul V Raval; Joseph V Sakran; Rachel Laura Medbery; Peter Angelos; Bruce L Hall
Journal:  Bull Am Coll Surg       Date:  2014-07

6.  Reg-ent within the Learning Health System.

Authors:  Kayte Spector-Bagdady; Andrew G Shuman
Journal:  Otolaryngol Head Neck Surg       Date:  2018-03       Impact factor: 3.497

7.  Analysis of state laws on informed consent for clinical genetic testing in the era of genomic sequencing.

Authors:  Kayte Spector-Bagdady; Anya E R Prince; Joon-Ho Yu; Paul S Appelbaum
Journal:  Am J Med Genet C Semin Med Genet       Date:  2018-03-22       Impact factor: 3.908

8.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

9.  Promoting healthcare innovation on the demand side.

Authors:  Rebecca S Eisenberg; W Nicholson Price
Journal:  J Law Biosci       Date:  2017-01-16
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  95 in total

Review 1.  Deep learning in histopathology: the path to the clinic.

Authors:  Jeroen van der Laak; Geert Litjens; Francesco Ciompi
Journal:  Nat Med       Date:  2021-05-14       Impact factor: 53.440

2.  Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future.

Authors:  Eyal Lotan; Charlotte Tschider; Daniel K Sodickson; Arthur L Caplan; Mary Bruno; Ben Zhang; Yvonne W Lui
Journal:  J Am Coll Radiol       Date:  2020-04-28       Impact factor: 5.532

Review 3.  Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Authors:  Giovanni Cammarota; Gianluca Ianiro; Anna Ahern; Carmine Carbone; Andriy Temko; Marcus J Claesson; Antonio Gasbarrini; Giampaolo Tortora
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2020-07-09       Impact factor: 46.802

4.  Poor quality data, privacy, lack of certifications: the lethal triad of new technologies in intensive care.

Authors:  Valentina Bellini; Jonathan Montomoli; Elena Bignami
Journal:  Intensive Care Med       Date:  2021-07-15       Impact factor: 17.440

5.  Identifying Ethical Considerations for Machine Learning Healthcare Applications.

Authors:  Danton S Char; Michael D Abràmoff; Chris Feudtner
Journal:  Am J Bioeth       Date:  2020-11       Impact factor: 11.229

6.  Fold-stratified cross-validation for unbiased and privacy-preserving federated learning.

Authors:  Romain Bey; Romain Goussault; François Grolleau; Mehdi Benchoufi; Raphaël Porcher
Journal:  J Am Med Inform Assoc       Date:  2020-08-01       Impact factor: 4.497

Review 7.  Realistically Integrating Machine Learning Into Clinical Practice: A Road Map of Opportunities, Challenges, and a Potential Future.

Authors:  Ira S Hofer; Michael Burns; Samir Kendale; Jonathan P Wanderer
Journal:  Anesth Analg       Date:  2020-05       Impact factor: 5.108

8.  Privacy Gaps for Digital Cardiology Data: Big Problems With Big Data.

Authors:  Jessica R Golbus; W Nicholson Price; Brahmajee K Nallamothu
Journal:  Circulation       Date:  2020-02-24       Impact factor: 29.690

Review 9.  Big Data in Nephrology.

Authors:  Navchetan Kaur; Sanchita Bhattacharya; Atul J Butte
Journal:  Nat Rev Nephrol       Date:  2021-06-30       Impact factor: 28.314

Review 10.  Axes of a revolution: challenges and promises of big data in healthcare.

Authors:  Smadar Shilo; Hagai Rossman; Eran Segal
Journal:  Nat Med       Date:  2020-01-13       Impact factor: 53.440

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