Bankstown Hospital - Grand Rounds - Further Reading

Welcome to the Grand Rounds Further Reading List, Cardiology edition, brought to you by the Clinical Library, on Level 4, next to the Auditorium.

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AI in cardiology: love at first byte

Agrawal, A. (2021). "Interventional Pulmonology: Diagnostic and Therapeutic Advances in Bronchoscopy." American Journal of Therapeutics 28(2): e204-e216 https://journals.lww.com/americantherapeutics/fulltext/2021/04000/interventional_pulmonology__diagnostic_and.6.aspx REQUEST ARTICLE

                Background: Interventional pulmonology is a rapidly evolving subspecialty of pulmonary medicine that offers advanced consultative and procedural services to patients with airway diseases, pleural diseases, as well as in the diagnosis and management of patients with thoracic malignancy. Areas of Uncertainty: The institution of lung cancer screening modalities as well as the search of additional minimally invasive diagnostic and treatment modalities for lung cancer and other chronic lung diseases has led to an increased focus on the field of interventional pulmonology. Rapid advancements in the field over the last 2 decades has led to development of various new minimally invasive bronchoscopic approaches and techniques for patients with cancer as well as for patients with chronic lung diseases. Data Sources: A review of literature was performed using PubMed database to identify all articles published up till October 2020 relevant to the field of interventional pulmonology and bronchoscopy. The reference list of each article was searched to look for additional articles, and all relevant articles were included in the article. Therapeutic Advances: Newer technologies are now available such navigation platforms to diagnose and possibly treat peripheral pulmonary nodules, endobronchial ultrasound in diagnosis of mediastinal and hilar adenopathy as well as cryobiopsy in the diagnosis of diffuse lung diseases. In addition, flexible and rigid bronchoscopy continues to provide new and expanding ability to manage patients with benign and malignant central airway obstruction. Interventions are also available for diseases such as asthma, chronic bronchitis, chronic obstructive pulmonary disease, and emphysema that were traditionally treated with medical management alone. Conclusions: With continued high quality research and an increasing body of evidence, interventional bronchoscopy has enormous potential to provide both safe and effective options for patients with a variety of lung diseases.

Boonstra, M. J., et al. (2024). "Artificial intelligence: revolutionizing cardiology with large language models." European Heart Journal 45(5): 332-345 https://doi.org/10.1093/eurheartj/ehad838 REQUEST ARTICLE

                Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.

Deo, R. C. (2024). "Artificial Intelligence and Machine Learning in Cardiology." Circulation 149(16): 1235-1237 https://www.ahajournals.org/doi/abs/10.1161/CIRCULATIONAHA.123.065469 

REQUEST ARTICLE

Seven years ago, I wrote a review in this journal on the state of machine learning in medicine.¹ My tenet then was that although numerous medical applications could benefit from machine learning, and the requisites for such models—data and algorithms—were widely present, few examples had made their way into practice. I struggled to find illustrative examples from cardiovascular research, let alone commercial products. Since that time, much has changed. Artificial intelligence (AI), a broader term that includes machine learning as a subdiscipline, dominates research publications and the news. And with innovation comes uncertainty, such as concerns over which professions (physicians included) will be made redundant by these innovations. It is thus timely to revisit this topic, offering some perspective on the dizzying pace of innovation.

Gala, D., et al. (2024). "The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature." Healthcare 12(4): 481 https://www.mdpi.com/2227-9032/12/4/481  REQUEST ARTICLE

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Madaudo, C., et al. (2024). "Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice." Journal of Cardiovascular Medicine 25(11): 766-771 https://journals.lww.com/jcardiovascularmedicine/fulltext/2024/11000/artificial_intelligence_in_cardiology__a_peek_at.2.aspx  REQUEST ARTICLE

Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in healthcare is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. ChatGPT could be exploited in the medical field to improve patient education and information, thus increasing compliance. ChatGPT could facilitate information exchange on major cardiovascular diseases, provide clinical decision support, and improve patient communication and education. It could assist the clinician in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, it is unclear whether it will be possible to use ChatGPT for the management of patients who require rapid decisions. Indeed, many drawbacks are associated with the daily use of these technologies in the medical field, such as insufficient expertise in specialized fields and a lack of comprehension of the context in which it works. The pros and cons of its use have been explored in this review, which was not written with the help of ChatGPT.

Oke, O. A. and N. Cavus (2025). "A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology." International Journal of Medical Informatics 195: 105753 https://www.sciencedirect.com/science/article/pii/S1386505624004167 REQUEST ARTICLE

Background Artificial intelligence (AI) has revolutionized numerous industries, enhancing efficiency, scalability, and insight generation. In cardiology, particularly through electrocardiogram (ECG) analysis, AI has the potential to improve diagnostic accuracy and reduce the time needed for diagnosis. This systematic review explores the integration of AI, machine learning (ML), and deep learning (DL) in ECG analysis, focusing on their impact on predictive diagnostics and treatment support in cardiology. Methods A systematic literature review was conducted following the PRISMA 2020 framework, using four high-impact databases to identify studies from 2014 to -2024. The inclusion criteria included English-language journal articles and research papers that focused on AI applications in cardiology, specifically ECG analysis. Records were screened, duplicates were removed, and final selections were made on the basis of their relevance to AI-ECG integration for cardiac health. Results The review included 46 studies that met the inclusion criteria, covering diverse AI models such as CNNs, RNNs, and hybrid models. These models were applied to ECG data to detect and predict heart conditions such as arrhythmia, myocardial infarction, and heart failure. These findings indicate that AI-driven ECG analysis improves diagnostic accuracy and provides significant support for early diagnosis and personalized treatment. Conclusions

Olawade, D. B., et al. (2024). "Advancements and applications of Artificial Intelligence in cardiology: Current trends and future prospects." Journal of Medicine, Surgery, and Public Health 3: 100109 https://www.sciencedirect.com/science/article/pii/S2949916X24000628  REQUEST ARTICLE

                Using Artificial intelligence technologies in cardiology has witnessed rapid advancements across various domains, fostering innovation and reshaping clinical practices. The study aims to provide a comprehensive overview of these AI-driven advancements and their implications for enhancing cardiovascular healthcare. A systematic approach was adopted to conduct an extensive review of scholarly articles and peer-reviewed literature focusing on the application of AI in cardiology. Databases including PubMed/MEDLINE, ScienceDirect, IEEE Xplore, and Web of Science were systematically searched. Articles were screened following a defined selection criteria. These articles' synthesis highlighted AI's diverse applications in cardiology, including but not limited to diagnostic innovations, precision medicine, remote monitoring technologies, drug discovery, and clinical decision support systems. The review shows the significant role of AI in reshaping cardiovascular medicine by revolutionising diagnostics, treatment strategies, and patient care. The diverse applications of AI in cardiology showcased in this study reflect the transformative potential of these technologies. However, challenges such as algorithm accuracy, interoperability, and integration into clinical workflows persist. AI's continued advancements and strategic integration in cardiology promise to deliver more personalised, efficient, and effective cardiovascular care, ultimately improving patient outcomes and shaping the future of cardiology practice.

Patrascanu, O. S., et al. (2024). "Future Horizons: The Potential Role of Artificial Intelligence in Cardiology." Journal of Personalized Medicine 14(6): 656 https://www.mdpi.com/2075-4426/14/6/656 REQUEST ARTICLE

Using Artificial intelligence technologies in cardiology has witnessed rapid advancements across various domains, fostering innovation and reshaping clinical practices. The study aims to provide a comprehensive overview of these AI-driven advancements and their implications for enhancing cardiovascular healthcare. A systematic approach was adopted to conduct an extensive review of scholarly articles and peer-reviewed literature focusing on the application of AI in cardiology. Databases including PubMed/MEDLINE, ScienceDirect, IEEE Xplore, and Web of Science were systematically searched. Articles were screened following a defined selection criteria. These articles' synthesis highlighted AI's diverse applications in cardiology, including but not limited to diagnostic innovations, precision medicine, remote monitoring technologies, drug discovery, and clinical decision support systems. The review shows the significant role of AI in reshaping cardiovascular medicine by revolutionising diagnostics, treatment strategies, and patient care. The diverse applications of AI in cardiology showcased in this study reflect the transformative potential of these technologies. However, challenges such as algorithm accuracy, interoperability, and integration into clinical workflows persist. AI's continued advancements and strategic integration in cardiology promise to deliver more personalised, efficient, and effective cardiovascular care, ultimately improving patient outcomes and shaping the future of cardiology practice.

• Therapeutic Guidelines: Cardiovascular
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• CVD Calculator