Artificial Intelligence, Brain and Computer in Cardiocerebrovascular Medicine

Artificial Intelligence, Brain and Computer in Cardiocerebrovascular Medicine | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 August 2025 V1 Latest version Share on Artificial Intelligence, Brain and Computer in Cardiocerebrovascular Medicine Author : Chunsong Hu 0000-0002-0590-3909 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175648078.81053796/v1 252 views 131 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This article briefly discusses the crucial roles of computers and artificial intelligence (AI) in current human life, particularly related clinical applications in cardio-cerebrovascular medicine, as well as the associations with human brain. Despite the risks and challenges, as classical AI technologies, deep learning models (e.g., ChatGPT and DeepSeek) and human-computer interaction have currently enormous development potential, great application value, and bright prospects in precision clinical medicine. No doubt, with the wide application of AI in the biomedical field, especially in clinical medicine, and the rapid development of AI technologies, it’s about time to establish AI cardiocerebrovascular medicine – an emerging frontier discipline, so as to better server human cardiovascular and cerebrovascular health. MINI REVIEW ARTICLE Artificial Intelligence, Brain and Computer in Cardiocerebrovascular Medicine Running title: AiBC in CCV Medicine Author : Chunsong Hu, M.D., Ph.D. Affiliation : From the Department of Cardiovascular Medicine, Nanchang University, Hospital of Nanchang University, Jiangxi Academy of Medical Science, No. 461 Bayi Ave, Nanchang 330006, Jiangxi, China. Tel: (+86) 189 70816800; Email: [email protected] or [email protected] ; https://orcid.org/0000-0002-0590-3909 Word count: 1,636 (Main text) + 112 (Abstract) References: 75 Table: 0 Figure: 1 Total pages: 16 The author reports no conflicts of interest. Submission Declaration Statement: The manuscript is original, with no portion under simultaneous consideration for publication elsewhere or previously published, and the authors are responsible for the contents and have read and approved the manuscript for submission. Corresponding author: Chunsong Hu, M.D., Ph.D., Associate Professor of Medicine, Department of Cardiovascular Medicine, Jiangxi Academy of Medical Science, Hospital of Nanchang University, Nanchang University, No. 461 Bayi Ave, Nanchang 330006, Jiangxi, China. Tel: (+86) 189 70816800; Email: [email protected] or [email protected] Abstract This article briefly discusses the crucial roles of computers and artificial intelligence (AI) in current human life, particularly related clinical applications in cardio-cerebrovascular medicine, as well as the associations with human brain. Despite the risks and challenges, as classical AI technologies, deep learning models (e.g., ChatGPT and DeepSeek) and human-computer interaction have currently enormous development potential, great application value, and bright prospects in precision clinical medicine. No doubt, with the wide application of AI in the biomedical field, especially in clinical medicine, and the rapid development of AI technologies, it’s about time to establish AI cardiocerebrovascular medicine – an emerging frontier discipline, so as to better server human cardiovascular and cerebrovascular health. Keywords: Artificial intelligence; Biological computer; Cardiocerebrovascular disease; Deep learning system or model; Human brain Highlights • There are more and more AI technology and application in clinical practice. • Deep learning models have great potential and value for human health. • AI technology shows bright prospects in clinical application. • It’s time to establish AI cardiocerebrovascular medicine - an emerging frontier discipline with great significance. 1 | INTRODUCTION Last year, the Nobel Prizes in both Physics and Chemistry were awarded to artificial intelligence (AI) related work. With the rapid development of technology, it’s believed that AI will bring a new round of industrial and health revolution. In fact, AI may improve diagnosis [1], cancer treatment [2] and patient care [3] in clinical settings, it’s more accuracy for genetic diagnosis [4] and effective in clinical decision-making [5]. I still remember during my postgraduate studies, Professor Wang had a famous saying in computer courses: “Computers are smart fools”, which left a deep impression on me. The fact is indeed true. At present, as the most popular representative of AI, it is the computer commonly used by people, which has been deeply integrated into human daily learning, work, and life. The biggest difference between current computers and the human brain is that computers perform everything according to pre-set programs, are the most “principled”, and do not understand “flexibility and adaptability” like the human brain. Moreover, working on a computer in reality is often constrained by many objective factors. For example, once there is a malfunction or power outage, the system will crash, and as a user’s doctor, sometimes we have to pause our diagnosis and treatment work. However, patients do not choose the time when they get sick, and they will not be able to avoid getting sick due to computer malfunctions or system crashes. So, sometimes it can be awkward. 2 | AI TECHNOLOGY AND AI MEDICINE IN CLINICAL PRACTICE In fact, there are wide application for AI in biomedicine. Due to the human-AI interaction, AI will assist editors and peer reviewers and completeness for clinical trial protocols [6], and generate text for scientific writing [7]. And multimodal AI in medical diagnostics [8], for example, Generative Pre-trained Transformer 4 with Vision (GPT-4V) [9], indicates higher accuracy than the average human, but AI may also presents flawed rationales in cases. All in all, although there are coexistence of benefits, limits, and challenges of GPT-4 for m edicine, there is almost no harm and the most good with AI in health care. Currently, AI technology is widely used in clinical practice. For example, AI-based ocular image analysis for predicting cardiovascular disease (CVD) [10], risk factors, and symptomatic events; for evaluation of individual-level risk of CVD, including hypertension, coronary artery disease, heart failure, stroke and vascular dementia [11]. And promising developments and applications of AI solutions for analysis of plaque characterization (e.g., high-risk plaque or vulnerable plaque) and cardiovascular events [12], and development of machine learning models may help to predict 10-year risk of CVD so as to make earlier clinical decisions for potential high-risk [13]. Herein, the interaction of AI, human brain and computer will help to develop a novel cardiovascular and cerebrovascular medicine (Fig. 1). FIGURE 1 AI CCV MED. Since the rapid development of artificial intelligence (AI) in the biomedical field, particularly the interaction of AI, brain and computer, it will greatly promote the establishment of an emerging frontier discipline– AI cardio-cerebrovascular medicine (CCV MED). Here, Br: brain; Co: computer. In the field of biomedical engineering, AI has a wide range of application prospects. For example, a customizable soft robotic aortic sleeve [14] may improve the management and treatment of patients with aortic stenosis. A deep learning system may help to assess and predict CVD risk via retinal fundus photographs [15] and the measurement of retinal-vessel calibre [16]. In fact, this technology and AI solution can not only detect left ventricular structural abnormalities with chest X-rays [17], but also accurately predict left atrial appendage thrombus [18], so as to guide the decision to perform transoesophageal echocardiography despite chronic oral anticoagulation. 3 | DEEP LEARNING MODELS HAVE GREAT POTENTIAL AND VITAL VALUE FOR HUMAN HEALTH In fact, machine or deep learning models (DLM) is widely used in clinical practice for the diagnosis of various circulation system diseases or CVD [19]. Usually, DLM of electrocardiographic images is helpful to detection, evaluation, and screening of arrhythmia (such as atrial fibrillation [20,21] and long QT syndrome [22,23]), structural heart d isease including hypertrophic cardiomyopathy [24-26], and hyperkalemia [27]. Cardiovascular DLM analysis is also helpful to prediction, assessment, and identification or detection of congenital heart d isease [28], left ventricular hypertrophy [29], cardiometabolic disease prevalence [30], heart failure with reduced ejection fraction [31], ischemic stroke lesions [32], and diabetic retinopathy [33-35]. Currently, DLM is also used for fully automated, scalable analysis of millions of echocardiogram [36], left ventricular systolic dysfunction [37], accurate classifying mitral regurgitation (MR) severity and refine assessments of MR [38], prediction of clinical outcomes of out-of-hospital cardiac arrest [39] and improvement of prehospital resuscitation [40], screening and staging of Moyamoya disease, a rare and complex pathological condition [41], replacing conventional cardiac magnetic resonance (CMR) scans by virtual native enhancement without the need for contrast administration, and faster and cheaper [42], getting better performance for coronary CT angiography [43], increase the accuracy and efficiency of quantitative coronary angiography [44], and assisting decision-making for endovascular treatment in acute ischemic stroke [45]. Moreover, a deep learning solution for analysis of echocardiographic videos [46] may improve predictions of all-cause mortality. And machine learning for t he computational-experimental screening is helpful in the identification of mitophagy modulators and control of Alzheimer’s disease [47] . A deep learning algorithm [48] as a promising tool combining with coronary artery calcium for screening cardiovascular risk has a potential beyond traditional risk prediction and may help earlier intervention. It is also a powerful tool for the development of tailored and personalized treatment strategies of cardiovascular stents for angioplasty [49], and predicting CVD/stroke risk in Parkinson’s disease patients with COVID-19 infection [50]. In addition, an AI-based system for automatic M-mode echocardiographic analysis [51] has excellent accuracy, reproducibility, and speed, and may improve efficiency and reduce variability in clinical practice. Since DLM of CMR imaging [52] can reconstruct high-quality images from highly under-sampled k-space data, multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data has been constructed in a dataset. Moreover, there are new biomedical tools for brain and neuroscience research, such as Neurodesk platform (https://www.neurodesk.org/) [53] for accessible, flexible, portable and fully reproducible neuroimaging analysis, and a virtual reality-trained DLM [54] for whole-brain imaging data in health and disease, for example, cancer-related brain activity. In fact, rationalized DLM of super-resolution microscopy may improve imaging information of rapid subcellular processes [55]. And specific DLM have higher efficiencies for prediction of base editors [56]. 4 | AI TECHNOLOGY SHOWS BRIGHT PROSPECTS IN CLINICAL APPLICATIONS At present, AI represented by computers is increasingly “bundled” with people’s learning, work, and life. This is not only the advancement of technology, but also brings new challenges and uncertainties. And some people have concerns about the future development of AI. For example, AI may lead to more people becoming “unemployed” and other uncertainties in the future. Although all of these things are possible, people should be prepared but not “give up eating because of choking”. With the development of a combination of artificial neuromorphic systems with biological neural networks [57], self-organizing nanowire networks 58 and large-area neural interfaces [59], there will be more artificial neural networks implementation in biomedical interfaces including prosthetics and brain-machine interfaces. Currently, there are increasing rapidly with the incorporation of AI approaches for DNA synthesis and computational protein design [60]. However, this technology is vulnerable to misuse and the production of dangerous biological agents. Herein, we should pay more attention to AI related bio-safety and bio-security [61], for example, ensure AI protein design to go in a safe, secure, and trustworthy manner [60]. Herein, people should recognize the advantages of AI while avoiding its weaknesses, so that the significant advantages of AI can be fully utilized. 5 | CONCLUSIONS AND FUTURE PERSPECTIVES More and more popular brain-machine interaction is the future development direction of clinical medicine, including the cardiovascular field, with broad application prospects. For example, remote robotic surgery for CVD, and applications of brain-machine interface in neurological diseases. At the same time, we need to be cautious in dealing with potential risks and challenges. And even if there are big breakthroughs in AI and their enormous potentials in biomedicine, particularly precision cardiovascular medicine [62,63], we should pay more attention to its transparency and reproducibility [64]. No doubt, the advent of digital health and AI technology has promised to revolutionize clinical care, there are still the practical, ethical, and regulatory challenges [65] that may limit the wide applications of these technologies. Whatever, the current data and AI revolutions will lead to a novel paradigm of clinical medicine and traditional practices [66]. In fact, as powerful bioinformatics and computational tools, AI and DLM have been used for protein modeling, drug screening, and vaccine design [67], as well as host-microbe interactions [68], antigen discovery and genetic associations [69], and antibody research [70]. Despite the risks and challenges, as classical AI technologies, DLM including large language models (LLM), e.g., ChatGPT and DeepSeek, and human-computer interaction have currently enormous development potential, high application value, and bright prospects in precision clinical medicine. Particularly, as an open-source LLM, the DeepSeek [71,72] has better effects on medical tasks, clinical reasoning, and other transforming medical applications. For example, based on healthy diets of traditional Chinese medicine [73] and natural marine products [74], and a new scoring system of CVD prevention and human longevity [75], people can develop a more practical and powerful computing tool of anti-aging and life expectancy. It is no exaggeration to say that a new discipline on AI cardiovascular or cerebrovascular medicine has already been born with the help of LLM. Chunsong Hu contributed to conceptualization, data analysis, methodology, resources, visualization, writing-original draft, and writing-review & editing. The author read and approved the final version of this manuscript. The author did not receive any funding or material support and gratefully acknowledges the reviewers and editors for their critical reviews. The author declared no competing interests for this work. 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Marine natural products and human immunity: novel biomedical resources for anti-infection of SARS-CoV-2 and related cardiovascular disease. Nat Prod Bioprospect 2024;14:12.[75] Hu C. Prevention of cardiovascular disease for healthy aging and longevity: A new scoring system and related “mechanisms-hallmarks-biomarkers”. Ageing Res Rev 2025;107:102727. Information & Authors Information Version history V1 Version 1 29 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Chunsong Hu 0000-0002-0590-3909 [email protected] Nanchang University View all articles by this author Metrics & Citations Metrics Article Usage 252 views 131 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Chunsong Hu. Artificial Intelligence, Brain and Computer in Cardiocerebrovascular Medicine. Authorea . 29 August 2025. 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