Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine?

The extension of blockchain use for nonfinancial domains has revealed opportunities to the health care sector that answer the need for efficient and effective data and information exchanges in a secure and transparent manner. Blockchain is relatively novel in health care and particularly for data analytics, although there are examples of improvements achieved. We provide a systematic review of blockchain uses within the health care industry, with a particular focus on the in vitro fertilization (IVF) field. Blockchain technology in the fertility sector, including data sharing collaborations compliant with ethical data handling within confines of international law, allows for large-scale prospective cohort studies to proceed at an international scale. Other opportunities include gamete donation and matching, consent sharing, and shared resources between different clinics. The extension of blockchain use for nonfinancial domains has revealed opportunities to the health care sector that answer the need for efficient and effective data and information exchanges in a secure and transparent manner. Blockchain is relatively novel in health care and particularly for data analytics, although there are examples of improvements achieved. We provide a systematic review of blockchain uses within the health care industry, with a particular focus on the in vitro fertilization (IVF) field. Blockchain technology in the fertility sector, including data sharing collaborations compliant with ethical data handling within confines of international law, allows for large-scale prospective cohort studies to proceed at an international scale. Other opportunities include gamete donation and matching, consent sharing, and shared resources between different clinics. Discuss: You can discuss this article with its authors and other readers at https://www.fertstertdialog.com/posts/31498 Discuss: You can discuss this article with its authors and other readers at https://www.fertstertdialog.com/posts/31498 For the last three years, prediction algorithms using artificial intelligence (AI) tools have gained particular prominence at assisted reproductive technology (ART) conferences (1Curchoe C. Farias A.F. Mendizaba G. Chavez-Badiola A. Evaluating predictive models in reproductive medicine.Fertil Steril. 2020; 114: 921-926Abstract Full Text Full Text PDF Scopus (10) Google Scholar, 2Chambost J, Jacques C, Pena CA, Kotrotsou M, Hickman C. Artificial intelligence (AI) is an emerging topic in the fertility field—it is time to discuss standards of reporting AI outcomes. In: Abstract Book of the ESHRE Virtual 36th Annual Meeting; 2020 July 5–8. Hum Reprod 2020;35(Suppl 1);i487–i488.Google Scholar) and in peer-reviewed publications (1Curchoe C. Farias A.F. Mendizaba G. Chavez-Badiola A. Evaluating predictive models in reproductive medicine.Fertil Steril. 2020; 114: 921-926Abstract Full Text Full Text PDF Scopus (10) Google Scholar). The objective is to improve the efficacy, efficiency, and consistency of clinical decisions made during an ART treatment, such as embryo classification and selection, sperm identification and classification, and clinical decisions during stimulation (3Kaufmann S.J. Eastaugh J.L. Snowden S. Smye S.W. Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization.Hum Reprod. 1997; 12: 1454-1457Crossref PubMed Scopus (57) Google Scholar, 4Wang R. Pan W. Jin L. Li Y. Geng Y. Gao C. et al.Artificial intelligence in reproductive medicine.Reproduction. 2019; 158: R139-R154Crossref Scopus (35) Google Scholar). However, tools based on AI require large amounts of data to learn the association between a range of factors (input data) and predicted outcome (output data) (2Chambost J, Jacques C, Pena CA, Kotrotsou M, Hickman C. Artificial intelligence (AI) is an emerging topic in the fertility field—it is time to discuss standards of reporting AI outcomes. In: Abstract Book of the ESHRE Virtual 36th Annual Meeting; 2020 July 5–8. Hum Reprod 2020;35(Suppl 1);i487–i488.Google Scholar). Up to now, this dependency on data volume, quality, and diversity has restricted the progress of AI tools toward becoming sufficiently robust for safe clinical implementation. In addition, interclinic variation in data point definitions, patient demographics, and differences in clinical and laboratory practices may cause data bias, resulting in AI tools that cannot be generalized because they are based on training in one original clinic (5Krittanawong C. Zhang H. Wang Z. Aydar M. Kitai T. Artificial intelligence in precision cardiovascular medicine.J Am Coll Cardiol. 2017; 69: 2657-2664Crossref PubMed Scopus (386) Google Scholar). With the limitations in mind, one way to overcome data insufficiency and data bias would be access to archives of large, global, and diverse data (6Perugini M. Hall J. Perugini D. Why commercial artificial intelligence products do not scale: Presagen, June 26, 2020.https://www.presagen.com/why-commercial-artificial-intelligence-products-do-not-scaleGoogle Scholar), representative of different patient demographics and clinical settings (7Rieke N. Hancox J. Li W. Milletari F. Roth H. Albarqouni S. et al.The future of digital health with federated learning.NPJ Digit Med. 2020; 3: 119Crossref Scopus (195) Google Scholar). With more than 2.7 million ART cycles per year worldwide (8Fauser B. Towards the global coverage of a unified registry of IVF outcomes.Reprod Biomed Online. 2019; 38: 133-137Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar), this goal should be theoretically easily achievable because sufficient varied data exist. The challenge lies with collecting, handling, amalgamating, and accessing the necessary data. First, health care data are highly sensitive, requiring strict regulation and adherence to standards with regard to preservation of the privacy and confidentiality of the patients, traceability and transparency of all interactions with the data, accountability of the data controller and the data processor, security of access to the data, and other ethics considerations (9Van Panhuis W.G. Paul P. Emerson C. Grefenstette J. Wilder R. Herbst A.J. et al.A systematic review of barriers to data sharing in public health.BMC Public Health. 2014; 14: 1144Crossref Scopus (192) Google Scholar). The consequence has been a convoluted process from the time the data are collected to the time research can start, which includes consenting of participants, data organization and processing, regulatory approval, ethics approval, and local clinic approval. This process usually takes months if not years to complete, and it not only extends the time and the cost of learning from and applying clinical data to improve clinical practice but also limits exploratory research on multicenter data to answer very specific study questions. Streamlining this process in a manner that preserves the standards of research would therefore help the field evolve faster, translating benefits directly to professionals and patients. Second, the data from the 2.7 million cycles performed each year worldwide vary in definition and standards of validation with regards to accuracy and completeness. Moreover, most in vitro fertilization (IVF) clinics are still collecting data either wholly or in part in paper form. This highlights the need to enhance the digitization of IVF clinics and incorporate a culture of understanding the value of data integrity and to ensure that the various solutions employed to collect data are integrated with each other. Paper and inaccurate or disjointed data greatly reduce the potential of AI analytics tools. Third, the IVF field lacks a system to amalgamate data effectively. At the global level, organizations such as the International Committee Monitoring Assisted Reproductive Technologies (ICMART) are only capable of collecting summary data, which is impossible to verify. At the national level, data sets such as that from the Human Fertilisation and Embryology Authority (HFEA) have obvious limitations. Indeed, they are set up for regulatory purposes not for research, so they lack the detail necessary to answer basic clinical practice questions. Not all countries have such mandatory registries, and an international version of the HFEA registry is not possible given the varying regulations among countries on how data move across international borders. As a result, it is very difficult for AI researchers to access sufficient good-quality data to use in training algorithms to make relevant predictions that could help clinicians make better decisions. In the process of handling sensitive health data, traceability and immutability should be prerequisites to track and monitor any interaction with that data. A blockchain is an immutable ledger containing a time-stamped series of records of data that is managed by a distributed, decentralized cluster of computers. It records all events occurring in the network in a “chain of blocks” that cannot be altered once recorded. By making this ledger accessible to all members of the network, it allows trustless transactions. For public blockchains, anyone can be part of the network, and the blockchain is maintained by the public community. This is the case, for example, for Bitcoin, Ethereum, and Dash. For private blockchains, entrants need a permission to participate in the network, and the ledger of transactions is only accessible to validated participants. Blockchain has been around since 2008 when the Bitcoin white paper was published, setting the groundwork for a distributed ledger technology that, at the time, focused on electronic financial transactions. Since then, new potential uses have been found for the technology. The second generation of blockchain allowed for smart contracts (10Swan M. Blockchain: blueprint for a new economy. O’Reilly Media, Sebastopol, CA2015Google Scholar). The third generation has extended to nonfinancial applications of blockchain (e.g., insurance, supply chain, dispute resolution, identity management, and health care). The novelty lies in the application of blockchain-based technologies in health care as a tool to address sensitive data-sharing challenges (11McMahan HB, Moore E, Ramage D, Hampson S. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS); 2017 20–22 April; Fort Lauderdale, FL; MLR: W&CP volume 54. Available at: http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf.Google Scholar). We provide a systematic review of blockchain uses within the human health care industry, and blockchain’s relevance to the IVF field. This review was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to search four different scientific databases (PubMed, IEEE Xplore, Web of Science, and Scopus) with the search string “blockchain” AND (“health∗” OR “medic∗” OR “biomedic∗” OR “clinic∗” OR “fertility∗” OR “IVF∗”). Relevant publications were selected and analyzed. The search was performed on January 26, 2020. The review includes peer-reviewed publications with a major focus related to the application of blockchain in health care, papers written in English. Only journals with a selective editorial policy and tracked by Clarivate, Scopus, or SciELO were considered. We excluded studies that were performed with nonhuman species. We also excluded studies published in languages other than English and reviews. After the duplicates were removed, two investigators independently assessed the titles and abstracts of all the articles. Studies that did not meet the inclusion criteria were excluded. After this first step, the text of the selected studies was evaluated fully. Disagreements regarding inclusion were resolved by consensus or with the involvement of a third author. The search across all databases did not reveal any blockchain proposed for the purpose of IVF/fertility. As a systematic literature review from academic search engines, this study did not include blockchain technologies advertised on the Internet or elsewhere, as this was considered outside the scope of this study. Additionally, the use of English terms in the search engine excludes publications in foreign languages. The review revealed 55 studies proposing various blockchain models with different use cases in health care (Fig. 1; Supplemental Table 1, available online) (12Yli-Huumo J. Ko D. Choi S. Park S. Smolander K. Where is current research on blockchain technology? A systematic review.PloS One. 2016; 11e0163477Crossref Scopus (947) Google Scholar, 13Radanović I. Likić R. Opportunities for use of blockchain technology in medicine.Appl Health Econ Health Policy. 2018; 16: 583-590Crossref PubMed Scopus (118) Google Scholar, 14Li H. Zhu L. Shen M. Gao F. Tao X. Liu S. 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Modelchain: decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks.arXiv. 2018; 1802.01746 (Available at:)https://arxiv.org/abs/1802.01746Google Scholar). Technologies that enable AI only represented a minority of the studies among those using blockchain in health care. Most blockchain publications (69%, 38 papers) were in the area of general health, followed by clinical trials/biomedical research. There were also examples in diabetes, oncology, chronic disease, insomnia, dentistry, genomics, and pain management. The search across all databases did not reveal any blockchain proposed for the purpose of IVF/fertility. The number of publications increased from four publications in 2016 to 26 publications in 2018, and have since decreased to 10 in 2019 (Supplemental Table 2, available online). Of these proposed blockchains, 84% did not involve federated learning and 16% did (Supplemental Table 2). Over quarter of these proposed blockchains were developed in the People’s Republic of China (30%), and 20% by the United States, and others were by collaborations across countries (Fig. 2). Over half the publications used were proposed for electronic medical records (56%) and remote patient monitoring (13%); 5% were designed for drug/pharmaceutic supply chain, 9% for biomedical research/education, and 11% for health data analytics (Table 1).Table 1Use cases of blockchain in health care and examples of proposed systems.Use caseNo. of proposed systemsPercentageExamples of proposed systemsElectronic medical records3156DPS (14Li H. Zhu L. Shen M. Gao F. Tao X. Liu S. Blockchain-based data preservation system for medical data.J Med Syst. 2018; 42: 141Crossref Scopus (147) Google Scholar), DAPPS (15Zhang P, Walker MA, White J, Schmidt DC, Lenz G. Metrics for assessing blockchain-based healthcare decentralized apps. In: Proceedings of the IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom); 2017 October 12–15; Dalian, People’s Republic of China. Available at: https://doi.org/10.1109/HealthCom.2017.8210842.Google Scholar), MedBlock (23Fan K. Wang S. Ren Y. Li H. Yang Y. MedBlock: efficient and secure medical data sharing via blockchain.J Med Syst. 2018; 42: 136Crossref Scopus (235) Google Scholar), MedRec (24Azaria A, Ekblaw A, Vieira T, Lippman A. MedRec: using blockchain for medical data access and permission management. In: Proceedings of the 2nd International Conference on Open and Big Data (OBD); 2016 August 22–24; Vienna, Austria. Available at: https://doi.org/10.1109/OBD.2016.11.Google Scholar), MeDShare (25Xia Q. Sifah E.B. Asamoah K.O. Gao J. Du X. Guizani M. MeDShare: trust-less medical data sharing among cloud service providers via blockchain.IEEE Access. 2017; 5: 14757-14767Crossref Scopus (553) Google Scholar), Ancile (42Dagher G.G. Mohler J. Milojkovic M. Marella P.B. Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology.Sustain Cities and Soc. 2018; 39: 283-297Crossref Scopus (351) Google Scholar)Pharmaceutical supply chain35Modum.io (17Bocek T, Rodrigues BB, Strasser T, Stiller B. Blockchains everywhere—a use-case of blockchains in the pharma supply-chain. In: Proceedings of the IFIP/IEEE Symposium on Integrated Network and Service Management (IM); 2017 8–12 May; Lisbon, Portugal. Available at: https://doi.org/10.23919/INM.2017.7987376.Google Scholar), Gcoin (21Tseng J.H. Liao Y.C. Chong B. Liao S.W. Governance on the drug supply chain via Gcoin blockchain.Int J Environ Res Public Health. 2018; 15: 1055Crossref Scopus (132) Google Scholar, 63Bernardi F. Lima V. Pellison F. de Azevedo Marques P.M. Rijo R. Galliez R.M. et al.Blockchain based network for tuberculosis: a data sharing initiative in Brazil.Stud Health Technol Inform. 2019; 262: 264-267Google Scholar)Health data analytics61119Mamoshina P. Ojomoko L. Yanovic