The 100 most influential papers in medical artificial intelligence; a bibliometric analysis.

Objective: To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. METHODS: The bibliometric analysis was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. RESULTS: Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal 'Artificial Intelligence in Medicine' accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion: The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review.

BACKGROUND: Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. OBJECTIVE: The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. METHODS: We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. CONCLUSIONS: There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

Design of activation functions for inference of fuzzy cognitive maps: application to clinical decision making in diagnosis of pulmonary infection.

OBJECTIVES: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. METHODS: The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. RESULTS: Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. CONCLUSIONS: Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.

Design of Activation Functions for Inference of Fuzzy Cognitive Maps: Application to Clinical Decision Making in Diagnosis of Pulmonary Infection / 대한의료정보학회지

OBJECTIVES: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. METHODS: The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. RESULTS: Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. CONCLUSIONS: Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.

Design of Activation Functions for Inference of Fuzzy Cognitive Maps: Application to Clinical Decision Making in Diagnosis of Pulmonary Infection / 대한의료정보학회지

OBJECTIVES: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. METHODS: The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. RESULTS: Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. CONCLUSIONS: Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.