Clinical & Experimental Cardiology
Open Access
ISSN: 2155-9880
+44 1300 500008
ISSN: 2155-9880
+44 1300 500008
Fangling Tu
Chongqing University Three Gorges Hospital, China
Scientific Tracks Abstracts: J Clin Exp Cardiolog
Objective: Cardiovascular diseases (cvds) remain the leading cause of death globally, posing significant challenges to healthcare systems. The objective of this review is to explore how artificial intelligence (AI) can optimize the prediction, diagnosis, and treatment of cvds, thereby improving patient outcomes and enhancing healthcare efficiency. Methods and Materials: This review synthesizes findings from various studies and applications of AI in the field of cardiovascular medicine. AI technologies discussed include machine learning algorithms, deep learning networks, and predictive analytics applied to patient data. The methods focus on AI's capabilities in processing large datasets, recognizing patterns, and making predictions that surpass traditional statistical approaches. Results: AI has demonstrated significant advancements in the field of cardiovascular health: Prediction: AI models have accurately predicted acute myocardial infarction with a sensitivity of over 90%. For instance, using features from electronic health records and ECG data, AI algorithms have reduced false negatives by 30% compared to traditional models. Diagnosis: In diagnosing cvds through imaging, AI has achieved accuracy levels exceeding 95% when interpreting echocardiograms and MRI scans. Automated systems have reduced diagnostic times from hours to minutes, crucially speeding up the intervention process. Treatment: In treatment applications, AI-driven robotic surgeries have shown a 20% improvement in patient recovery times and a 15% reduction in complications. AI has also been instrumental in developing personalized medication plans, increasing the efficacy of treatments by tailoring them to individual genetic profiles. Conclusion: AI has profoundly impacted the field of cardiovascular medicine, offering substantial improvements over conventional methods in prediction, diagnosis, and treatment. The data-driven insights provided by AI not only enhance the accuracy and efficiency of cardiovascular disease diagnosis but also pave the way for personalized and predictive healthcare solutions. Continued integration of AI into cardiovascular disease diagnosis promises to further revolutionize this vital field, making it more adaptive, efficient, and patient-focused.
Dr. Fangling Tu is a prominent healthcare professional at Chongqing University Three Gorges Hospital in China. With a background in advanced clinical practice and medical research, Dr. Tu has made significant contributions to patient care, medical education, and clinical research. Specializing in specific field if known, e.g., cardiology, oncology, etc. Dr. Tu is dedicated to integrating innovative practices and treatments that elevate healthcare quality and accessibility for patients in the region.