The term big data, defined to include both large quantities and diverse types of data, was mentioned in earnest in the 1990s, and the term artificial intelligence (AI) emerged even earlier: the first academic conference on AI occurred in 1956. Although the concept of big data and AI has been around for decades, there has never been as much interest in those as in recent times. We currently are in the midst of an abundance of articles involving big data science and AI in clinical medicine. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Both big data and AI research are data driven, rather than based on the traditional observation-to-hypothesis approach, allowing serendipities overcoming the traditional research methodology.
Despite the successful results of big data and AI in numerous studies, most of the clinicians and researchers are still hesitant to apply the results in real clinical settings. There are several essential and data-based analysis-specific limitations in big data and AI. First, massive data allows statistical significance to even small differences that are not clinically significant. Second, risks and anticipatory anxieties about applying mathematical and mechanical conclusions such as AI algorithms with lack of biophysiological interpretation is still existed. Third, the reliable, prospective real-world validations of both big data and AI study have markedly been lacking. As this field of research continues to advance, the limitations have to be overcome.
Research within the scope of linking big data and AI study in ophthalmology to allow discoveries outside the boundaries of the hurdles are invited.
The goal of this research topic is in order to highlight novel developments and discoveries in big data and AI in ophthalmology, to arouse drastic but scientific challenges to medical data science.
Specific themes we would like contributors to consider include:
(1) Advanced computational methods and large-scale datasets to concede new insights in ophthalmology.
(2) Novel concept for interpretation and application of AI in ophthalmology.
(3) Description for reappraising previous theories and hypotheses by using big data and/or AI interpretation.
Eligible article types include Original Articles, Reviews, Study Protocols, Methods and Data reports.
The term big data, defined to include both large quantities and diverse types of data, was mentioned in earnest in the 1990s, and the term artificial intelligence (AI) emerged even earlier: the first academic conference on AI occurred in 1956. Although the concept of big data and AI has been around for decades, there has never been as much interest in those as in recent times. We currently are in the midst of an abundance of articles involving big data science and AI in clinical medicine. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Both big data and AI research are data driven, rather than based on the traditional observation-to-hypothesis approach, allowing serendipities overcoming the traditional research methodology.
Despite the successful results of big data and AI in numerous studies, most of the clinicians and researchers are still hesitant to apply the results in real clinical settings. There are several essential and data-based analysis-specific limitations in big data and AI. First, massive data allows statistical significance to even small differences that are not clinically significant. Second, risks and anticipatory anxieties about applying mathematical and mechanical conclusions such as AI algorithms with lack of biophysiological interpretation is still existed. Third, the reliable, prospective real-world validations of both big data and AI study have markedly been lacking. As this field of research continues to advance, the limitations have to be overcome.
Research within the scope of linking big data and AI study in ophthalmology to allow discoveries outside the boundaries of the hurdles are invited.
The goal of this research topic is in order to highlight novel developments and discoveries in big data and AI in ophthalmology, to arouse drastic but scientific challenges to medical data science.
Specific themes we would like contributors to consider include:
(1) Advanced computational methods and large-scale datasets to concede new insights in ophthalmology.
(2) Novel concept for interpretation and application of AI in ophthalmology.
(3) Description for reappraising previous theories and hypotheses by using big data and/or AI interpretation.
Eligible article types include Original Articles, Reviews, Study Protocols, Methods and Data reports.