Artificial Intelligence and Machine Learning technologies have been increasingly popular as potential research and disease management tools in medicine and healthcare. However, three potential challenges are limiting the implementation of such applications on a larger scale.
One challenge is the lack of explainability and interpretability. The AI field is still relatively new to many medical researchers and clinicians. It is then a considerable obstacle for many medical researchers to understand and adapt to this new tool.
The second challenge is the information published. Many researchers work together with mathematicians, data scientists, and computer scientists to optimize machine learning algorithms to generate accurate disease detection and monitoring models. Many published papers can be either too technical for other medical researchers to understand its significance or too general and lack sufficient data to be appropriately reviewed by data scientists/mathematicians. These issues limit the wide adoption and application of machine learning in the medical field.
The third challenge is its ethics and policy implications. How to institute an AI-based model at a practice? Should it work alongside the specialist or alone? What happens if a diagnosis is incorrect? These are questions remained without a clear answer and should be discussed and addressed.
It is undoubtedly clear that AI and machine learning are reshaping medicine. The aim of this themed article collection is to facilitate the understanding and adaptation of this emerging field to a broader research community. Without explainability and interpretability, it may be possible to have benchside research but not a bedside AI. We would like to achieve this goal by encouraging the submission and publishing of articles that build bridges between machine learning research and its clinical applications.
This Research Topic focuses on AI in use within medicine and its subspecialties OR focuses on AI in use within the medical subspecialties of ophthalmology and ocular oncology, inviting a multidisciplinary collaboration between mathematics, data scientists, clinicians, researchers, and regulatory bodies.
The articles that we would like to recruit can fall into the below scopes:
• Articles that aim to review or comment on recent machine learning applications in ophthalmology and its subspecialties, including eye cancer.
• Research articles on the development of new AI models for disease detection, monitoring, or treatment, focusing on medical implementation; and including coding scripts that can be used for reproducibility checks.
• Policy and practice reviews or perspective and opinion papers on introducing AI to the clinical practice; current practice, policy directives, ethical and legal considerations for the clinic and specialist.
• Surveys or review papers on recent advances in the interaction between AI and medicine applications in terms of methodology and modeling. We also welcome other types of articles (such as methods and trials) that can better understand and clarify the significance of AI and machine learning applications in medicine.
Editor J.Y. Holding patents & Chief Scientific Officer for iFix Medical Pty Ltd. The other Topic Editors declares no competing interests in regard to the Research Topic subject
Artificial Intelligence and Machine Learning technologies have been increasingly popular as potential research and disease management tools in medicine and healthcare. However, three potential challenges are limiting the implementation of such applications on a larger scale.
One challenge is the lack of explainability and interpretability. The AI field is still relatively new to many medical researchers and clinicians. It is then a considerable obstacle for many medical researchers to understand and adapt to this new tool.
The second challenge is the information published. Many researchers work together with mathematicians, data scientists, and computer scientists to optimize machine learning algorithms to generate accurate disease detection and monitoring models. Many published papers can be either too technical for other medical researchers to understand its significance or too general and lack sufficient data to be appropriately reviewed by data scientists/mathematicians. These issues limit the wide adoption and application of machine learning in the medical field.
The third challenge is its ethics and policy implications. How to institute an AI-based model at a practice? Should it work alongside the specialist or alone? What happens if a diagnosis is incorrect? These are questions remained without a clear answer and should be discussed and addressed.
It is undoubtedly clear that AI and machine learning are reshaping medicine. The aim of this themed article collection is to facilitate the understanding and adaptation of this emerging field to a broader research community. Without explainability and interpretability, it may be possible to have benchside research but not a bedside AI. We would like to achieve this goal by encouraging the submission and publishing of articles that build bridges between machine learning research and its clinical applications.
This Research Topic focuses on AI in use within medicine and its subspecialties OR focuses on AI in use within the medical subspecialties of ophthalmology and ocular oncology, inviting a multidisciplinary collaboration between mathematics, data scientists, clinicians, researchers, and regulatory bodies.
The articles that we would like to recruit can fall into the below scopes:
• Articles that aim to review or comment on recent machine learning applications in ophthalmology and its subspecialties, including eye cancer.
• Research articles on the development of new AI models for disease detection, monitoring, or treatment, focusing on medical implementation; and including coding scripts that can be used for reproducibility checks.
• Policy and practice reviews or perspective and opinion papers on introducing AI to the clinical practice; current practice, policy directives, ethical and legal considerations for the clinic and specialist.
• Surveys or review papers on recent advances in the interaction between AI and medicine applications in terms of methodology and modeling. We also welcome other types of articles (such as methods and trials) that can better understand and clarify the significance of AI and machine learning applications in medicine.
Editor J.Y. Holding patents & Chief Scientific Officer for iFix Medical Pty Ltd. The other Topic Editors declares no competing interests in regard to the Research Topic subject