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Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.

ABSTRACT: Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

SUBMITTER: Wang S

PROVIDER: S-EPMC8733656 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveIntegrated therapy of traditional Chinese medicine (TCM) and Western medicine (WM) has gradually been applied to the treatment of rheumatoid arthritis (RA). Recently published studies have provided a wealth of data and information about the effectiveness of combination treatments, but high-quality evidence-based meta-analysis on this issue is not available yet. This study was conducted to compare and evaluate the efficacy and safety of the integrated therapy for RA.MethodsPubMed, EMBASE, and the Cochrane Library were searched up to January 2020. Randomized controlled trials (RCTs) that compared the efficacy and safety of integrative TCM-WM with WM alone for RA were included. The outcome measures contained therapeutic effects (TEs), tender joint count (TJC), swollen joint count (SJC), duration of morning stiffness (DMS), grip strength (GS), disease activity score in 28 joints (DAS28), rheumatoid factor (RF), anti-cyclic peptide containing citrulline (anti-CCP), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and adverse events (AEs) to assess the efficacy and safety of different treatments.ResultsA total of 20 RCTs with 2269 patients met the inclusion criteria. TCM used in these studies included Chinese herbal decoctions and tablets or capsules made from herbs and their extracts, while WM included disease-modifying antirheumatic drugs (DMARDs), nonsteroidal anti-inflammatory drugs (NSAIDs), and glucocorticoids (GC). Compared with patients receiving WM treatment alone, patients with integrative TCM-WM treatment showed better TEs (OR = 3.03, 95% CI [2.36, 3.88]). The integrative treatment group showed reductions in TJC (MD = -1.17, 95% CI [-2.12, -0.21]), SJC (MD = -0.87, 95% CI [-1.85, 0.10]), DMS (SMD = -0.69, 95% CI [-0.98, -0.41]), DAS28 (MD = -0.43, 95% CI [-0.57, -0.29]), RF (SMD = -0.59, 95% CI [-0.91, -0.27]), anti-CCP (SMD = -0.21, 95% CI [-0.36, -0.06]), ESR (MD = -8.36, 95% CI [-12.60, -4.12]), and CRP (MD = -6.73, 95% CI [-9.38, -4.08]), and increment in GS (SMD = 0.12, 95% CI [-0.63, 0.87]). AEs, especially gastrointestinal disorders, abnormal liver function, leukopenia, skin allergies and rashes, headaches and dizziness, and alopecia, significantly decreased (OR = 0.37, 95% CI [0.29, 0.47]) in the integrative treatment group.ConclusionsThe findings of this meta-analysis indicate that integrative TCM-WM could obtain effective and safe results in the treatment of RA. Using TCM as an adjunctive therapy in RA has great prospects for further development.

Project description:Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.

Project description:BackgroundThis review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first introduced in the 1950s, is defined as "the machine simulation of human mental reasoning, decision making, and behavior". The increased power of computing, expansion of storage capacity, and compilation of medical big data helped the AI implementation surge in medical practice and research. Ophthalmology is a leading medical specialty in applying AI in screening, diagnosis, and treatment. The first Food and Drug Administration approved autonomous diagnostic system served to diagnose and classify diabetic retinopathy. Other ophthalmic conditions such as age-related macular degeneration, glaucoma, retinopathy of prematurity, and congenital cataract, among others, implemented AI too.PurposeTo review the contemporary literature of the bioethical issues of AI in medicine and ophthalmology, classify ethical issues in medical AI, and suggest possible standardizations of ethical frameworks for AI implementation.MethodsKeywords were searched on Google Scholar and PubMed between October 2019 and April 2020. The results were reviewed, cross-referenced, and summarized. A total of 284 references including articles, books, book chapters, and regulatory reports and statements were reviewed, and those that were relevant were cited in the paper.ResultsMost sources that studied the use of AI in medicine explored the ethical aspects. Bioethical challenges of AI implementation in medicine were categorized into 6 main categories. These include machine training ethics, machine accuracy ethics, patient-related ethics, physician-related ethics, shared ethics, and roles of regulators.ConclusionsThere are multiple stakeholders in the ethical issues surrounding AI in medicine and ophthalmology. Attention to the various aspects of ethics related to AI is important especially with the expanding use of AI. Solutions of ethical problems are envisioned to be multifactorial.

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Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.

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