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Cited by 286Blockchain Technology Applications and SecurityCOVID-19 Digital Contact TracingHealthcare during COVID-19 Pandemic

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Open access
Nguyen, Dinh C., Ding, Ming, Pathirana, Pubudu N., Seneviratne, Aruna·2021·IEEE Access
DOI 10.1109/access.2021.3093633PMC PMC8545197

Published version of a preprint

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Abstract

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

AI snapshot

Written from the paper’s full text.

TL;DR

This survey reviews blockchain and AI solutions for COVID-19, proposes an integrated four-layer architecture, and surveys applications, use cases, projects, and challenges. It includes a federated-learning case study using GANs for COVID-19 X-ray classification, reporting high detection metrics.

Key findings

  • The paper proposes a conceptual architecture integrating blockchain and AI across coronavirus data sources, blockchain functions, AI functions, and stakeholders; blockchain is discussed for outbreak monitoring, safe day-to-day operations, medical supply chain, and donation tracing, while AI is discussed for outbreak estimation, coronavirus detection, coronavirus analytics, vaccine/drug development, and prediction of future coronavirus-like outbreaks.
  • In the case study, a federated GAN approach with five institutions used 620 X-ray images in three classes (COVID-19, normal, pneumonia) and generated 1,500 synthetic X-ray images; the advanced FL scheme achieved COVID-19 precision 0.983, sensitivity 0.993, and F1-score 0.995, outperforming standalone without GAN (0.891, 0.865, 0.921), standalone with GAN (0.914, 0.872, 0.927), and FL without GAN (0.950, 0.973, 0.986).
  • The survey cites AI detection results including ResNet50 with 98% accuracy on chest X-ray radiographs, a 3D DL location-attention model with 86.7% accuracy for COVID-19, Influenza-A viral pneumonia, and healthy cases, and ResNet18 with 98.06% COVID-19 detection performance in federated X-ray learning.
  • As of 21 September 2020, WHO reported 30,949,804 confirmed COVID-19 cases and 959,116 deaths; the paper identifies challenges in regulatory consideration, privacy preservation, security of blockchain/AI ecosystems, lack of unified databases, and implementation constraints.

Methods

  • The survey used a systematic mapping study: search terms included blockchain, machine learning, deep learning, and COVID-19; four scientific databases were searched (IEEE Xplore, ACM Digital Library, Springer Link, ScienceDirect); papers were screened by title, excluding low-quality, unavailable, or non-English works, then keyworded and extracted for mapping.
  • The case study developed a federated AI approach for COVID-19 detection using GANs with generator and discriminator CNNs; five institutions trained local GAN models on their own X-ray datasets and exchanged parameters with a cloud server for aggregation, keeping actual images local.
  • A CNN-based classifier with three convolutional layers and Adam optimizer was evaluated on 620 X-ray images in three classes, with 1,500 synthetic images generated; precision, sensitivity, and F1-score were averaged over five runs.

Limitations

  • The paper states challenges rather than a formal limitations section: regulatory/accountability issues, privacy preservation, security weaknesses in blockchain and AI, lack of unified coronavirus databases, and implementation constraints including hardware/storage demands, energy consumption, network latency, and limited blockchain throughput (e.g., Bitcoin 4 transactions/second, Ethereum about 20 transactions/second, versus Visa up to 1667 transactions/second).
  • Evident from the text: the case study is simulation-based with a small dataset (620 X-ray images) and five institutions, with no clinical deployment or external validation reported.
  • Evident from the text: the survey relies on rapidly emerging literature, including many arXiv and bioRxiv preprints, and some cited use cases are company claims or trials not independently verified; the comparison table lists limitations of related works but leaves the “Our paper” limitations cell blank.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1109/access.2021.3093633

Cites

Statistics

Nothing to recompute.

Claims the paper makes

0 of 88 cited sources checked · 0 supported · 0 flagged

  • Findingreasoning

    The proposed advanced FL scheme has better sensitivity and F1-score performances than the compared schemes.

    “Our scheme is also much better in sensitivity and F1-score performances.”
    Quote in the paperSubstantial claim
  • Findingown data

    The proposed FL model generates 1500 synthetic X-ray images which are combined with an actual dataset for COVID-19 classification.

    “By using the proposed FL model, we generate 1500 synthetic X-ray images which are then combined with an actual dataset for COVID-19 classification.”
    Quote in the paperSubstantial claim
  • Findingown data

    The proposed advanced FL scheme outperforms all other state-of-the-art schemes in precision, sensitivity, and F1-score for COVID-19 detection.

    “As illustrated in Table 3, our advanced FL scheme outperforms all other state-of-the-art schemes in terms of these three metrics.”
    Quote in the paperSubstantial claim
  • Premisecitation

    Blockchain with its distributed nature can work well when any party fails due to its decentralized nature.

    “Specially, blockchain with its distributed nature can work well when any party fails due to its decentralized nature [12].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [12] L. Garg, E. Chukwu, N. Nasser, C. Chakraborty, and G. Garg, “ Anonymity preserving IoT-based COVID-19 and other infectious disease contact tracing model,” IEEE Access, vol. 8, pp. 159402– 159414, 2020. 34786286 PMC8545304 source

      Not checked in the free run.

  • Interpretationreasoning

    The immutability feature of blockchain makes data fabrication significantly more difficult because reported data will be under scrutiny in the future by statistical and longitudinal data analysis.

    “Here, we argue that the immutability feature of blockchain would make the data fabrication significantly more difficult since the reported data shall be under scrutiny in the future by statistical and longitudinal data analysis.”
    Quote in the paperSubstantial claim
  • May combine two claims. 42
  • Classified as probably not a substantial claim. 38

  • May combine two claims.

    Findingown data

    The advanced FL scheme with GAN can learn over the entire data span from distributed institutions, achieving better minimum loss and helping extract better image features for efficient data augmentation.

    “Meanwhile, our advanced FL scheme with GAN can learn over the entire data span from distributed institutions which achieves better minimum loss and helps extract better image features for efficient data augmentation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    For the COVID-19 class, the precision of the proposed advanced FL scheme is 0.983, higher than the standalone scheme without GAN (0.891), the GAN-based approach (0.914), and the FL scheme without GAN (0.950).

    “For instance, for the COVID-19 class, the precision of our scheme is the best (0.983), while the standalone scheme without GAN, the GAN-based approach, and the FL scheme achieve lower performances, with 0.891, 0.914, and 0.950, respectively.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Blockchain can combat COVID-19-like pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during outbreak tracing.

    “In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Blockchain constructs immutable ledgers of transactions for medical data sharing systems, and transactions recorded in the blockchain cannot be modified or altered by any entities.

    “Indeed, the blockchain constructs immutable ledgers of transactions for medical data sharing systems. Transactions (e.g., COVID-19 data) recorded in the blockchain cannot be modified or altered by any entities.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [11] H.-N. Dai, M. Imran, and N. Haider, “ Blockchain-enabled Internet of medical things to combat COVID-19,” IEEE Internet Things Mag., vol. 3, no. 3, pp. 52– 57, Sep. 2020.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    There is a lack of a reliable data surveillance system that would instantly give relevant healthcare organizations the information they need about potential outbreaks.

    “At present, there is a lack of a reliable data surveillance system that would instantly give relevant healthcare organizations the information they need about potential outbreaks.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    It is challenging to process coronavirus data with complex patterns and large volumes by using human-depending medicine tools.

    “Furthermore, it is very challenging to process coronavirus data with complex patterns and large volumes by using human-depending medicine tools.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisereasoning

    Existing healthcare systems have limitations in timely handling public health emergencies.

    “The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisereasoning

    Blockchain coupled with smart contract technology eliminates the reliance on central servers to ensure fairness among transaction parties and provides effective access control over data usage.

    “More importantly, blockchain coupled with the smart contract technology eliminates the reliance on central servers to ensure fairness among transaction parties, e.g., patients, hospitals, governments and provide effective access control over data usage.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing.

    “Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    The use of unreliable information makes it challenging for potential outbreak identification and quarantine.

    “The use of unreliable information makes it challenging for potential outbreak identification and quarantine.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Most current coronavirus information comes from separate sources such as the public, hospitals, and clinical labs with a large amount of inaccurate data without being monitored thoroughly.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    Smart contracts potentially provide strong transparency for the involved blockchain network.

    “Due to the elimination of the involvement of external authorities, smart contracts potentially provide strong transparency for the involved blockchain network.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The use of wrong data or unreliable database sources may result in biased analytical results, which can lead to fatal consequences like incorrect COVID-19 diagnosis.

    “The use of wrong data or unreliable database sources may result in biased analytical results, which can lead to fatal consequences like incorrect COVID-19 diagnosis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The area under the receiver operating characteristic curve (AUC) was computed to determine the adverse effects which support the clinical decision making for appropriate patient care.

    “The area under the receiver operating characteristic curve (AUC) was then computed to determine the adverse effects which support the clinical decision making for appropriate patient care.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [22] D. Colombi, F. C. Bodini, M. Petrini, G. Maffi, N. Morelli, G. Milanese, M. Silva, N. Sverzellati, and E. Michieletti, “ Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia,” Radiology, vol. 296, no. 2, pp. E86– E96, Aug. 2020. 32301647 PMC7233411 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Consensus mechanism such as Proof of Work (PoW) in the blockchain ensures that the state of the entire blockchain is consistent and secure without the need for any third parties.

    “The consensus mechanism such as Proof of Work (PoW) in the blockchain ensures that the state of the entire blockchain is consistent and secure without the need for any third parties.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Modelling the coronavirus structure is important to shed light on which drugs could work against COVID-19.

    “To prevent the COVID-19 disease, modelling the coronavirus structure is very important to shed lights on which drugs could work against COVID-19.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    AI helps build prediction models that can be used to estimate the outbreak in the future, which is useful for governments in preparing necessary solutions in response to COVID-19.

    “It also helps build prediction models that can be used to estimate the outbreak in the future, which is very useful for governments in preparing necessary solutions in response to COVID-19.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: AI is able to learn from huge datasets on relevant factors including the number of cases, deaths, demographics, and related environments conditions.

  • May combine two claims.

    Interpretationnone given

    In the coronavirus epidemic, AI can detect virus and predict how the virus is going to spread by analysing the combined information of environmental conditions, access to healthcare, and the way it is transmitted.

    “AI can be used to detect virus and predict how the virus is going to spread by analysing the combined information of environmental conditions, access to healthcare, and the way it is transmitted.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Blockchain can support reliable COVID-19 analytics by ensuring the reliability of collected data and the correct ordering of COVID-19 data records due to consensus mechanisms.

    “In such contexts, blockchain is high of need to ensure the reliability of the collected data thanks to its security nature.”
    • Also stated in: § Motivations of Using Blockchain and AI for Coronavirus Fighting · plain
      “Blockchain also ensures the correct ordering of the COVID-19 data records from the data sources to the destinations (e.g., hospitals or clinical labs) due to consensus mechanisms, which also ensures the high quality of data collection.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    AI can identify coronavirus within localized outbreaks of the disease and help reveal the nature of the virus.

    “Based on that, AI can identify coronavirus within localized outbreaks of the disease and help reveal the nature of the virus [21].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [21] J. Mushtaq, R. Pennella, S. Lavalle, A. Colarieti, S. Steidler, C. M. A. Martinenghi, D. Palumbo, A. Esposito, P. Rovere-Querini, M. Tresoldi, G. Landoni, F. Ciceri, A. Zangrillo, and F. De Cobelli, “ Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: Analysis of 697 Italian patients,” Eur. Radiol., vol. 31, no. 3, pp. 1770– 1779, Mar. 2021. 32945968 PMC7499014 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Real-world use cases have demonstrated the potential of AI in supporting the fighting of infectious epidemics by forecasting the spread of virus and helping the drug production for treatment of infectious diseases.

    “These real-world use cases have demonstrated the potential of AI in supporting the fighting of infectious epidemics by forecasting the spread of virus and helping the drug production for treatment of infectious diseases.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisenone given

    Many sources of COVID-19 data are collected from hospitals, the public, or media without being protected, which can result in COVID-19 data modifications.

    “Moreover, in the emergency epidemic situation, many sources of COVID-19 data are collected from hospitals, the public, or media without being protected which can result in COVID-19 data modifications.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Blockchain has proved its success in healthcare and biomedical applications with promising performances in terms of healthcare data security, secure data management, and transparent medical data storage.

    “Up to date, blockchain has proved its success in healthcare and biomedical applications with promising performances in terms of healthcare data security [49], secure data management [50], [51], and transparent medical data storage [52], [53].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [49] A. Hasselgren, K. Kralevska, D. Gligoroski, S. A. Pedersen, and A. Faxvaag, “ Blockchain in healthcare and health sciences—A scoping review,” Int. J. Med. Informat., vol. 134, Feb. 2019, Art. no. 104040. 31865055 source

      Not checked in the free run.

    • cites [50], [51]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [52], [53]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    In the coronavirus outbreak fighting, blockchain can simplify the process of fast-tracking drug trials, and recording and tracking all fundraising activities and donations, in an immutable fashion, which can support the management of outbreaks and treatment.

    “In the coronavirus outbreak fighting, blockchain can actively simplify the process of fast-tracking drug trials, and recording and tracking all fundraising activities and donations, in an immutable fashion, which can support the management of outbreaks and treatment.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    Blockchain database distributed across a network of participants provides high robustness and security for database stored on blockchain with no single-point failure.

    “This decentralized concept provides high robustness and security for database stored on blockchain with no single-point failure.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Logistic regression models were used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit admission or death versus no ICU admission or death).

    “More specifically, logistic regression models were used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit admission or death versus no ICU admission or death).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [22] D. Colombi, F. C. Bodini, M. Petrini, G. Maffi, N. Morelli, G. Milanese, M. Silva, N. Sverzellati, and E. Michieletti, “ Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia,” Radiology, vol. 296, no. 2, pp. E86– E96, Aug. 2020. 32301647 PMC7233411 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    Traceability and decentralization are among the key unique features of blockchain that cannot be found in other traditional security techniques.

    “Such traceability and decentralization are among the key unique features of blockchain that cannot be found in other traditional security techniques.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    In the distributed ledger, each record contains a unique cryptographic signature decoupled with a timestamp which makes the ledger resistant to be modified.

    “In the distributed ledger, each record contains a unique cryptographic signature decoupled with a timestamp which makes the ledger resistant to be modified.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    The unique advantage of blockchain is its ability to ensure the chronological order of data records such as COVID-19 data.

    “The unique advantage of blockchain is its ability to ensure the chronological order of data records such as COVID-19 data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Blockchain can offer viable solutions for coronavirus-related services such as outbreak monitoring, safe day-to-day operations, medical supply chain, and donation tracing.

    “blockchain can offer a number of viable solutions for coronavirus-related services such as outbreak monitoring, safe day-to-day operations, medical supply chain, and donation tracing.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Recent studies show that AI has been mostly used for solving coronavirus-related issues via two key approaches: machine learning (ML) and deep learning (DL).

    “Recent studies show that AI has been mostly used for solving coronavirus-related issues via two key approaches: machine learning (ML) and deep learning (DL) [56].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [56] D. C. Nguyen, P. Cheng, M. Ding, D. Lopez-Perez, P. N. Pathirana, J. Li, A. Seneviratne, Y. Li, and H. V. Poor, “ Enabling AI in future wireless networks: A data life cycle perspective,” IEEE Commun. Surveys Tuts., vol. 23, no. 1, pp. 553– 595, 1st Quart., 2021.

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    Blockchain can establish communication with hospitals and COVID-19 research centers and securely transfer COVID-19 data to cloud computing where AI functions are located for data processing and computation.

    “blockchain can establish the communication with hospitals and COVID-19 research centers and transfer securely COVID-19 data to the cloud computing where AI functions are located for data processing and computation.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    An AI company in the US utilized ML-powered interactive graphs tracking the virus migration across China, for creating an alert system whereby users will be able to receive information about whether an infected individual has travelled within their vicinity.

    “For example, recently an AI company in the US specialising has utilized ML-powered interactive graphs are tracking the virus migration across China [60], for creating an alert system, whereby users will be able to receive information about whether an infected individual has travelled within their vicinity.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [60] N. Lu, K.-W. Cheng, N. Qamar, K.-C. Huang, and J. A. Johnson, “ Weathering COVID-19 storm: Successful control measures of five Asian countries,” Amer. J. Infection Control, vol. 48, no. 7, pp. 851– 852, Jul. 2020. PMC7189844 32360746 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Federated learning potentially prevents disclosing sensitive user information and user preference, and thus mitigates privacy leakage risks in the COVID-19 pandemic.

    “This potentially prevents disclosing sensitive user information and user preference, and thus mitigates privacy leakage risks in this pandemic.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    AI can provide support for coronavirus fighting via five main applications: outbreak estimation, coronavirus detection, coronavirus analytics, vaccine/drug development, and prediction of future coronavirus-like outbreak.

    “AI can provide support for coronavirus fighting via five main applications, namely outbreak estimation, coronavirus detection, coronavirus analytics, vaccine/drug development, and prediction of any future coronavirus-like outbreak.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Several preliminary studies use temperature checking-based techniques such as facial and body temperature detection with smart helmet for detecting people with abnormal temperature, aiming to identify potentially infected persons.

    “For example, several preliminary studies in [58] use temperature checking-based techniques such as facial and body temperature detection with smart helmet [59] for detecting people with abnormal temperature, aiming to identify potentially infected persons.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [58] K. P Rane, “ Design and development of low cost humanoid robot with thermal temperature scanner for COVID-19 virus preliminary identification,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 3, pp. 3485– 3493, Jun. 2020.

      Not checked in the free run.

    • cites [59] M. Mohammed, H. Syamsudin, S. Al-Zubaidi, R. R. Aks, and E. Yusuf, “ Novel COVID-19 detection and diagnosis system using IoT based smart helmet,” Int. J. Psychosocial Rehabil., vol. 24, no. 7, pp. 2296– 2303, 2020.

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The outbreak of fake news generates fear and panic due to unverified rumours and exaggerated claims, and promotes racist forms of digital vigilantism and scapegoating.

    “The outbreak of fake news also generates fear and panic due to unverified rumours and exaggerated claims, and promotes racist forms of digital vigilantism and scapegoating [79].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [79] M. Cinelli, W. Quattrociocchi, A. Galeazzi, C. M. Valensise, E. Brugnoli, A. L. Schmidt, P. Zola, F. Zollo, and A. Scala, “ The COVID-19 social media infodemic,” Sci. Rep., vol. 10, no. 1, pp. 1– 10, Dec. 2020. 33024152 PMC7538912

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The decentralized concept of blockchain ensures payments in a secure, transparent, and immutable interface, free from human interaction.

    “The decentralized concept of blockchain ensures payments in a secure, transparent, and immutable interface, which is free from human interaction.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Electronic payment among customers and companies can be performed via blockchain instead of cash, which may be a source of coronavirus spread.

    “electronic payment among customers and companies now can be performed via blockchain instead of cash which may be a source of coronavirus spread [81].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [81] A. Jain, A. Sarupria, and A. Kothari, “ The impact of COVID-19 on e-wallet’s payments in Indian economy,” Int. J. Creative Res. Thoughts, pp. 2447– 2454, 2020.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    During outbreaks of infection, people are likely to experience fear of falling sick or dying themselves, feelings of helplessness, and stigma.

    “During outbreaks of infection, people are likely to experience fear of falling sick or dying themselves, feelings of helplessness, and stigma [96].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [96] A. V. Mattioli, S. Sciomer, C. Cocchi, S. Maffei, and S. Gallina, “ Quarantine during COVID-19 outbreak: Changes in diet and physical activity increase the risk of cardiovascular disease,” Nutrition, Metabolism Cardiovascular Diseases, vol. 30, no. 9, pp. 1409– 1417, Aug. 2020. PMC7260516 32571612

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    From the authors' observation and discussions, the use of blockchain has great potential to support strategies towards fighting COVID-19 and control the epidemic effectively.

    “From our observation and discussions, the use of blockchain has the great potential to support strategies towards fighting the COVID-19 disease and control effectively the COVID-19 epidemic.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Blockchain can process 2,919 different types of documents, which is faster than traditional working approaches.

    “The trial results indicate that blockchain can process 2,919 different types of documents, which is even faster than the traditional working approaches.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [80] Blockchain and AI Amidst the Coronavirus Crisis: A Call to Arms. Accessed: May 30, 2021. [Online]. Available: https://www.cryptonewsz.com/blockchain-and-ai-amidst-the-coronavirus-crisis-a-call-to-arms/

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    During home confinement in the lockdown period, sleep timing has markedly changed, with people going to bed and waking up later, and spending more time in bed.

    “during home confinement in the lockdown period, sleep timing has markedly changed, with people going to bed and waking up later, and spending more time in bed [95].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [95] N. Cellini, N. Canale, G. Mioni, and S. Costa, “ Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy,” J. Sleep Res., vol. 29, no. 4, p. e13074, Aug. 2020. 32410272 PMC7235482

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Ant Financial has launched a blockchain-enabled online bid opening system to replace cash-based approaches, allowing contactless bidding from remote locations during quarantine.

    “Another example is Ant Financial which has been launching a blockchain-enabled online bid opening system to replace the cash-based approaches [83].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [83] How Blockchain Technology is Helping Businesses Overcome the COVID-19 Outbreak in China. Accessed: May 30, 2021. [Online]. Available: https://irishtechnews.ie/blockchain-businesses-overcome-COVID-19-China/

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    By using blockchain, supply chain issues can be solved, helping save thousands of lives and billions of dollars.

    “By using blockchain, the supply chain issues can be solved, helping to save thousands of lives and billions of dollars [89].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [89] Five Ways Blockchain Can Unblock the Coronavirus Medical Supply Chain. Accessed: May 30, 2021. [Online]. Available: https://forbes.com/sites/nishandegnarain/2020/03/22/5-ways-blockchain-can-unblock-the-coronavirus-medical-supply-chain/

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    This blockchain-based solution helps reduce the risk of infection from face-to-face contact and mitigates the possibility of virus spread in the community.

    “this blockchain-based solution really helps to reduce the risk of infection from face-to-face contact that accordingly mitigates the possibility of virus spread in the community.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Blockchain can offer five key solutions to support the medical supply chain in the coronavirus crisis: product requirements, supply creditability, transportation tracking, financial payments, and customs certifications.

    “In summary, blockchain can offer five key solutions to support the medical supply chain in the coronavirus crisis: •Product requirements: provide a solution to update real-time demands and medical factories for fast response (e.g., supply chain rate adjustment).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    According to a recent study, from December 2019 through February 2020 as the pandemic spread, populations with a higher rate of poverty showed an increased consumption of unhealthy food.

    “according to a recent study [97], from December 2019 through February 2020 as the pandemic spread, the populations with a higher rate of poverty showed an increased consumption of unhealthy food.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [97] N. J. S. Ashby, “ Impact of the COVID-19 pandemic on unhealthy eating in populations with obesity,” Obesity, vol. 28, no. 10, pp. 1802– 1805, Oct. 2020. 32589788 PMC7361200

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    During the pandemic, collecting sufficient data to implement intelligent algorithms is more challenging and user privacy concerns are growing due to public data sharing with datacentres for COVID-19 data analytics.

    “However, in the pandemic, collecting sufficient data to implement intelligent algorithms becomes more challenging and the user privacy concerns are growing due to the public data sharing with datacentres for COVID-19 data analytics [135].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [135] M. Loey, F. Smarandache, and N. E. M. Khalifa, “ Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning,” Symmetry, vol. 12, no. 4, p. 651, Apr. 2020.

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    In FL, each institution trains its local DL model using local COVID-19 images and only model parameters such as gradients are exchanged while actual data and sensitive user information need not be shared.

    “In this way, each institution participates in training their DL model using their local COVID-19 images, e.g., X-ray and computed tomography (CT) images, and only model parameters such as gradients are exchanged while there is no need to share actual data and sensitive user information.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Data threats or adversaries can enter blockchain software to hold control of the blockchain, leading to modifications of medical transaction or patient data and raising privacy concerns.

    “Data threats or adversaries can enter the blockchain software to hold the control of the blockchain, which can lead to serious consequences, like modifications of medical transaction or patient data information, raising privacy concerns [158].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [158] X. Li, P. Jiang, T. Chen, X. Luo, and Q. Wen, “ A survey on the security of blockchain systems,” Future Gener. Comput. Syst., vol. 107, pp. 841– 853, Jun. 2020. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The stochastic model based on a Markov chain can estimate the number of cases in Mexico, facilitating government estimation of virus spread and appropriate actions.

    “This also can estimate the number of cases in Mexico that facilitates the government in the estimation of virus spread making appropriate actions for future fightings.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [131] A. Vivanco-Lira, “ Predicting COVID-19 distribution in Mexico through a discrete and time-dependent Markov chain and an SIR-like model,” 2020, arXiv:2003.06758. [Online]. Available: http://arxiv.org/abs/2003.06758

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Simulations from 34,006 CT scan slices of 89 subjects verify high COVID-19 X-ray image classification and low data loss in the FL algorithm running.

    “Simulations from 34,006 CT scan slices (images) of 89 subjects verify a high COVID-19 X-ray image classification and low data loss in the FL algorithm running.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [136] R. Kumar, A. A. Khan, S. Zhang, J. Kumar, T. Yang, N. A. Golalirz, Zakria, I. Ali, S. Shafiq, and W. Wang, “ Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging,” 2020, arXiv:2007.06537. [Online]. Available: http://arxiv.org/abs/2007.06537 PMC8791443 35789224 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    Clustering algorithms may be useful to classify different cities and regions for investigating virus transmissions for outbreak prediction.

    “Clustering algorithms may be useful to classify different cities, regions for investigating the virus transmissions for outbreak prediction.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The reported results are averaged from five runs of numerical simulations.

    “For reliable evaluation, the reported results are averaged from five runs of numerical simulations.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The advanced FL scheme can generate better realistic X-ray images, leading to higher detection rates.

    “As a result, our scheme can generate better realistic X-ray images that leads to higher detection rates.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A recipient-oriented transaction processing solution validates transactions before appending to the block to mitigate double spending attacks.

    “Moreover, to mitigate double spending attacks in blockchain, a solution with recipient-oriented transaction processing is introduced in [170], aiming to validate the transaction before appending to the block.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [170] H. Lee, M. Shin, K. S. Kim, Y. Kang, and J. Kim, “ Recipient-oriented transaction for preventing double spending attacks in private blockchain,” in Proc. 15th Annu. IEEE Int. Conf. Sens., Commun., Netw. (SECON), Jun. 2018, pp. 1– 2.

      Not checked in the free run.

  • May combine two claims.

    Interpretationnone given

    Blockchain still has security issues, such as 51% attack in block mining and double spending attacks on data storage on blockchain.

    “Despite its great potential, blockchain still exists security issues, such as 51% attack in the block mining and double spending attacks on data storage on blockchain.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Alibaba has integrated AI with cloud computing for supporting coronavirus data analytics.

    “For instance, Alibaba has integrated AI with cloud computing for supporting coronavirus data analytics [172].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [172] Y. Wang, T. Wang, Y. Cui, H. Mei, X. Wen, J. Lu, and W. Chen, “ COVID-19 data visualization public welfare activity,” Vis. Informat., vol. 4, no. 3, pp. 51– 54, Sep. 2020. PMC7486205 38620335 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Drones help reinforce contactless monitoring of the outbreak.

    “Besides, drones also help to reinforce the contactless monitoring of the outbreak.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Hashlog is a blockchain solution created by Acoer to combat and control coronavirus spreading by ensuring logging and data visualization of the outbreak from CDC and WHO public data.

    “They has come up with a HashLog blockchain solution to combat and control the coronavirus from spreading. This can be enabled by the distributed blockchain ledger technology which ensures logging and data visualization of the coronavirus outbreak from the public data of US Centres for Disease Control (CDC) and WHO.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [142] Acoer Coronavirus Tracker, Powered By Hedera Hashgraph, Now Freely Available to General Public With Added Clinical Trial Data. Accessed: May 30, 2021. [Online]. Available: https://www.hedera.com/blog/acoer-coronavirus-tracker-powered-by-hedera-hashgraph-now-freely-available-to-general-public-with-added-clinical-trial-data

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    Malicious attacks can inject false data or adversarial sample inputs which makes AI learning invalid, while the AI model can be tampered with.

    “More importantly, malicious attacks can inject false data or adversarial sample inputs which makes AI learning invalid, while the AI model can be tampered with [159].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [159] Q. Liu, T. Liu, Z. Liu, Y. Wang, Y. Jin, and W. Wen, “ Security analysis and enhancement of model compressed deep learning systems under adversarial attacks,” in Proc. 23rd Asia South Pacific Design Automat. Conf. (ASP-DAC), Jan. 2018, pp. 721– 726.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A mining pool strategy is proposed to enhance the efficiency of the mining process by solving security bottlenecks such as 51% vulnerability with reduced block and transaction propagation delays.

    “For example, a mining pool strategy is proposed in [169] to enhance the efficiency of the mining process, by solving security bottlenecks such as 51% vulnerability with reduced block and transaction propagation delays.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [169] X. Li, P. Jiang, T. Chen, X. Luo, and Q. Wen, “ A survey on the security of blockchain systems,” Future Gener. Comput. Syst., vol. 107, pp. 841– 853, Jun. 2020.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Blockchain and AI can be incorporated with other technologies to build a comprehensive healthcare system.

    “To achieve better efficiency in solving epidemic-related issues, blockchain and AI can be incorporated with other technologies to build a comprehensive healthcare system.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Resourceful storage and high computation capability are key features that cloud can provide to facilitate AI analytics.

    “The resourceful storage and high computation capability are the key features that cloud can provide to facilitate AI analytics.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Most current coronavirus-related databases come from individual resources and are not sufficient for large-scale AI operations.

    “Most of the current coronavirus-related databases come from individual resources, e.g., social media [160], patient collection [161], [162], but they are not sufficient for large-scale AI operations that potentially creates greater impacts on fighting COVID-19, compared to the current results.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [160] E. Chen, K. Lerman, and E. Ferrara, “ COVID-19: The first public coronavirus Twitter dataset,” 2020, arXiv:2003.07372. [Online]. Available: https://arxiv.org/abs/2003.07372

      Not checked in the free run.

    • cites [161] J. P. Cohen, P. Morrison, and L. Dao, “ COVID-19 image data collection,” 2020, arXiv:2003.11597. [Online]. Available: http://arxiv.org/abs/2003.11597

      Not checked in the free run.

    • cites [162] W. Liu, P. T.-W. Yen, and S. A. Cheong, “ Coronavirus disease 2019 (COVID-19) outbreak in China, spatial temporal dataset,” 2020, arXiv:2003.11716. [Online]. Available: https://arxiv.org/abs/2003.11716

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Bitcoin can process a maximum of four transactions/second, Ethereum about 20 transactions/second, and Visa up to 1667 transactions/second.

    “For example, Bitcoin can only process a maximum of four transactions/second, and the throughput of Ethereum achieved is about 20 transactions/second, while Visa can process up to 1667 transactions/second [44].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [44] D. C. Nguyen, P. N. Pathirana, M. Ding, and A. Seneviratne, “ Integration of blockchain and cloud of things: Architecture, applications and challenges,” IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp. 2521– 2549, Aug. 2020. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    PHBC is a monitoring blockchain for continual and anonymous verification of communities and workplaces free from COVID-19 and other high-risk viruses and bacteria.

    “Another promising blockchain platform is PHBC [145] that is a monitoring blockchain for the continual and anonymous verification of communities and workplaces that are free from coronavirus COVID-19 as well as other high-risk viruses, bacteria, to aid them in staying free of deadly diseases.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [145] Public Health Blockchain Consortium: PHBC. Accessed: May 30, 2021. [Online]. Available: https://www.phbconsortium.org/

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    Infervision AI has improved CT diagnosis speed, which is important to accelerate pneumonia diagnosis for early detection of suspected patients.

    “Infervision AI has improved the CT diagnosis speed, which is highly important to accelerate pneumonia diagnosis for early detection of suspected patients.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [147] J. Huang, F. Liu, Z. Teng, J. Chen, J. Zhao, X. Wang, and R. Wu, “ Care for the psychological status of frontline medical staff fighting against coronavirus disease 2019 (COVID-19),” Clin. Infectious Diseases, vol. 71, no. 12, pp. 3268– 3269, Dec. 2020. PMC7184370 32246142 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    AlphaFold is under trial stages and its experiments have not yet been verified.

    “Up to date, AlphaFold is under the trial stages and experiments have yet not been verified.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [148] S. Dananjayan and G. M. Raj, “ Artificial intelligence during a pandemic: The COVID-19 example,” Int. J. Health Planning Manage., vol. 35, no. 5, pp. 1260– 1262, Sep. 2020. PMC7276785 32430976

      Not checked in the free run.

69 of 69

  • The paper relies on this without support.

    The truthfulness of each medical data transaction is hard to verify because involved medical entities might lie about the uploaded data.

    “Admittedly, the truthfulness of each medical data transaction is hard to verify since the involved medical entities might lie about the uploaded data.”

  • The paper relies on this without support.

    Most current coronavirus information comes from separate sources such as the public, hospitals, and clinical labs with a large amount of inaccurate data without being monitored thoroughly.

    “In fact, most of the current coronavirus information comes from separate sources such as the public, hospitals, clinical labs with a large amount of inaccurate data without being monitored thoroughly.”

  • The paper relies on this without support.

    The current coronavirus detection procedure often takes several hours to complete the virus tests.

    “Another limitation is the current time-consuming and in-accuracy coronavirus detection procedure that often takes several hours to complete the virus tests.”

  • The paper relies on this without support.

    AI is able to learn from huge datasets on relevant factors including the number of cases, deaths, demographics, and related environments conditions.

    “AI is able to learn from huge datasets on relevant factors including the number of cases, deaths, demographics, and related environments conditions.”

  • The paper relies on this without support.

    AI algorithms based on computer computation can learn data and predict how the proteins that make up an organism curve or crinkle based on their genomes to identify the shape of the receptor.

    “AI algorithms based on computer computation can learn data and predict how the proteins that make up an organism curve or crinkle based on their genomes to identify the shape of the receptor.”

  • The paper relies on this without support.

    Several patients may deliberately declare wrong symptoms to avoid hospital visits, which can lead to patient quarantine failure.

    “several patients can deliberately declare wrong their symptoms for avoiding hospital visits, which can lead to patient quarantine failure.”

  • The paper relies on this without support.

    When people are sick, dead due to coronavirus infection, or taking care of family members, they tend to change their patterns of phone usage.

    “when people are sick, dead due to coronavirus infection or taking care of family members, they tend to change their patterns of phone usage, which can be detected by AI algorithms run on the datasets provided by wireless operators.”

  • The paper relies on this without support.

    Maintaining a continuous supply of medicines and food has become a challenge for the healthcare sector during the pandemic.

    “In this pandemic crisis, maintaining a continuous supply of medicines and food has become a challenge for the healthcare sector.”

  • The paper relies on this without support.

    There is no effective treatment for the COVID-19 epidemic.

    “Up to now, there is no effective treatment for this COVID-19 epidemic.”

  • The paper relies on this without support.

    People in several Chinese cities rely on their mobile phone for daily public transportation.

    “people in several Chinese cities rely on their mobile phone for daily public transportation”

  • The paper relies on this without support.

    Social media platforms such as Facebook and Twitter are facilitators and multipliers of COVID-19-related misinformation.

    “these platforms are facilitators and multipliers of COVID-19-related misinformation.”

  • The paper relies on this without support.

    Countries are reluctant to share coronavirus-related databases.

    “Countries are now reluctant to share database, that make international healthcare organizations like WHO challenging to evaluate the epidemic for fighting effectively the virus spread.”

  • The paper relies on this without support.

    WHO cannot provide appropriate public health guidance without disaggregated data and detailed local epidemic information.

    “In fact, WHO cannot provide appropriate public health guidance without disaggregated data and detailed local epidemic information.”

  • The paper relies on this without support.

    The efficiency of intelligent healthcare data analytics mainly depends on AI algorithms.

    “The efficiency of intelligent healthcare data analytics, e.g., virus information analysis, mainly depends on the AI algorithms.”

  • The paper relies on this without support.

    Using drones would be the fastest way to deliver essential facilities to victims in quarantine areas where all modes of transport are interrupted.

    “In fact, using drones would be the fastest way to deliver essential facilities to the victims in the quarantine areas where all modes of transport are interrupted.”
15 of 15

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Claim of outperforming every scheme conflicts with pneumonia results

    Minor

    The advanced FL scheme does not outperform every comparator on all three metrics: for pneumonia, its sensitivity is 0.854 versus 0.964 for the standalone-with-GAN scheme, and its F1-score is 0.918 versus 0.963.

  • Conclusion counts five blockchain solutions where the survey lists four

    Minor

    The conclusion describes five key blockchain solutions and includes user privacy protection, whereas the blockchain-solutions section explicitly introduces four and does not list privacy protection among them.

  • Challenges are counted as four but presented under five headings

    Minor

    The challenges section says it analyzes four aspects, but it subsequently has five challenge subsections, including a separate "Implementation Challenges" subsection.

3 of 3

The citation lists haven’t been fetched yet

References 211 · Cited by 286

Research works printed but not linked: 18

  1. 2021, WHO Coronavirus Disease (COVID-19) Dashboard
  2. 2021, Blockchain and AI Technologies Combined to Battle Coronavirus—COVID-19 as Immuno-Oncology Company Mateon Partners With Meridian IT to Fast Track Drug Manufacturing
  3. 2021, BlueDot Outbreak Risk Software
  4. 2021, AI Helps Doctors Diagnose the Coronavirus
  5. 2020, Economic Outlook
  6. 10.1016/j.future.2017.08.020 · DOI · Paper page
  7. 2021, Iran Minister Accuses Some Countries of Not Declaring Their Coronavirus Cases
  8. 2021, Acoer Coronavirus Tracker Powered By Hedera Hashgraph Now Freely Available to General Public With Added Clinical Trial Data
  9. 2021, HyperChain-Blockchain Charity Platform to Fight Against the Coronavirus
  10. 2021, VeChain Announces Blockchain Vaccine Tracing Solution for China
  11. 2021, Open database of covid-19 cases with chest x-ray or ct images
  12. 2021, Phbc - public health blockchain consortium
  13. 2021, Blockhain AI and the Wuhan Coronavirus
  14. jain, 2020, The impact of COVID-19 on e-wallet’s payments in Indian economy, International Journal of Creative Research Thoughts, 2447
  15. 2021, How Blockchain Technology is Helping Businesses Overcome the COVID-19 Outbreak in China
  16. 2021, Blockchain and AI Amidst the Coronavirus Crisis A Call to Arms
  17. 2021, Five Ways Blockchain Can Unblock the Coronavirus Medical Supply Chain
  18. 2021, Alipay Launches Blockchain Based Platform to Help Combat Corona Virus

Authors
Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A.
Affiliations
  • Deakin University (AU)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
  • UNSW Sydney (AU)
Corresponding author
Nguyen, Dinh C.
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
9
Pages
95730-95753
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2021.3093633
PMC
PMC8545197
OpenAlex
W3016342701
Cited by
286
References
211
Citations by year
2026: 15 · 2025: 35 · 2024: 41 · 2023: 39 · 2022: 59 · 2021: 72 · 2020: 25
FWCI
47.61
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Own your potential (DEAKIN) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
  • Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
Keywords
blockchain, artificial intelligence, COVID-19 pandemic, pandemic response, outbreak detection, health data management, healthcare supply chain, contact tracing, symptom recognition, pharmaceutical manufacturing, federated learning, public health emergencies

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

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