VerifyMed -- A blockchain platform for transparent trust in virtualized healthcare: Proof-of-concept
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Abstract (arXiv)
Patients living in a digitized world can now interact with medical professionals through online services such as chat applications, video conferencing or indirectly through consulting services. These applications need to tackle several fundamental trust issues: 1. Checking and confirming that the person they are interacting with is a real person; 2. Validating that the healthcare professional has competence within the field in question; and 3. Confirming that the healthcare professional has a valid license to practice. In this paper, we present VerifyMed -- the first proof-of-concept platform, built on Ethereum, for transparently validating the authorization and competence of medical professionals using blockchain technology. Our platform models trust relationships within the healthcare industry to validate professional clinical authorization. Furthermore, it enables a healthcare professional to build a portfolio of real-life work experience and further validates the competence by storing outcome metrics reported by the patients. The extensive realistic simulations show that with our platform, an average cost for creating a smart contract for a treatment and getting it approved is around 1 USD, and the cost for evaluating a treatment is around 50 cents.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 May 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 4; 2024: 2; 2025: 4; 2026: 1.
14 papers.
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- 2023Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testingcited by 23
- 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
- 2023Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinkingcited by 14
- 2021Distributed Deep Learning Using Volunteer Computing-Like Paradigmcited by 8
- 2024MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Searchcited by 4
- 2025FairSense: Long-Term Fairness Analysis of ML-Enabled Systemscited by 3
- 2022Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instancescited by 2
- 2025Fairness Testing through Extreme Value Theorycited by 2
- 2024Predicting Fairness of ML Software Configurationscited by 1
- 2025Causally Perturbed Fairness Testingcited by 1
- 2021A Survey on Proactive Customer Care: Enabling Science and Steps to Realize itcited by 0
- 2023Myths and Legends in High-Performance Computingcited by 0
- 2026Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs26 Aug 2026
- 2025Causally Perturbed Fairness Testing21 Oct 2025
- 2025Testing Individual Fairness in Graph Neural Networks25 Apr 2025
- 2025Fairness Testing through Extreme Value Theory20 Jan 2025
- 2025FairSense: Long-Term Fairness Analysis of ML-Enabled Systems3 Jan 2025
- 2024MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search28 Dec 2024
- 2024Predicting Fairness of ML Software Configurations29 Apr 2024
- 2023A Critical Survey on Fairness Benefits of Explainable AI15 Oct 2023
- 2023Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing19 May 2023
- 2023Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking16 Feb 2023
- 2023Myths and Legends in High-Performance Computing6 Jan 2023
- 2022Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances17 Sep 2022
2 more
- 2023Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testingcited by 23
- 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
- 2023Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinkingcited by 14
- 2021Distributed Deep Learning Using Volunteer Computing-Like Paradigmcited by 8
- 2024MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Searchcited by 4
- 2025FairSense: Long-Term Fairness Analysis of ML-Enabled Systemscited by 3
- 2022Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instancescited by 2
- 2025Fairness Testing through Extreme Value Theorycited by 2
- 2024Predicting Fairness of ML Software Configurationscited by 1
- 2025Causally Perturbed Fairness Testingcited by 1
- 2021A Survey on Proactive Customer Care: Enabling Science and Steps to Realize itcited by 0
- 2023Myths and Legends in High-Performance Computingcited by 0
12 most cited of 14 All 14 (JSON)
- 2026Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs26 Aug 2026
- 2025Causally Perturbed Fairness Testing21 Oct 2025
- 2025Testing Individual Fairness in Graph Neural Networks25 Apr 2025
- 2025Fairness Testing through Extreme Value Theory20 Jan 2025
- 2025FairSense: Long-Term Fairness Analysis of ML-Enabled Systems3 Jan 2025
- 2024MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search28 Dec 2024
- 2024Predicting Fairness of ML Software Configurations29 Apr 2024
- 2023A Critical Survey on Fairness Benefits of Explainable AI15 Oct 2023
- 2023Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing19 May 2023
- 2023Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking16 Feb 2023
- 2023Myths and Legends in High-Performance Computing6 Jan 2023
- 2022Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances17 Sep 2022
12 newest of 14 All 14 (JSON)