|
| Ref.No | Technologies used | Key contributions | Limitations |
|
| [19] | Blockchain-based FL framework | Training a global, more accurate ML model on hospital data can assist in detecting COVID-19 cases during lung screenings | It is challenging to share data securely (without compromising the privacy of users) and to train global models for -detecting positive cases |
|
| [20] | UCADI framework | A decentralized model, the unified CT-COVID AI diagnostic initiative, distributes and performs the AI model at each participating institution independently without sharing personal data | (i) Data deficiency |
| (ii) Data isolation |
| (iii) Data heterogeneity |
|
| [38] | AI and big data | The coronavirus disease COVID-19 is being controlled with the use of AI and big data | Privacy and security issues due to insufficient standard datasets |
|
| [39] | AI | In the fight against COVID-19, AI can contribute in six ways:
(i) Early warnings and alerts | (i) Too much, and
(ii) Too little
(iii) Data |
| (ii) Tracking and prediction |
| (iii) Data dashboards |
| (iv) Diagnosis and prognosis |
| (v) Treatments and cures, and |
| (v) Social control |
|
| [78] | Blockchain and AI | The coronavirus (COVID-19) epidemic can be combated using AI and blockchain technology | The lack of unified databases is a concern for protecting the privacy and the security of blockchain |
|
| [90] | AI-related technologies | A comparison of FL to training without an FL framework was conducted using four different models:
(i) MobileNet | FL presents a number of statistical and system challenges when distributed device networks are used to train machine models |
| (ii) ResNet18 |
| (iii) MoblieNet, and |
| (iv) COVID-net |
|
| [91] | A novel collaborative city DT framework | FL combined with city DTs alleviates the data sparsity challenge, facilitates collaboration, and provides privacy protection by design | Collaborative training problems, such as:
(i) Disaster surveillance and |
| (ii) Prediction |
|
