Table 2.
Recent Contributions and Developments in AI in Healthcare.
| Authors | Year | Contribution |
|---|---|---|
| Guoguang Rong et al. [33] | 2020 | The paper focuses on AI developments in disease diagnostics and prediction, living assistance, biomedicine, biomedical research, etc. The major area covered by the reviewers is biomedicine. |
| Silvana Secinaro et al. [34] | 2021 | The review focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. It gives an overview of how AI is being used in these areas and briefs about the developments that need to be carried out. |
| Thomas Davenport et al. [35] | 2019 | This review paper showcases how AI is being used in healthcare, the relevance between AI and healthcare, various applications, and the implications related to the same. |
| Pouyan Esmaeilzadeh et al. [36] | 2020 | This study examines AI medical devices’ perceived benefits and risks with clinical decision support (CDS) features from consumers’ perspectives, sheds more light on factors affecting perceived risks, and proposes recommendations to practically reduce these concerns. |
| Jonathan Waring et al. [37] | 2020 | A state of the art review of 101 papers identifies the potential opportunities and barriers to using AutoML in healthcare and the existing applications of AutoML in healthcare. |
| Onus Asan et al. [38] | 2019 | This paper shows the clinician’s point of view: how AI is helping their work and domain, the challenges that usually arise, and the possible future scope of AI in healthcare. |
| Jiamin Yin et al. [39] | 2021 | Fifty-one healthcare studies were reviewed, targeting clinical tasks, disease diagnosis, risk analysis, and treatment. |
| DonHee Lee et al. [20] | 2021 | Reviews the current state of artificial intelligence [AI]-based technology applications and their impact on the healthcare industry, the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances in AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. |
| Adam Bohr et al. [21] | 2020 | Applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living are discussed in this review. |
| Nagendra et al. [22] | 2020 | This review compares the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. |