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Kalsi S, French H, Chhaya S, Madani H, Mir R, Anosova A, Dubash S. The Evolving Role of Artificial Intelligence in Radiotherapy Treatment Planning-A Literature Review. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00218-8. [PMID: 38981781 DOI: 10.1016/j.clon.2024.06.005] [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: 09/14/2023] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 07/11/2024]
Abstract
This paper examines the integration of artificial intelligence (AI) in radiotherapy for cancer treatment. The importance of radiotherapy in cancer management and its time-intensive planning process make AI adoption appealing especially with the escalating demand for radiotherapy. This review highlights the efficacy of AI across medical domains, where it surpasses human capabilities in areas such as cardiology and dermatology. Focusing on radiotherapy, the paper details AI's applications in target segmentation, dose optimization, and outcome prediction. It discusses adaptive radiotherapy's benefits and AI's potential to enhance patient outcomes with much improved treatment accuracy. The paper explores ethical concerns, including data privacy and bias, stressing the need for robust guidelines. Educating healthcare professionals and patients about AI's role is crucial as it acknowledges potential job-role changes and concerns about patients' trust in the use of AI. Overall, the integration of AI in radiotherapy holds transformative potential in streamlining processes, improving outcomes, and reducing costs. AI's potential to reduce healthcare costs underscores its significance with impactful change globally. However, successful implementation hinges on addressing ethical and logistical challenges and fostering collaboration among healthcare professionals and patient population data sets for its optimal utilization. Rigorous education, collaborative efforts, and global data sharing will be the compass guiding its' success in radiotherapy and healthcare.
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Affiliation(s)
- S Kalsi
- Lister Hospital, Stevenage, United Kingdom.
| | - H French
- University of Chester, United Kingdom
| | - S Chhaya
- New Cross Hospital, Wolverhampton, United Kingdom
| | - H Madani
- Lister Hospital, Stevenage, United Kingdom
| | - R Mir
- Mount Vernon Cancer Centre, Northwood, United Kingdom; University of Manchester, Manchester, United Kingdom
| | - A Anosova
- Mount Vernon Cancer Centre, East & North Hertfordshire NHS Trust, United Kingdom
| | - S Dubash
- Mount Vernon Cancer Centre, Northwood, United Kingdom; Department of Surgery and Cancer, Imperial College, London, United Kingdom
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2
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Zhou L, Ji Q, Peng H, Chen F, Zheng Y, Jiao Z, Gong J, Li W. Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning. Eur Radiol 2024; 34:3644-3655. [PMID: 37994966 DOI: 10.1007/s00330-023-10316-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 08/19/2023] [Accepted: 08/26/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVES To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs. METHODS Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model. RESULTS Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively. CONCLUSIONS A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs. CLINICAL RELEVANCE STATEMENT A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time. KEY POINTS • A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.
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Affiliation(s)
- Lili Zhou
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Qiang Ji
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Hong Peng
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No. 639 Zhizaoju Road, Huangpu District, Shanghai, 20011, China
| | - Feng Chen
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | - Yi Zheng
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China
| | | | - Jian Gong
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical Unversity, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
| | - Wenbin Li
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No. 119, Nansihuan West Road, Fengtai District, Beijing, 100070, China.
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3
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Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med 2024; 151:102861. [PMID: 38555850 DOI: 10.1016/j.artmed.2024.102861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks.
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Affiliation(s)
- Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University (FIU), Modesto A. Maidique Campus, 11200 S.W. 8th St, RB 261B, Miami, FL 33199, United States.
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4
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Martín-Noguerol T, López-Úbeda P, Luna A. Imagine there is no paperwork… it's easy if you try. Br J Radiol 2024; 97:744-746. [PMID: 38335929 PMCID: PMC11027242 DOI: 10.1093/bjr/tqae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/11/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.
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Affiliation(s)
| | | | - Antonio Luna
- MRI Unit, Radiology Department, HT medica, Jaén 23007, Spain
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5
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Chao CT, Hung KY. Emphasizing probabilistic reasoning education: Helping nephrology trainees to cope with uncertainty in the era of AI-assisted clinical practice. Nephrology (Carlton) 2024; 29:169-171. [PMID: 38109797 DOI: 10.1111/nep.14263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/30/2023] [Accepted: 12/03/2023] [Indexed: 12/20/2023]
Affiliation(s)
- Chia-Ter Chao
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
- Graduate Institute of Toxicology, National Taiwan University College of Medicine, Taipei, Taiwan
- Center of Faculty Development, National Taiwan University College of Medicine, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Min-Sheng General Hospital, Taoyuan City, Taiwan
| | - Kuan-Yu Hung
- Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taipei Medical University-Shuang-Ho Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan
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6
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Hassankhani A, Amoukhteh M, Valizadeh P, Jannatdoust P, Sabeghi P, Gholamrezanezhad A. Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists. Acad Radiol 2024; 31:306-321. [PMID: 37349157 DOI: 10.1016/j.acra.2023.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
RATIONALE AND OBJECTIVES Artificial intelligence (AI) is changing radiology by automating tasks and assisting in abnormality detection and understanding perceptions of medical students, radiology trainees, and radiologists is vital for preparing them for AI integration in radiology. MATERIALS AND METHODS A systematic review and meta-analysis were conducted following established guidelines. PubMed, Scopus, and Web of Science were searched up to March 5, 2023. Eligible studies reporting outcomes of interest were included, and relevant data were extracted and analyzed using STATA software version 17.0. RESULTS A meta-analysis of 21 studies revealed that 22.36% of individuals were less likely to choose radiology as a career due to concerns about advances in AI. Medical students showed higher rates of concern (31.94%) compared to radiology trainees and radiologists (9.16%) (P < .01). Radiology trainees and radiologists also demonstrated higher basic AI knowledge (71.84% vs 35.38%). Medical students had higher rates of belief that AI poses a threat to the radiology job market (42.66% vs 6.25%, P < .02). The pooled rate of respondents who believed that "AI will revolutionize radiology in the future" was 79.48%, with no significant differences based on participants' positions. The pooled rate of responders who believed in the integration of AI in medical curricula was 81.75% among radiology trainees and radiologists and 70.23% among medical students. CONCLUSION The study revealed growing concerns regarding the impact of AI in radiology, particularly among medical students, which can be addressed by revamping education, providing direct AI experience, addressing limitations, and emphasizing medico-legal issues to prepare for AI integration in radiology.
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Affiliation(s)
- Amir Hassankhani
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (A.H., M.A.).
| | - Melika Amoukhteh
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (A.H., M.A.)
| | - Parya Valizadeh
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
| | - Payam Jannatdoust
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
| | - Paniz Sabeghi
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
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7
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Guermazi A, Omoumi P, Tordjman M, Fritz J, Kijowski R, Regnard NE, Carrino J, Kahn CE, Knoll F, Rueckert D, Roemer FW, Hayashi D. How AI May Transform Musculoskeletal Imaging. Radiology 2024; 310:e230764. [PMID: 38165245 PMCID: PMC10831478 DOI: 10.1148/radiol.230764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/18/2023] [Accepted: 07/11/2023] [Indexed: 01/03/2024]
Abstract
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.
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Affiliation(s)
- Ali Guermazi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Patrick Omoumi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Mickael Tordjman
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Jan Fritz
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Richard Kijowski
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Nor-Eddine Regnard
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - John Carrino
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Charles E. Kahn
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Florian Knoll
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daniel Rueckert
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Frank W. Roemer
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daichi Hayashi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
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8
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Martín-Noguerol T, Luna A. Beyond the AJR: Artificial Intelligence Helps Radiologists to Improve Their Performance in Differentiating Colon Carcinoma From Acute Diverticulitis on CT. AJR Am J Roentgenol 2023; 221:849. [PMID: 37073898 DOI: 10.2214/ajr.23.29466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Affiliation(s)
| | - Antonio Luna
- Radiology Department, MRI Unit, HT Medica, Carmelo Torres 2, 23007 Jaén, Spain
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9
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Chekmeyan M, Baccei SJ, Garwood ER. Cross-Check QA: A Quality Assurance Workflow to Prevent Missed Diagnoses by Alerting Inadvertent Discordance Between the Radiologist and Artificial Intelligence in the Interpretation of High-Acuity CT Scans. J Am Coll Radiol 2023; 20:1225-1230. [PMID: 37423347 DOI: 10.1016/j.jacr.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE The aim of this study was to implement and evaluate a quality assurance (QA) workflow that leverages natural language processing to rapidly resolve inadvertent discordance between radiologists and an artificial intelligence (AI) decision support system (DSS) in the interpretation of high-acuity CT studies when the radiologist does not engage with AI DSS output. METHODS All consecutive high-acuity adult CT examinations performed in a health system between March 1, 2020, and September 20, 2022, were interpreted alongside an AI DSS (Aidoc) for intracranial hemorrhage, cervical spine fracture, and pulmonary embolus. CT studies were flagged for this QA workflow if they met three criteria: (1) negative results by radiologist report, (2) a high probability of positive results by the AI DSS, and (3) unviewed AI DSS output. In these cases, an automated e-mail notification was sent to our quality team. If discordance was confirmed on secondary review-an initially missed diagnosis-addendum and communication documentation was performed. RESULTS Of 111,674 high-acuity CT examinations interpreted alongside the AI DSS over this 2.5-year time period, the frequency of missed diagnoses (intracranial hemorrhage, pulmonary embolus, and cervical spine fracture) uncovered by this workflow was 0.02% (n = 26). Of 12,412 CT studies prioritized as depicting positive findings by the AI DSS, 0.4% (n = 46) were discordant, unengaged, and flagged for QA. Among these discordant cases, 57% (26 of 46) were determined to be true positives. Addendum and communication documentation was performed within 24 hours of the initial report signing in 85% of these cases. CONCLUSIONS Inadvertent discordance between radiologists and the AI DSS occurred in a small number of cases. This QA workflow leveraged natural language processing to rapidly detect, notify, and resolve these discrepancies and prevent potential missed diagnoses.
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Affiliation(s)
| | - Steven J Baccei
- Professor, Vice-Chair, Quality, Safety, and Process Improvement, and Interim Co-CMO, UMass Memorial Medical Center and Department of Radiology, UMass Chan Medical School, Worcester, Massachusetts
| | - Elisabeth R Garwood
- Assistant Professor and Director of Radiology AI and Clinical Innovation, Department of Radiology, UMass Chan Medical School, Worcester, Massachusetts
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10
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Mattay GS, Griffey RT, Narra V, Poirier RF, Bierhals A. Impact of Predictive Text Clinical Decision Support on Imaging Order Entry in the Emergency Department. J Am Coll Radiol 2023; 20:1250-1257. [PMID: 37805010 DOI: 10.1016/j.jacr.2023.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/03/2023] [Accepted: 05/09/2023] [Indexed: 10/09/2023]
Abstract
PURPOSE Imaging clinical decision support (CDS) is designed to assist providers in selecting appropriate imaging studies and is now federally required. The aim of this study was to understand the effect of CDS on decisions and workflows in the emergency department (ED). METHODS The authors' institution's order entry platform serves up structured indications for imaging orders. Imaging orders are scored by CDS on the basis of appropriate use criteria (AUC). CDS triggers alerts for imaging orders with low AUC scores. Because free text alone cannot be scored by CDS, an artificial intelligence predictive text (AIPT) module was implemented to guide the selection of structured indications when free-text indications are entered. A total of 17,355 imaging orders in the ED over 6 months were retrospectively analyzed. RESULTS CDS alerts for low AUC scores were triggered for 3% of all imaging study orders (522 of 17,355). Providers spent an average of 24 seconds interacting with alerts. In 18 of 522 imaging orders with alerts, alternative studies were ordered. After AIPT implementation, the percentage of unscored studies significantly decreased from 81% to 45% (P < .001). CONCLUSIONS In a quaternary academic ED, CDS alerts triggered by low AUC scores caused minimal increase in time spent on imaging order entry but had a relatively marginal impact on imaging study selection. AIPT implementation increased the number of scored studies and could potentially enhance CDS effects. CDS implementation enables the collection of novel data regarding which imaging studies receive low AUC scores. Future work could include exploring alternative models of CDS implementation to maximize its impact.
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Affiliation(s)
- Govind S Mattay
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri.
| | - Richard T Griffey
- Associate Chief, Emergency Medicine, Department of Emergency Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Vamsi Narra
- Senior Vice Chair, Imaging Informatics and New Business Development, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Chief, Radiology, Barnes-Jewish West County Hospital, St. Louis, Missouri; Associate Chief Medical Informatics Officer, BJC HealthCare, St. Louis, Missouri
| | - Robert F Poirier
- Associate Chief, Emergency Medicine, Department of Emergency Medicine, Washington University School of Medicine, St. Louis, Missouri; Medical Director and Chief of Clinical Operations, Emergency Medicine, Barnes-Jewish Hospital, St. Louis, Missouri
| | - Andrew Bierhals
- Vice Chair, Community Radiology, Vice Chair, Quality and Safety, Medical and Director for CT, Center for Clinical Imaging Research, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; Director of Cardiothoracic Imaging, Barnes-Jewish West County Hospital, St. Louis, Missouri. https://twitter.com/AMdmph
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11
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Shreve LA, Fried JG, Liu F, Cao Q, Pakpoor J, Kahn CE, Zafar HM. Impact of Artificial Intelligence-Assisted Indication Selection on Appropriateness Order Scoring for Imaging Clinical Decision Support. J Am Coll Radiol 2023; 20:1258-1266. [PMID: 37390881 DOI: 10.1016/j.jacr.2023.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 07/02/2023]
Abstract
PURPOSE The aim of this study was to assess appropriateness scoring and structured order entry after the implementation of an artificial intelligence (AI) tool for analysis of free-text indications. METHODS Advanced outpatient imaging orders in a multicenter health care system were recorded 7 months before (March 1, 2020, to September 21, 2020) and after (October 20, 2020, to May 13, 2021) the implementation of an AI tool targeting free-text indications. Clinical decision support score (not appropriate, may be appropriate, appropriate, or unscored) and indication type (structured, free-text, both, or none) were assessed. The χ2 and multivariate logistic regression adjusting for covariables with bootstrapping were used. RESULTS In total, 115,079 orders before and 150,950 orders after AI tool deployment were analyzed. The mean patient age was 59.3 ± 15.5 years, and 146,035 (54.9%) were women; 49.9% of orders were for CT, 38.8% for MR, 5.9% for nuclear medicine, and 5.4% for PET. After deployment, scored orders increased to 52% from 30% (P < .001). Orders with structured indications increased to 67.3% from 34.6% (P < .001). On multivariate analysis, orders were more likely to be scored after tool deployment (odds ratio [OR], 2.7, 95% CI, 2.63-2.78; P < .001). Compared with physicians, orders placed by nonphysician providers were less likely to be scored (OR, 0.80; 95% CI, 0.78-0.83; P < .001). MR (OR, 0.84; 95% CI, 0.82-0.87) and PET (OR, 0.12; 95% CI, 0.10-0.13) were less likely to be scored than CT (; P < .001). After AI tool deployment, 72,083 orders (47.8%) remained unscored, 45,186 (62.7%) with free-text-only indications. CONCLUSIONS Embedding AI assistance within imaging clinical decision support was associated with increased structured indication orders and independently predicted a higher likelihood of scored orders. However, 48% of orders remained unscored, driven by both provider behavior and infrastructure-related barriers.
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Affiliation(s)
- Lauren A Shreve
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Jessica G Fried
- Program Director, Abdominal Imaging, Associate Medical Director of Radiology Informatics, and Co-Director, Tumor Response Assessment Core, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Fang Liu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Quy Cao
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jina Pakpoor
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Charles E Kahn
- Vice Chair, Department of Radiology, and Vice Chair of Informatics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hanna M Zafar
- Vice Chair of Quality, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
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12
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Innes AL, Martinez A, Gao X, Dinh N, Hoang GL, Nguyen TBP, Vu VH, Luu THT, Le TTT, Lebrun V, Trieu VC, Tran NDB, Qin ZZ, Pham HM, Dinh VL, Nguyen BH, Truong TTH, Nguyen VC, Nguyen VN, Mai TH. Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam's District Health Facilities: An Implementation Study. Trop Med Infect Dis 2023; 8:488. [PMID: 37999607 PMCID: PMC10675130 DOI: 10.3390/tropicalmed8110488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/20/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023] Open
Abstract
In Vietnam, chest radiography (CXR) is used to refer people for GeneXpert (Xpert) testing to diagnose tuberculosis (TB), demonstrating high yield for TB but a wide range of CXR abnormality rates. In a multi-center implementation study, computer-aided detection (CAD) was integrated into facility-based TB case finding to standardize CXR interpretation. CAD integration was guided by a programmatic framework developed for routine implementation. From April through December 2022, 24,945 CXRs from TB-vulnerable populations presenting to district health facilities were evaluated. Physicians interpreted all CXRs in parallel with CAD (qXR 3.0) software, for which the selected TB threshold score was ≥0.60. At three months, there was 47.3% concordance between physician and CAD TB-presumptive CXR results, 7.8% of individuals who received CXRs were referred for Xpert testing, and 858 people diagnosed with Xpert-confirmed TB per 100,000 CXRs. This increased at nine months to 76.1% concordant physician and CAD TB-presumptive CXRs, 9.6% referred for Xpert testing, and 2112 people with Xpert-confirmed TB per 100,000 CXRs. Our programmatic CAD-CXR framework effectively supported physicians in district facilities to improve the quality of referral for diagnostic testing and increase TB detection yield. Concordance between physician and CAD CXR results improved with training and was important to optimize Xpert testing.
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Affiliation(s)
- Anh L. Innes
- FHI 360 Asia Pacific Regional Office, Bangkok 10330, Thailand
| | | | - Xiaoming Gao
- FHI 360, Durham, NC 27701, USA; (A.M.); (X.G.); (N.D.)
| | - Nhi Dinh
- FHI 360, Durham, NC 27701, USA; (A.M.); (X.G.); (N.D.)
| | - Gia Linh Hoang
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Thi Bich Phuong Nguyen
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Viet Hien Vu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Tuan Ho Thanh Luu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Thi Thu Trang Le
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Victoria Lebrun
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Van Chinh Trieu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Nghi Do Bao Tran
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Zhi Zhen Qin
- Stop TB Partnership, Grand-Saconnex, 1218 Geneva, Switzerland;
| | - Huy Minh Pham
- United States Agency for International Development/Vietnam, Hanoi 10000, Vietnam;
| | - Van Luong Dinh
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Binh Hoa Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Thi Thanh Huyen Truong
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Van Cu Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Viet Nhung Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
- Pulmonology Department, University of Medicine and Pharmacy, Vietnam National University, Hanoi 10000, Vietnam
| | - Thu Hien Mai
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
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13
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Chen Y, Wu Z, Wang P, Xie L, Yan M, Jiang M, Yang Z, Zheng J, Zhang J, Zhu J. Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. J Med Internet Res 2023; 25:e48249. [PMID: 37856181 PMCID: PMC10623237 DOI: 10.2196/48249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/07/2023] [Accepted: 09/01/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of "human intelligence" to AI. OBJECTIVE This study aims to comprehend radiologists' perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors. METHODS Between December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs. RESULTS In summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI. CONCLUSIONS This study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly.
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Affiliation(s)
- Yanhua Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Ziye Wu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Peicheng Wang
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Linbo Xie
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Mengsha Yan
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Maoqing Jiang
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jianjun Zheng
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Jingfeng Zhang
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Jiming Zhu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
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14
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Baecher H, Hoch CC, Knoedler S, Maheta BJ, Kauke-Navarro M, Safi AF, Alfertshofer M, Knoedler L. From bench to bedside - current clinical and translational challenges in fibula free flap reconstruction. Front Med (Lausanne) 2023; 10:1246690. [PMID: 37886365 PMCID: PMC10598714 DOI: 10.3389/fmed.2023.1246690] [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: 06/24/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
Fibula free flaps (FFF) represent a working horse for different reconstructive scenarios in facial surgery. While FFF were initially established for mandible reconstruction, advancements in planning for microsurgical techniques have paved the way toward a broader spectrum of indications, including maxillary defects. Essential factors to improve patient outcomes following FFF include minimal donor site morbidity, adequate bone length, and dual blood supply. Yet, persisting clinical and translational challenges hamper the effectiveness of FFF. In the preoperative phase, virtual surgical planning and artificial intelligence tools carry untapped potential, while the intraoperative role of individualized surgical templates and bioprinted prostheses remains to be summarized. Further, the integration of novel flap monitoring technologies into postoperative patient management has been subject to translational and clinical research efforts. Overall, there is a paucity of studies condensing the body of knowledge on emerging technologies and techniques in FFF surgery. Herein, we aim to review current challenges and solution possibilities in FFF. This line of research may serve as a pocket guide on cutting-edge developments and facilitate future targeted research in FFF.
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Affiliation(s)
- Helena Baecher
- Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Cosima C. Hoch
- Medical Faculty, Friedrich Schiller University Jena, Jena, Germany
| | - Samuel Knoedler
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
- Division of Plastic Surgery, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Plastic Surgery and Hand Surgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bhagvat J. Maheta
- College of Medicine, California Northstate University, Elk Grove, CA, United States
| | - Martin Kauke-Navarro
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
| | - Ali-Farid Safi
- Craniologicum, Center for Cranio-Maxillo-Facial Surgery, Bern, Switzerland
- Faculty of Medicine, University of Bern, Bern, Switzerland
| | - Michael Alfertshofer
- Division of Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Leonard Knoedler
- Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
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15
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Rao A, Kim J, Kamineni M, Pang M, Lie W, Dreyer KJ, Succi MD. Evaluating GPT as an Adjunct for Radiologic Decision Making: GPT-4 Versus GPT-3.5 in a Breast Imaging Pilot. J Am Coll Radiol 2023; 20:990-997. [PMID: 37356806 PMCID: PMC10733745 DOI: 10.1016/j.jacr.2023.05.003] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVE Despite rising popularity and performance, studies evaluating the use of large language models for clinical decision support are lacking. Here, we evaluate ChatGPT (Generative Pre-trained Transformer)-3.5 and GPT-4's (OpenAI, San Francisco, California) capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain. METHODS We compared ChatGPT's responses to the ACR Appropriateness Criteria for breast pain and breast cancer screening. Our prompt formats included an open-ended (OE) and a select all that apply (SATA) format. Scoring criteria evaluated whether proposed imaging modalities were in accordance with ACR guidelines. Three replicate entries were conducted for each prompt, and the average of these was used to determine final scores. RESULTS Both ChatGPT-3.5 and ChatGPT-4 achieved an average OE score of 1.830 (out of 2) for breast cancer screening prompts. ChatGPT-3.5 achieved a SATA average percentage correct of 88.9%, compared with ChatGPT-4's average percentage correct of 98.4% for breast cancer screening prompts. For breast pain, ChatGPT-3.5 achieved an average OE score of 1.125 (out of 2) and a SATA average percentage correct of 58.3%, as compared with an average OE score of 1.666 (out of 2) and a SATA average percentage correct of 77.7%. DISCUSSION Our results demonstrate the eventual feasibility of using large language models like ChatGPT for radiologic decision making, with the potential to improve clinical workflow and responsible use of radiology services. More use cases and greater accuracy are necessary to evaluate and implement such tools.
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Affiliation(s)
- Arya Rao
- Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare, Innovation in Operations Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - John Kim
- Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare, Innovation in Operations Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Meghana Kamineni
- Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare, Innovation in Operations Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Michael Pang
- Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare, Innovation in Operations Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Winston Lie
- Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare, Innovation in Operations Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Keith J Dreyer
- Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare, Innovation in Operations Research Center, Massachusetts General Hospital, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; and Chief Data Science Officer and Chief Imaging Information Officer for Mass General Brigham, Boston, Massachusetts
| | - Marc D Succi
- Harvard Medical School, Boston, Massachusetts; Medically Engineered Solutions in Healthcare, Innovation in Operations Research Center and Associate Chair of Innovation & Commercialization, Mass General Brigham Enterprise Radiology; Executive Director, MESH Incubator. Massachusetts General Hospital, Boston, Massachusetts; and Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
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16
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Walther F, Eberlein-Gonska M, Hoffmann RT, Schmitt J, Blum SFU. Measuring appropriateness of diagnostic imaging: a scoping review. Insights Imaging 2023; 14:62. [PMID: 37052758 PMCID: PMC10102275 DOI: 10.1186/s13244-023-01409-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 03/18/2023] [Indexed: 04/14/2023] Open
Abstract
In radiology, the justification of diagnostic imaging is a key performance indicator. To date, specific recommendations on the measurement of appropriateness in diagnostic imaging are missing. To map the study literature concerning the definition, measures, methods and data used for analyses of appropriateness in research of diagnostic imaging. We conducted a scoping review in Medline, EMBASE, Scopus and the Cochrane Central Register of Controlled Trials. Two independent reviewers undertook screening and data extraction. After screening 6021 records, we included 50 studies. National guidelines (n = 22/50) or American College of Radiology Appropriateness Criteria (n = 23/50) were used to define and rate appropriateness. 22/50 studies did not provide methodological details about the appropriateness assessment. The included studies varied concerning modality, amount of reviewed examinations (88-13,941) and body regions. Computed tomography (27 studies, 27,168 examinations) was the most frequently analyzed modality, followed by magnetic resonance imaging (17 studies, 6559 examinations) and radiography (10 studies, 7095 examinations). Heterogeneous appropriateness rates throughout single studies (0-100%), modalities, and body regions (17-95%) were found. Research on pediatric and outpatient imaging was sparse. Multicentric, methodologically robust and indication-oriented studies would strengthen appropriateness research in diagnostic imaging and help to develop reliable key performance indicators.
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Affiliation(s)
- Felix Walther
- Center for Evidence-Based Healthcare, University Hospital Carl Gustav Carus and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.
- Quality and Medical Risk Management, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
| | - Maria Eberlein-Gonska
- Quality and Medical Risk Management, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ralf-Thorsten Hoffmann
- Institute and Polyclinic for Diagnostic and Interventional Radiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Jochen Schmitt
- Center for Evidence-Based Healthcare, University Hospital Carl Gustav Carus and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Sophia F U Blum
- Quality and Medical Risk Management, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Institute and Polyclinic for Diagnostic and Interventional Radiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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17
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Pierre K, Haneberg AG, Kwak S, Peters KR, Hochhegger B, Sananmuang T, Tunlayadechanont P, Tighe PJ, Mancuso A, Forghani R. Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Semin Roentgenol 2023; 58:158-169. [PMID: 37087136 DOI: 10.1053/j.ro.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 04/24/2023]
Abstract
There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Adam G Haneberg
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Sean Kwak
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL
| | - Keith R Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Thiparom Sananmuang
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Padcha Tunlayadechanont
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Patrick J Tighe
- Departments of Anesthesiology & Orthopaedic Surgery, University of Florida College of Medicine, Gainesville, FL
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
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18
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Perchik JD, Rothenberg SA, Tridandapani S, Perchik LM, Smith AD. Artificial Intelligence in Body Imaging: An Overview of Commercially Available Tools. Semin Roentgenol 2023; 58:196-202. [PMID: 37087140 DOI: 10.1053/j.ro.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 02/18/2023] [Accepted: 02/23/2023] [Indexed: 03/29/2023]
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19
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Rao A, Kim J, Kamineni M, Pang M, Lie W, Succi MD. Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.02.23285399. [PMID: 36798292 PMCID: PMC9934725 DOI: 10.1101/2023.02.02.23285399] [Citation(s) in RCA: 66] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
BACKGROUND ChatGPT, a popular new large language model (LLM) built by OpenAI, has shown impressive performance in a number of specialized applications. Despite the rising popularity and performance of AI, studies evaluating the use of LLMs for clinical decision support are lacking. PURPOSE To evaluate ChatGPT's capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain. MATERIALS AND METHODS We compared ChatGPT's responses to the American College of Radiology (ACR) Appropriateness Criteria for breast pain and breast cancer screening. Our prompt formats included an open-ended (OE) format, where ChatGPT was asked to provide the single most appropriate imaging procedure, and a select all that apply (SATA) format, where ChatGPT was given a list of imaging modalities to assess. Scoring criteria evaluated whether proposed imaging modalities were in accordance with ACR guidelines. RESULTS ChatGPT achieved an average OE score of 1.83 (out of 2) and a SATA average percentage correct of 88.9% for breast cancer screening prompts, and an average OE score of 1.125 (out of 2) and a SATA average percentage correct of 58.3% for breast pain prompts. CONCLUSION Our results demonstrate the feasibility of using ChatGPT for radiologic decision making, with the potential to improve clinical workflow and responsible use of radiology services.
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20
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Mahajan A, Chakrabarty N. Editorial: The use of deep learning in mapping and diagnosis of cancers. Front Oncol 2022; 12:1077341. [PMID: 36582789 PMCID: PMC9793849 DOI: 10.3389/fonc.2022.1077341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 11/22/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Liverpool, Liverpool, United Kingdom,*Correspondence: Abhishek Mahajan,
| | - Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute (HBNI), Mumbai, India
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21
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Daye D, Wiggins WF, Lungren MP, Alkasab T, Kottler N, Allen B, Roth CJ, Bizzo BC, Durniak K, Brink JA, Larson DB, Dreyer KJ, Langlotz CP. Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? Radiology 2022; 305:555-563. [PMID: 35916673 PMCID: PMC9713445 DOI: 10.1148/radiol.212151] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 03/30/2022] [Accepted: 04/12/2022] [Indexed: 01/03/2023]
Abstract
As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.
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Affiliation(s)
- Dania Daye
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Walter F. Wiggins
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Matthew P. Lungren
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Tarik Alkasab
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Nina Kottler
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Bibb Allen
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Christopher J. Roth
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Bernardo C. Bizzo
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - Kimberly Durniak
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - James A. Brink
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
| | - David B. Larson
- From the Department of Radiology, Massachusetts General Hospital,
Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A.,
B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham,
NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford,
Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.);
and Department of Radiology, Grandview Medical Center, Birmingham, Ala
(B.A.)
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Rushlow DR, Croghan IT, Inselman JW, Thacher TD, Friedman PA, Yao X, Pellikka PA, Lopez-Jimenez F, Bernard ME, Barry BA, Attia IZ, Misra A, Foss RM, Molling PE, Rosas SL, Noseworthy PA. Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. Mayo Clin Proc 2022; 97:2076-2085. [PMID: 36333015 DOI: 10.1016/j.mayocp.2022.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/09/2022] [Accepted: 04/04/2022] [Indexed: 03/19/2023]
Abstract
OBJECTIVE To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting patients with low EF. METHODS Clinicians in 48 practice sites of a US Midwest health system were cluster-randomized by the care team to usual care or to receive a notification that suggested ordering an echocardiogram in patients flagged as potentially having low EF based on an AI-ECG algorithm. Enrollment was between June 26, 2019, and July 30, 2019; participation concluded on March 31, 2020. This report is focused on those clinicians randomized to receive the notification of the AI-ECG algorithm. At the patient level, data were analyzed for the proportion of patients with positive AI-ECG results. Adoption was defined as the clinician order of an echocardiogram after prompted by the alert. RESULTS A total of 165 clinicians and 11,573 patients were included in this analysis. Among patients with positive AI-ECG, high adopters (n=41) were twice as likely to diagnose patients with low EF (33.9%) vs low adopters, n=124, (16.9%); odds ratio, 1.62; 95% CI, 1.21 to 2.17). High adopters were more often advanced practice providers (eg, nurse practitioners and physician assistants) vs physicians, Family Medicine vs Internal Medicine specialty, and tended to have less complex patients. CONCLUSION Clinicians who most frequently followed the recommendations of an AI tool were twice as likely to diagnose low EF. Those clinicians with less complex patients were more likely to be high adopters. TRIAL REGISTRATION Clinicaltrials.gov Identifier: NCT04000087.
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Affiliation(s)
- David R Rushlow
- Department of Family Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Ivana T Croghan
- Department of Medicine, Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Jonathan W Inselman
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Tom D Thacher
- Department of Family Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Barbara A Barry
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Itzhak Z Attia
- Department of Cardiology, Mayo Clinic, Rochester, MN, USA
| | - Artika Misra
- Department of Family Medicine, Mayo Clinic Health System, Mankato, MN, USA
| | - Randy M Foss
- Department of Family Medicine, Mayo Clinic Health System, Lake City, MN, USA
| | - Paul E Molling
- Department of Family Medicine, Mayo Clinic Health System, Onalaska, WI, USA
| | - Steven L Rosas
- Department of Family Medicine, Mayo Clinic Health System, Menomonie, WI, USA
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23
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Artificial intelligence and machine learning in cancer imaging. COMMUNICATIONS MEDICINE 2022; 2:133. [PMID: 36310650 PMCID: PMC9613681 DOI: 10.1038/s43856-022-00199-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
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Al-Dasuqi K, Johnson MH, Cavallo JJ. Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities. Clin Imaging 2022; 89:61-67. [PMID: 35716432 DOI: 10.1016/j.clinimag.2022.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/04/2022] [Accepted: 05/23/2022] [Indexed: 11/16/2022]
Abstract
The value of artificial intelligence (AI) in healthcare has become evident, especially in the field of medical imaging. The accelerated pace and acuity of care in the Emergency Department (ED) has made it a popular target for artificial intelligence-driven solutions. Software that helps better detect, report, and appropriately guide management can ensure high quality patient care while enabling emergency radiologists to better meet the demands of quick turnaround times. Beyond diagnostic applications, AI-based algorithms also have the potential to optimize other important steps within the ED imaging workflow. This review will highlight the different types of AI-based applications currently available for use in the ED, as well as the challenges and opportunities associated with their implementation.
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Affiliation(s)
- Khalid Al-Dasuqi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America.
| | - Michele H Johnson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America.
| | - Joseph J Cavallo
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America.
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25
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Macchia G, Ferrandina G, Patarnello S, Autorino R, Masciocchi C, Pisapia V, Calvani C, Iacomini C, Cesario A, Boldrini L, Gui B, Rufini V, Gambacorta MA, Scambia G, Valentini V. Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept. Front Oncol 2022; 11:797454. [PMID: 35047408 PMCID: PMC8761664 DOI: 10.3389/fonc.2021.797454] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/08/2021] [Indexed: 11/29/2022] Open
Abstract
Aim The first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician’s attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation. Patients and Method Data from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography–Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient’s data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed. Results In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient’s staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way. Conclusion This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.
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Affiliation(s)
- Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Gabriella Ferrandina
- Department of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.,Department of Woman, Child and Public Health, Catholic University of the Sacred Heart, Rome, Italy
| | - Stefano Patarnello
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Rosa Autorino
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Carlotta Masciocchi
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vincenzo Pisapia
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Cristina Calvani
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Chiara Iacomini
- Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Alfredo Cesario
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Luca Boldrini
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Benedetta Gui
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vittoria Rufini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Maria Antonietta Gambacorta
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Giovanni Scambia
- Department of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy.,Department of Woman, Child and Public Health, Catholic University of the Sacred Heart, Rome, Italy
| | - Vincenzo Valentini
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
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26
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de Ga K, Noblett D, Bateni C. Ankle MRI and preceding radiographs: an evaluation of physician ordering practices. Skeletal Radiol 2022; 51:2263-2268. [PMID: 35666294 PMCID: PMC9560948 DOI: 10.1007/s00256-022-04084-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Multiple guidelines have been published for appropriate imaging in patients with ankle-related symptoms which suggest radiographs as the initial imaging examination for both acute and chronic ankle abnormalities. Few studies have evaluated adherence to these imaging guidelines. This study retrospectively evaluated the utilization of ankle MRI and preceding radiographs based on ordering provider group and MRI indication. MATERIALS AND METHODS A total of 4186 ankle MRIs performed over a 9-year period at a single institution were evaluated for the presence of preceding ankle and/or foot radiographs at two time points, within 3 months and within 6 months of the MRI examination. Ankle MRIs were then categorized based on 6 ordering provider groups and 13 MRI indications. RESULTS Of the 4186 MRIs evaluated, 68% had preceding radiographs within 3 months and 77% had radiographs within 6 months. Primary care, sports medicine, and podiatry had the lowest rates of preceding radiographs (73%, 68%, and 64%, respectively, within 6 months). Eighty-six percent of ankle MRIs ordered by orthopedic surgery had preceding radiographs within 6 months and 89% of ankle MRIs ordered by emergency medicine and inpatient providers had preceding radiographs. MRIs intended for evaluation of Achilles tendon or plantar fascia abnormalities were among the least likely indications to have preceding radiographs. CONCLUSION Based on established clinical guidelines, there was a lower-than-expected rate of obtaining preceding radiographs for ankle MRIs among most provider groups, particularly non-orthopedic outpatient providers. Additional research is needed to address the lack of adherence to clinical imaging guidelines and ensure appropriate imaging.
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Affiliation(s)
- Kristopher de Ga
- Department of Radiology, University of California Davis Medical Center, 4860 Y Street, Suite 3100, Sacramento, CA 95817 USA
| | - Dylan Noblett
- Department of Radiology, University of California Davis Medical Center, 4860 Y Street, Suite 3100, Sacramento, CA 95817 USA
| | - Cyrus Bateni
- Department of Radiology, University of California Davis Medical Center, 4860 Y Street, Suite 3100, Sacramento, CA 95817 USA
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27
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Ranschaert E, Topff L, Pianykh O. Optimization of Radiology Workflow with Artificial Intelligence. Radiol Clin North Am 2021; 59:955-966. [PMID: 34689880 DOI: 10.1016/j.rcl.2021.06.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The potential of artificial intelligence (AI) in radiology goes far beyond image analysis. AI can be used to optimize all steps of the radiology workflow by supporting a variety of nondiagnostic tasks, including order entry support, patient scheduling, resource allocation, and improving the radiologist's workflow. This article discusses several principal directions of using AI algorithms to improve radiological operations and workflow management, with the intention of providing a broader understanding of the value of applying AI in the radiology department.
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Affiliation(s)
- Erik Ranschaert
- Elisabeth-Tweesteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands; Ghent University, C. Heymanslaan 10, 9000 Gent, Belgium.
| | - Laurens Topff
- Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Oleg Pianykh
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 25 New Chardon Street, Suite 470, Boston, MA 02114, USA
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28
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Gish DS, Ellenbogen AL, Patrie JT, Gaskin CM. Retrospective Evaluation of Artificial Intelligence Leveraging Free-Text Imaging Order Entry to Facilitate Federally Required Clinical Decision Support. J Am Coll Radiol 2021; 18:1476-1484. [PMID: 34600896 DOI: 10.1016/j.jacr.2021.08.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 08/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE The Protecting Access to Medicare Act mandates clinical decision support (CDS) at imaging order entry, necessitating the use of structured indications to map CDS scores. We evaluated the performance of a commercially available artificial intelligence (AI) tool leveraging free-text order entry to facilitate provider selection of the necessary structured indications. METHODS Our institution implemented an AI tool offering predicted structured indications based upon the ordering provider's entry of a free-text reason for examination. Providers remained able to order via the traditional direct search for structured indications. Alternatively, they could take the new free-text-AI approach allowing them to select from AI-predicted indications, perform additional direct searches, indicate no matching indication, or exit CDS workflow. We hypothesized the free-text-AI approach would be elected more often and the AI tool would be successful in facilitating selection of structured indications. We reviewed advanced imaging orders (n = 40,053) for the first 3 months (February to May 2020) since implementation. RESULTS Providers were more likely (P < .001) to choose the free-text-AI approach (23,580; 58.9%) to order entry over direct search for structured indications (16,473; 41.1%). The AI tool yielded alerts with predicted indications in 91.7% (n = 21,631) of orders with free text. Ultimately, providers chose AI-predicted indications in 57.7% (n = 12,490) of cases in which they were offered by the tool. DISCUSSION Providers significantly more often elected the new free-text-AI approach to order entry for CDS, suggesting provider preference over the traditional approach. The AI tool commonly predicted indications acceptable to ordering providers.
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Affiliation(s)
- David S Gish
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia
| | - Amy L Ellenbogen
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia
| | - James T Patrie
- Department of Public Health Sciences, University of Virginia Health System, Charlottesville, Virginia
| | - Cree M Gaskin
- Vice-Chair of Clinical Operations and Informatics, Division Chief of Musculoskeletal Imaging and Intervention, and Associate Chief Medical Information Officer, Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville Virginia.
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29
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Stetson PD, Cantor MN, Gonen M. When Predictive Models Collide. JCO Clin Cancer Inform 2021; 4:547-550. [PMID: 32543898 DOI: 10.1200/cci.20.00024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Peter D Stetson
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Michael N Cantor
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Mithat Gonen
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
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30
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Bizzo BC, Almeida RR, Alkasab TK. Computer-Assisted Reporting and Decision Support in Standardized Radiology Reporting for Cancer Imaging. JCO Clin Cancer Inform 2021; 5:426-434. [PMID: 33852324 DOI: 10.1200/cci.20.00129] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Recent advances in structured reporting are providing an opportunity to enhance cancer imaging assessment to drive value-based care and improve patient safety. METHODS The computer-assisted reporting and decision support (CAR/DS) framework has been developed to enable systematic ingestion of guidelines as clinical decision structured reporting tools embedded within the radiologist's workflow. RESULTS CAR/DS tools can reduce the radiology reporting variability and increase compliance with clinical guidelines. The lung cancer use-case is used to describe various scenarios of a cancer imaging structured reporting pathway, including incidental findings, screening, staging, and restaging or continued care. Various aspects of these tools are also described using cancer-related examples for different imaging modalities and applications such as calculators. Such systems can leverage artificial intelligence (AI) algorithms to assist with the generation of structured reports and there are opportunities for new AI applications to be created using the structured data associated with CAR/DS tools. CONCLUSION These AI-enabled systems are starting to allow information from multiple sources to be integrated and inserted into structured reports to drive improvements in clinical decision support and patient care.
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Affiliation(s)
- Bernardo C Bizzo
- Harvard Medical School, Boston, MA.,Department of Radiology, Massachusetts General Hospital, Boston, MA.,Department of Radiology, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Renata R Almeida
- Harvard Medical School, Boston, MA.,Department of Radiology, Brigham and Women's Hospital, Boston, MA
| | - Tarik K Alkasab
- Harvard Medical School, Boston, MA.,Department of Radiology, Massachusetts General Hospital, Boston, MA
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31
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Kapoor N, Lacson R, Khorasani R. Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools. J Am Coll Radiol 2021; 17:1363-1370. [PMID: 33153540 DOI: 10.1016/j.jacr.2020.08.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/21/2020] [Accepted: 08/25/2020] [Indexed: 12/18/2022]
Abstract
In the past decade, there has been tremendous interest in applying artificial intelligence (AI) to improve the field of radiology. Currently, numerous AI applications are in development, with potential benefits spanning all steps of the imaging chain from test ordering to report communication. AI has been proposed as a means to optimize patient scheduling, improve worklist management, enhance image acquisition, and help radiologists interpret diagnostic studies. Although the potential for AI in radiology appears almost endless, the field is still in the early stages, with many uses still theoretical, in development, or limited to single institutions. Moreover, although the current use of AI in radiology has emphasized its clinical applications, some of which are in the distant future, it is increasingly clear that AI algorithms could also be used in the more immediate future for a variety of noninterpretive and quality improvement uses. Such uses include the integration of AI into electronic health record systems to reduce unwarranted variation in radiologists' follow-up recommendations and to improve other dimensions of radiology report quality. In the end, the potential of AI in radiology must be balanced with acknowledgment of its current limitations regarding generalizability and data privacy.
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Affiliation(s)
- Neena Kapoor
- Director of Diversity, Inclusion, and Equity, Department of Radiology, Brigham and Women's Hospital; Quality and Patient Safety Officer, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
| | - Ronilda Lacson
- Director of Education, Center for Evidence-Based Imaging, Brigham and Women's Hospital; Director of Clinical Informatics, Harvard Medical School Library of Evidence, Boston, Massachusetts
| | - Ramin Khorasani
- Director of the Center of Evidence Imaging and Vice Chair of Quality/Safety, Department of Radiology, Center for Evidence Based Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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32
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Burns J, Ciccarelli S, Mardakhaev E, Erdfarb A, Goldberg-Stein S, Bello JA. Handoffs in Radiology: Minimizing Communication Errors and Improving Care Transitions. J Am Coll Radiol 2021; 18:1297-1309. [PMID: 33989534 DOI: 10.1016/j.jacr.2021.04.007] [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: 01/01/2021] [Revised: 03/13/2021] [Accepted: 04/06/2021] [Indexed: 11/30/2022]
Abstract
Handoffs are essential to achieving safe care transitions. In radiology practice, frequent transitions of care responsibility among clinicians, radiologists, and patients occur between moments of care such as determining protocol, imaging, interpreting, and consulting. Continuity of care is maintained across these transitions with handoffs, which are the process of communicating patient information and transferring decision-making responsibility. As a leading cause of medical error, handoffs are a major communication challenge that is exceedingly common in both diagnostic and interventional radiology practice. The frequency of handoffs in radiology underscores the importance of using evidence-based strategies to improve patient safety in the radiology department. In this article, reliability science principles and handoff improvement tools are adapted to provide radiology-focused strategies at individual, team, and organizational levels with the goal of minimizing handoff errors and improving care transitions.
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Affiliation(s)
- Judah Burns
- Chair, Montefiore Medical Center Peer Review Board; Program Director, Montefiore Medical Center Diagnostic Radiology Residency Program; Department of Radiology, Montefiore Medical Center, Bronx, New York.
| | | | | | - Amichai Erdfarb
- Director of Quality and Safety, Department of Radiology, Montefiore Medical Center, Bronx, New York
| | - Shlomit Goldberg-Stein
- Director of Operational Improvement, Department of Radiology, Montefiore Medical Center, Bronx, New York
| | - Jacqueline A Bello
- Vice Chair, Board of Chancellors, American College of Radiology; Section Chief of Neuroradiology, Montefiore Medical Center; Department of Radiology, Montefiore Medical Center, Bronx, New York
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Chua IS, Gaziel-Yablowitz M, Korach ZT, Kehl KL, Levitan NA, Arriaga YE, Jackson GP, Bates DW, Hassett M. Artificial intelligence in oncology: Path to implementation. Cancer Med 2021; 10:4138-4149. [PMID: 33960708 PMCID: PMC8209596 DOI: 10.1002/cam4.3935] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high‐value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.
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Affiliation(s)
- Isaac S Chua
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Michal Gaziel-Yablowitz
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Zfania T Korach
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Kenneth L Kehl
- Harvard Medical School, Boston, MA, USA.,Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Gretchen P Jackson
- IBM Watson Health, Cambridge, MA, USA.,Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David W Bates
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Michael Hassett
- Harvard Medical School, Boston, MA, USA.,Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
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34
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Artificial intelligence in child abuse imaging. Pediatr Radiol 2021; 51:1061-1064. [PMID: 33904953 DOI: 10.1007/s00247-021-05073-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 03/08/2021] [Accepted: 03/24/2021] [Indexed: 12/22/2022]
Abstract
There have been rapid advances in artificial intelligence (AI) technology in recent years, and the field of diagnostic imaging is no exception. Just as digital technology revolutionized how radiology is practiced, so these new technologies also appear poised to bring sweeping change. As AI tools make the transition from the theoretical to the everyday, important decisions need to be made about how they will be applied and what their role will be in the practice of radiology. Pediatric radiology presents distinct challenges and opportunities for the application of these tools, and in this article we discuss some of these, specifically as they relate to the prediction, identification and investigation of child abuse.
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Shen L, Wright A, Lee LS, Jajoo K, Nayor J, Landman A. Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy. J Am Med Inform Assoc 2021; 28:95-103. [PMID: 33175157 DOI: 10.1093/jamia/ocaa250] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/22/2020] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy. MATERIALS AND METHODS The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods. RESULTS 22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54). DISCUSSION At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors. CONCLUSION A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.
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Affiliation(s)
- Lin Shen
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Adam Wright
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Linda S Lee
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Kunal Jajoo
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer Nayor
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Department of Gastroenterology, Emerson Hospital, Concord, Massachusetts, USA
| | - Adam Landman
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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36
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La integración de la inteligencia artificial en el abordaje clínico del paciente: enfoque en la imagen cardiaca. Rev Esp Cardiol 2021. [DOI: 10.1016/j.recesp.2020.07.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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37
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Artificial Intelligence and the Trainee Experience in Radiology. J Am Coll Radiol 2020; 17:1388-1393. [DOI: 10.1016/j.jacr.2020.09.028] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 12/23/2022]
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Bhatia N, Trivedi H, Safdar N, Heilbrun ME. Artificial Intelligence in Quality Improvement: Reviewing Uses of Artificial Intelligence in Noninterpretative Processes from Clinical Decision Support to Education and Feedback. J Am Coll Radiol 2020; 17:1382-1387. [DOI: 10.1016/j.jacr.2020.08.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 08/07/2020] [Indexed: 02/07/2023]
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Letterie G, Mac Donald A. Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertil Steril 2020; 114:1026-1031. [PMID: 33012555 DOI: 10.1016/j.fertnstert.2020.06.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/30/2020] [Accepted: 06/04/2020] [Indexed: 01/15/2023]
Abstract
OBJECTIVE To describe a computer algorithm designed for in vitro fertilization (IVF) management and to assess the algorithm's accuracy in the day-to-day decision making during ovarian stimulation for IVF when compared to evidence-based decisions by the clinical team. DESIGN Descriptive and comparative study of new technology. SETTING Private fertility practice. INTERVENTION(S) None. PATIENT(S) Data were derived from monitoring during ovarian stimulation from IVF cycles. The database consisted of 2,603 cycles (1,853 autologous and 750 donor cycles) incorporating 7,376 visits for training. An additional 556 unique cycles were used for challenge and to calculate accuracy. There were 59,706 data points. Input variables included estradiol concentrations in picograms per milliliter; ultrasound measurements of follicle diameters in two dimensions in millimeters; cycle day during stimulation and dose of recombinant follicle-stimulating hormone during ovarian stimulation for IVF. MAIN OUTCOME MEASURE(S) Accuracy of the algorithm to predict four critical clinical decisions during ovarian stimulation for IVF: [1] stop stimulation or continue stimulation. If the decision was to stop, then the next automated decision was to [2] trigger or cancel. If the decision was to return, then the next key decisions were [3] number of days to follow-up and [4] whether any dosage adjustment was needed. RESULT(S) Algorithm accuracies for these four decisions are as follows: continue or stop treatment: 0.92; trigger and schedule oocyte retrieval or cancel cycle: 0.96; dose of medication adjustment: 0.82; and number of days to follow-up: 0.87. These accuracies are for first iteration of the algorithm. CONCLUSION(S) We describe a first iteration of a predictive analytic algorithm that is highly accurate and in agreement with evidence-based decisions by expert teams during ovarian stimulation during IVF. These tools offer a potential platform to optimize clinical decision making during IVF.
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Loncaric F, Camara O, Piella G, Bijnens B. Integration of artificial intelligence into clinical patient management: focus on cardiac imaging. ACTA ACUST UNITED AC 2020; 74:72-80. [PMID: 32819849 DOI: 10.1016/j.rec.2020.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 07/01/2020] [Indexed: 10/23/2022]
Abstract
Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve-through automation, standardization and data integration-all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management-with a focus on cardiac imaging, but applicable to all information handling-and to discuss current barriers that remain to be overcome before its widespread implementation and integration.
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Affiliation(s)
- Filip Loncaric
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - Oscar Camara
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; ICREA, Barcelona, Spain
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Madurai Elavarasan R, Pugazhendhi R. Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 725:138858. [PMID: 32336562 PMCID: PMC7180041 DOI: 10.1016/j.scitotenv.2020.138858] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 04/18/2020] [Accepted: 04/19/2020] [Indexed: 04/15/2023]
Abstract
The emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in China at December 2019 had led to a global outbreak of coronavirus disease 2019 (COVID-19) and the disease started to spread all over the world and became an international public health issue. The entire humanity has to fight in this war against the unexpected and each and every individual role is important. Healthcare system is doing exceptional work and the government is taking various measures that help the society to control the spread. Public, on the other hand, coordinates with the policies and act accordingly in most state of affairs. But the role of technologies in assisting different social bodies to fight against the pandemic remains hidden. The intention of our study is to uncover the hidden roles of technologies that ultimately help for controlling the pandemic. On investigating, it is found that the strategies utilizing potential technologies would yield better benefits and these technological strategies can be framed either to control the pandemic or to support the confinement of the society during pandemic which in turn aids in controlling the spreading of infection. This study enlightens the various implemented technologies that assists the healthcare systems, government and public in diverse aspects for fighting against COVID-19. Furthermore, the technological swift that happened during the pandemic and their influence in the environment and society is discussed. Besides the implemented technologies, this work also deals with untapped potential technologies that have prospective applications in controlling the pandemic circumstances. Alongside the various discussion, our suggested solution for certain situational issues is also presented.
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Affiliation(s)
- Rajvikram Madurai Elavarasan
- Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India.
| | - Rishi Pugazhendhi
- Department of Mechanical Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India
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Allen B, Cook TS, Bello JA. Quality and Data Science. J Am Coll Radiol 2019; 16:1237-1238. [DOI: 10.1016/j.jacr.2019.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 06/03/2019] [Indexed: 10/26/2022]
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