With the wide application of artificial intelligence (AI) in the medical field, more and more AI-based clinical decision support systems have been applied in the clinical diagnosis, screening, and other fields. Early-stage clinical evaluation is important for evaluating the clinical performance, safety, and human factors of AI-based clinical decision support systems, and laying the foundation for large-scale trials. However, the transparency and integrity of the clinical reports need to be improved. The Developmental and Exploratory Clinical Investigations of DEcision Support Systems Driven by Artificial Intelligence (DECIDE-AI) was officially published online in May 2022. Based on this guideline and relative literature, this paper explores the transparent reporting of early-stage clinical evaluation of AI-based clinical decision support systems, in order to help developers and researchers better understand and apply the relevant guidelines, and improve the reporting transparency of early-stage clinical evaluation of AI-based clinical decision support systems.
Researches about prediction models based on machine learning in primary care developed rapidly in recent years, but there are few researches about the design features and methodological quality.
To systematacially summarize and analyze the design features and methodological quality of researches about prediction models based on machine learning in primary care.
Researches about prediction models based on machine learning in primary care was searched in PubMed, Embase, CNKI, Wanfang Data published from base-building to 2023-02-21, descriptive summary and description methods were used to analyze the basic characteristics of the included literature, types of prediction models, sample size, handling method of missing value, types of machine learning algorithms, model performance evaluation index and prediction efficiency, and model verification method.
Totally 30 literature were enrolled, involving 106 prediction models, thereinto 17 literature were published between 2021 and 2023; research topics: respiratory disease in 6 literature, tumour in 4 literature, outpatient appointment in 3 literature; sample size over 1 000 in 26 literature (accounting for 86.67%, 95%CI=68.36%-95.64%) ; using machine learning methods to hand missing value in 7 literature; 65 prediction models used tree-based machine learning algorithm, in which random forest was the most frequently used (accounting for 32.08%, 95%CI=23.53%-41.95%) ; 61 prediction models used AUC of ROC or consistency (C statistic) as the differentiation evaluation index (accounting for 57.55%, 95%CI=47.57%-66.97%), but only 14 prediction models reported prediction models (accounting for 13.21%, 95%CI=7.67%-21.50%) ; the differentiation of most of the 106 prediction models was good, but bias risk assessment results of 92 prediction models were high-risk (accounting for 86.79%, 95%CI=78.50%-92.33%) ; only 7 literature involved prediction models conducted the external validation.
Researches about prediction models based on machine learning in primary care increase gradually in the past three years, in which the topics mainly involve respiratory disease, tumour, outpatient appointment and so on; there are significant difference in sample size and handling method of missing value in the 106 prediction models, most of the 106 prediction models are with good differentiation, but most of them did not conducted the external validation, and the overall risk of bias is relatively high.
With the development of information technology, artificial intelligence shows great potentials for clinical diagnosis and treatment. Nevertheless, bias in algorithms derived by artificial intelligence can lead to problems such as unequal distribution of healthcare resources, which significantly affect patients' health equity. Algorithmic bias is a technical manifestation of human bias, whose formation strongly correlates with the entire development process of artificial intelligence, starting from data collection, model training and optimization to output application. Healthcare providers, as the key direct participants in ensuring patients' health, should take corresponding measures to prevent algorithmic bias to avoid its related health equity issues. It is important for healthcare providers to ensure the authenticity and unbiasedness of health data, optimize the fairness of artificial intelligence, and enhance the transparency of its output application. In addition, healthcare providers need to consider how to tackle bias-related health inequity, so as to comprehensively ensure patients' health equity. In this study, we reviewed the causes and coping strategies related to algorithmic bias in healthcare, with the aim of improving healthcare providers' awareness and ability to identify and address algorithmic bias, and laying a foundation for ensuring patients' health equity in the information age.
Osteoarthritis (OA) is a degenerative disease frequently encountered clinically, which can lead to loss of joint function in the late stage and is associated with a high disability rate. There is still no available cure for OA. Therefore, early diagnosis and precise treatment are the key to improving the therapeutic effect. Being an interdisciplinary research focus, artificial intelligence (AI) has been increasingly used in the diagnosis and treatment of OA recently, as it improves the diagnostic accuracy as well as clinical treatment and prognosis of OA. We summarized and systematically reviewed the literature on the application of AI in the diagnosis and treatment of OA, in which the application potential in assisting imaging diagnosis, surgical treatment, progression prediction and postoperative rehabilitation of OA was indicated, yet some limitations including non-standardized data collection and unstable algorithmic system were also identified. In the future, it is expected to establish a standardized clinical sample database and continuously optimize the algorithmic model, so as to better incorporate AI technologies in the diagnosis and treatment process of OA.
