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Artificial Intelligence and Blockchain

Blockchain and Artificial Intelligence


Since its establishment in 2010, MDEpiNet has worked to build a global real-world evidence (RWE) collaborative for health technologies.  Access to vast streams of patients’ healthcare data from real-world and clinical trial ecosystems amalgamated with information from reference databases have transformed research and development practice and accelerated scientific discoveries. Computerized automation driven by rigorous algorithms resulted in metamorphosis of manufacturing processes resulting in cheaper, safer and higher quality medical products. Connected wireless medical devices allowed us to constantly monitor patients through streaming vital signals and behaviors to intelligent algorithms.

Biomedical information has enormous discovery value for preclinical, clinical and post-market research. However, our capacity to generate such large volumes of data in so many different varieties from different sources has overwhelmed our ability to analyze and interpret by using merely human intelligence. For MDEpiNet to stay on its mission, it needs to evolve and integrate the two pillars of modern technological innovations into its workflows: Blockchain and Artificial Intelligence. 


Blockchain with its immutable and distributed data warehousing nature has a large potential to serve as an unadulterated provenance layer by securing consented privacy of the patients, providing data lifecycle supply chains and as such can enable compliance framework for global scale data aggregation and analysis.


Artificial Intelligence, evolutionary algorithms and deep machine learning algorithms have unprecedented transfiguration capacity to alter conventional reductionist thinking processes of a human brain. Self-architecting and self-parametrizing algorithms, massively parallelized optimization procedures of supervised or non-supervised classification methodologies have proven much more efficient in a variety of science and technology fields.

BAIT Task Force: Responsibilities and Privileges of the Taskforce

  • propose a mission charter, operating procedures and a structure of the task-force;

  • engage major stakeholders within industry, academia and regulatory bodies;

  • define areas of operations, inventorize technological solutions, propose trajectories of improvement;

  • organize a public meeting on the subject of “AI and Blockchain strategic vision plan” by inviting major stakeholders into the conference;

  • advise MDEpiNet on prioritization associated with adaptation of new technologies into MDEpiNet and CRN framework as relevant;


Initial scope would focus on two dimensions: functional and stakeholder.

Functional Dimension

Considering the overall context of MDEpiNet and CRNs we suggest the following functional areas:

  • data collection and QC: potential role of AI to facilitate high quality data collection efforts, how can AI perform data quality assessments, how Blockchain data supply framework can provide provenance and consent infrastructure for traceable and auditable data.

  • data harmonization and standardization:  what standardization/harmonization architecture should/could be adapted, enforced to facilitate uniformity of the data. How AI can assist in aggregation of data from different segmented sources through assistive dictation, NLP, run-time error correction, etc. 

  • data aggregation and storage: the role of blockchain to serve as an immutable data-lake infrastructure for long term trusted data repositories. Long term archival and referencability of the data across different execution platforms.

  • data analytics: AI and learning algorithmics, classical and non-classical methodologies and analytical approaches, the role of AI in structured and unstructured data analytics.

  • permissioning, consenting frameworks: blockchain, smart contracts and new alternatives of electronic consenting, modern approach to patient centric technology implementations, permissioning networks.

Stakeholder Dimension

In order to absorb encompassing perspectives and strike a balance between different stakeholders, together with MDEpiNet Leadership we decided to propose an initial list for consideration:

  • leaders and technical executors: leaders can provide bigger vision, perhaps better resource and funding support, networking and long-term perspectives. Technical experts can present a value in actual implementation of projects and contribute to executability of the proposed projects and choice of technological solutions.

  • industry, academia, government: each covering critically important aspect of the medical product lifecycle.

  • international perspective: representation from the global community is important given differing regulatory framework and cultural aspects.

  • technology expertise: AI and deep learning expertise is critical; blockchain expertise must be expressed. However, the task-force must also consider and involve classical school of algorithms and general data scientists’ overarching perspectives as well.


  • First week of May 2020 - Kick off conference call

  • August 2020 - Interim report 

  • October 2020 – Final report


Gil Alterovitz, PhD

Vahan Simonyan, MS, PhD

Yelena Yesha, PhD


Charles Kaminski

Eliot Siegel, MD

Elisabeth George

Fadia T. Shaya, PhD, MPH

Jeb Linton

Joseph Drozda, MD, FACC

Maria Palombini, MBA

Matthew Diamond, MD

Michael Mylrea, PhD

Michaela Iorga, PhD

Morgan Crafts, MBA

Patrick Lilley, PhD

Pierre D’Haese, PhD

Rick Kuntz, MD, MSc

Scott Duvall, PhD

Stephen Dennis

Vasant Honavar, PhD

Wendy Henry

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