Abstract
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.
Highlights
The digital revolution in medicine produced a paradigm shift in the healthcare industry
One of the major benefits of the digital healthcare system and electronic medical records is the improved access to the healthcare records both for health professionals and patients
While several attempts were made to evaluate the clinical benefit of the different methods [24] and multiple data types were used for evaluating the health status of the individual patients [25] including the widely popularized “Snyderome” project [26], none of these approaches are truly integrative on the population scale and compare the predictive nature and value of the various data types in the context of biomedicine
Summary
The digital revolution in medicine produced a paradigm shift in the healthcare industry. For the first time we introduce half-life period of analysis significance, models of data value for single and group of users and cost of buying data in the context of biomedical applications. ● Middleware provides ordering and atomicity of transactions, interoperability among services and clients, replication of services among nodes in the network (which is purposed for both service fault-tolerance and auditability via auditing nodes), management of service lifecycle (e.g., service deployment), data persistence, access control, assistance with generating responses to read requests, etc. Several research groups already studied the application of voice and speech pattern recognition for diagnosis of Parkinson’s disease and its severity prediction www.impactjournals.com/oncotarget [76, 77] Traditional diagnostics pipeline based on analysis combination of medical tests, especially when healthcare specialists try to diagnose serious and complex pathologies such as oncological, autoimmune, or neurodegenerative diseases. The problem could be solved with help of zero and one shot learning
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