Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

By Polina Mamoshina, Lucy Ojomoko, Yury Yanovich, Alexei Ostrovski, A. Botezatu, P. Prikhodko, E. Izumchenko, Alexander M. Aliper, Konstantin Romantsov, Alexander Zhebrak and 2 more. OncoTarget, 2015

389 citations recorded by the source corpus.

The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.

Subfields: Artificial Intelligence, Health Informatics, Information Systems

View Paper: https://www.semanticscholar.org/paper/b0f2c8ce920a63dfff803be0abe583ff03ee083e

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Citations in the corpus

Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research (2019, background): “The more data we feed to the machine, the more the ability a machine will gain to classify or predict patterns accurately [163].”
Industry 4.0 in Finance: The Impact of Artificial Intelligence (AI) on Digital Financial Inclusion (2020, positive): “Many digital technologies which use AI technology utilize other credit score mechanisms to create collateral free-loan products (Matsebula and Yu 2017).”
Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis (2022, background): “Integration of AI and blockchain create predictive system contributing to clinical workflow (Mamoshina et al., 2018).”
A systematic literature review of blockchain-based applications: Current status, classification and open issues (2019, neutral): “…2016; Ahram et al., 2017; Al Omar et al., 2017; Capgemini, 2017; Emrify Inc., 2017; Freed Associates, 2017; Shae et al., 2017; Zhao et al., 2017; Mamoshina, 2018; Mytis-Gkometh et al., 2017; Borioli and Couturier, 2018; Lee and Yang, 2018; Xia et al., 2017a, 2017c; Yue et al., 2016; Patel,…”
Blockchain Technology in Healthcare: A Systematic Review (2019, background): “[51] 2017 Journal Health Data Analytics”