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Al-Ani MA, Bai C, Hashky A, Parker AM, Vilaro JR, Aranda JM, Shickel B, Rashidi P, Bihorac A, Ahmed MM, Mardini MT. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review. Front Cardiovasc Med 2023; 10:1127716. [PMID: 36910520 PMCID: PMC9999024 DOI: 10.3389/fcvm.2023.1127716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/07/2023] [Indexed: 03/14/2023] Open
Abstract
Introduction Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence. Methods We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines. Results Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities. Conclusion Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.
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Affiliation(s)
- Mohammad A Al-Ani
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Chen Bai
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Amal Hashky
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States
| | - Alex M Parker
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan R Vilaro
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan M Aranda
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States.,Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Mustafa M Ahmed
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Mamoun T Mardini
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
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Hu Z, Hu R, Yau O, Teng M, Wang P, Hu G, Singla R. Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint. JMIR Med Inform 2022; 10:e34304. [PMID: 35969464 PMCID: PMC9425164 DOI: 10.2196/34304] [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: 10/16/2021] [Revised: 07/29/2022] [Accepted: 08/02/2022] [Indexed: 12/02/2022] Open
Abstract
The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.
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Affiliation(s)
- Zoe Hu
- School of Medicine, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Medicine, Queen's University, Kingston, ON, Canada.,School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Olivia Yau
- School of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Minnie Teng
- School of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Patrick Wang
- School of Medicine, Queen's University, Kingston, ON, Canada
| | - Grace Hu
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Rohit Singla
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.,School of Medicine, University of British Columbia, Vancouver, BC, Canada
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Rellum SR, Schuurmans J, van der Ven WH, Eberl S, Driessen AHG, Vlaar APJ, Veelo DP. Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review. J Thorac Dis 2022; 13:6976-6993. [PMID: 35070381 PMCID: PMC8743411 DOI: 10.21037/jtd-21-765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 08/27/2021] [Indexed: 12/27/2022]
Abstract
Background Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. Methods We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. Results Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. Conclusions ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.
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Affiliation(s)
- Santino R Rellum
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Jaap Schuurmans
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Ward H van der Ven
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Susanne Eberl
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Antoine H G Driessen
- Department of Cardiothoracic Surgery, Heart Center, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Alexander P J Vlaar
- Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Denise P Veelo
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
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McInerney CD, Scott BC, Johnson OA. Are Regulations Safe? Reflections From Developing a Digital Cancer Decision-Support Tool. JCO Clin Cancer Inform 2021; 5:353-363. [PMID: 33797951 PMCID: PMC8140795 DOI: 10.1200/cci.20.00148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/23/2020] [Accepted: 01/22/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE Informatics solutions to early diagnosis of cancer in primary care are increasingly prevalent, but it is not clear whether existing and planned standards and regulations sufficiently address patients' safety nor whether these standards are fit for purpose. We use a patient safety perspective to reflect on the development of a computerized cancer risk assessment tool embedded within a UK primary care electronic health record system. METHODS We developed a computerized version of the CAncer Prevention in ExetER studies risk assessment tool, in compliance with the European Union's Medical Device Regulations. The process of building this tool afforded an opportunity to reflect on clinical concerns and whether current regulations for medical devices are fit for purpose. We identified concerns for patient safety and developed nine practical recommendations to mitigate these concerns. RESULTS We noted that medical device regulations (1) were initially created for hardware devices rather than software, (2) offer one-shot approval rather than supporting iterative innovation and learning, (3) are biased toward loss-transfer approaches that attempt to manage the fallout of harm instead of mitigating hazards becoming harmful, and (4) are biased toward known hazards, despite unknown hazards being an expected consequence of health care as a complex adaptive system. Our nine recommendations focus on embedding less-reductionist and stronger system perspectives into regulations and standards. CONCLUSION Our intention is to share our experience to support research-led collaborative development of health informatics solutions in cancer. We argue that regulations in the European Union do not sufficiently address the complexity of healthcare information systems with consequences for patient safety. Future standards and regulations should continue to follow a system-based approach to risk, safety, and accident avoidance.
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Affiliation(s)
| | | | - Owen A. Johnson
- School of Computing, University of Leeds, Leeds, United Kingdom
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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