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Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
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.
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Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
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Bekbolatova M, Mayer J, Ong CW, Toma M. Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel) 2024; 12:125. [PMID: 38255014 PMCID: PMC10815906 DOI: 10.3390/healthcare12020125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.
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Affiliation(s)
- Molly Bekbolatova
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Jonathan Mayer
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Chi Wei Ong
- School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Milan Toma
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
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Frassanito L, Di Bidino R, Vassalli F, Michnacs K, Giuri PP, Zanfini BA, Catarci S, Filetici N, Sonnino C, Cicchetti A, Arcuri G, Draisci G. Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost-Benefit Derivatives of Digital Medical Devices. J Pers Med 2023; 14:58. [PMID: 38248759 PMCID: PMC10820080 DOI: 10.3390/jpm14010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/22/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Intraoperative hypotension is associated with increased perioperative complications, hospital length of stay (LOS) and healthcare expenditure in gynecologic surgery. We tested the hypothesis that the adoption of a machine learning-based warning algorithm (hypotension prediction index-HPI) might yield an economic advantage, with a reduction in adverse outcomes that outweighs the costs for its implementation as a medical device. METHODS A retrospective-matched cohort cost-benefit Italian study in gynecologic surgery was conducted. Sixty-six female patients treated with standard goal-directed therapy (GDT) were matched in a 2:1 ratio with thirty-three patients treated with HPI based on ASA status, diagnosis, procedure, surgical duration and age. RESULTS The most relevant contributor to medical costs was operating room occupation (46%), followed by hospital stay (30%) and medical devices (15%). Patients in the HPI group had EURO 300 greater outlay for medical devices without major differences in total costs (GDT 5425 (3505, 8127), HPI 5227 (4201, 7023) p = 0.697). A pre-specified subgroup analysis of 50% of patients undergoing laparotomic surgery showed similar medical device costs and total costs, with a non-significant saving of EUR 1000 in the HPI group (GDT 8005 (5961, 9679), HPI 7023 (5227, 11,438), p = 0.945). The hospital LOS and intensive care unit stay were similar in the cohorts and subgroups. CONCLUSIONS Implementation of HPI is associated with a scenario of cost neutrality, with possible economic advantage in high-risk settings.
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Affiliation(s)
- Luciano Frassanito
- Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy; (P.P.G.); (B.A.Z.); (S.C.); (N.F.); (C.S.); (G.D.)
| | - Rossella Di Bidino
- Department of Health Technology, IRCCS Fondazione Policlinico A. Gemelli, 00168 Rome, Italy; (R.D.B.); (G.A.)
| | - Francesco Vassalli
- Department of Critical Care and Perinatal Medicine, IRCCS Istituto G. Gaslini, 16147 Genoa, Italy;
| | | | - Pietro Paolo Giuri
- Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy; (P.P.G.); (B.A.Z.); (S.C.); (N.F.); (C.S.); (G.D.)
| | - Bruno Antonio Zanfini
- Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy; (P.P.G.); (B.A.Z.); (S.C.); (N.F.); (C.S.); (G.D.)
| | - Stefano Catarci
- Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy; (P.P.G.); (B.A.Z.); (S.C.); (N.F.); (C.S.); (G.D.)
| | - Nicoletta Filetici
- Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy; (P.P.G.); (B.A.Z.); (S.C.); (N.F.); (C.S.); (G.D.)
| | - Chiara Sonnino
- Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy; (P.P.G.); (B.A.Z.); (S.C.); (N.F.); (C.S.); (G.D.)
| | - Americo Cicchetti
- Department of Management Studies, Faculty of Economics, Catholic University of Sacred Heart, 00168 Rome, Italy;
| | - Giovanni Arcuri
- Department of Health Technology, IRCCS Fondazione Policlinico A. Gemelli, 00168 Rome, Italy; (R.D.B.); (G.A.)
| | - Gaetano Draisci
- Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy; (P.P.G.); (B.A.Z.); (S.C.); (N.F.); (C.S.); (G.D.)
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