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Wee NK, Git KA, Lee WJ, Raval G, Pattokhov A, Ho ELM, Chuapetcharasopon C, Tomiyama N, Ng KH, Tan CH. Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology. Korean J Radiol 2024; 25:603-612. [PMID: 38942454 PMCID: PMC11214917 DOI: 10.3348/kjr.2024.0419] [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: 04/30/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 06/30/2024] Open
Abstract
Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.
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Affiliation(s)
- Nicole Kessa Wee
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Kim-Ann Git
- Department of Diagnostic Radiology, Pantai Hospital, Kuala Lumpur, Malaysia
| | - Wen-Jeng Lee
- Department of Diagnostic Radiology, National Taiwan University Hospital, Taipei, Taiwan
| | - Gaurang Raval
- Department of Diagnostic Radiology, Workhardt Hospitals Limited, Mumbai, India
| | - Aziz Pattokhov
- Faculty of Medicine, Tashkent State Dental Institute, Tashkent, Uzbekistan
| | - Evelyn Lai Ming Ho
- Department of Diagnostic Radiology, ParkCity Medical Centre, Kuala Lumpur, Malaysia
| | | | - Noriyuki Tomiyama
- Department of Diagnostic and Interventional Radiology Suita, Osaka University Hospital, Osaka, Japan
| | - Kwan Hoong Ng
- Department of Biomedical Imaging and University of Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia
- Faculty of Medicine and Health Sciences, UCSI University Springhill Campus, Port Dickson, Malaysia
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
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Wang Y, Guo Y, Wang Z, Yu L, Yan Y, Gu Z. Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation. PLoS One 2024; 19:e0299623. [PMID: 38913621 PMCID: PMC11195943 DOI: 10.1371/journal.pone.0299623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/08/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures. METHOD This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle. RESULTS The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. The algorithm for performing ps-KDE is available at https://github.com/wyc79/ps-KDE. DISCUSSION Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analyses such as disease classification and risk stratification.
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Affiliation(s)
- Yuanchen Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yujie Guo
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ziqi Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Linzi Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yujie Yan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Zifan Gu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
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Tripathi S, Dako F. The Potential of Large Language Models for Radiology Report Simplification and Translations. J Am Coll Radiol 2024:S1546-1440(24)00524-6. [PMID: 38880292 DOI: 10.1016/j.jacr.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/05/2024] [Accepted: 06/12/2024] [Indexed: 06/18/2024]
Affiliation(s)
- Satvik Tripathi
- Student Researcher, Center for Global and Population Health Research in Radiology, Department of Radiology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pennsylvania
| | - Farouk Dako
- Director, Center for Global and Population Health Research in Radiology, Department of Radiology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pennsylvania.
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Daye D, Parker R, Tripathi S, Cox M, Brito Orama S, Valentin L, Bridge CP, Uppot RN. CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology. Cancers (Basel) 2024; 16:1975. [PMID: 38893096 PMCID: PMC11171258 DOI: 10.3390/cancers16111975] [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: 05/06/2024] [Revised: 05/18/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm's performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model's predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.
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Affiliation(s)
- Dania Daye
- Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (M.C.); (L.V.); (C.P.B.); (R.N.U.)
- Harvard Medical School, Boston, MA 02115, USA;
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
| | | | - Satvik Tripathi
- Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (M.C.); (L.V.); (C.P.B.); (R.N.U.)
- Harvard Medical School, Boston, MA 02115, USA;
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
| | - Meredith Cox
- Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (M.C.); (L.V.); (C.P.B.); (R.N.U.)
- Harvard Medical School, Boston, MA 02115, USA;
| | | | - Leonardo Valentin
- Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (M.C.); (L.V.); (C.P.B.); (R.N.U.)
- Professional Hospital Guaynabo, Guaynabo 00971, Puerto Rico
| | - Christopher P. Bridge
- Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (M.C.); (L.V.); (C.P.B.); (R.N.U.)
- Harvard Medical School, Boston, MA 02115, USA;
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
| | - Raul N. Uppot
- Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (M.C.); (L.V.); (C.P.B.); (R.N.U.)
- Harvard Medical School, Boston, MA 02115, USA;
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Tripathi S, Sukumaran R, Cook TS. Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care. J Am Med Inform Assoc 2024; 31:1436-1440. [PMID: 38273739 PMCID: PMC11105142 DOI: 10.1093/jamia/ocad258] [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: 08/14/2023] [Revised: 12/01/2023] [Accepted: 12/29/2023] [Indexed: 01/27/2024] Open
Abstract
PURPOSE This article explores the potential of large language models (LLMs) to automate administrative tasks in healthcare, alleviating the burden on clinicians caused by electronic medical records. POTENTIAL LLMs offer opportunities in clinical documentation, prior authorization, patient education, and access to care. They can personalize patient scheduling, improve documentation accuracy, streamline insurance prior authorization, increase patient engagement, and address barriers to healthcare access. CAUTION However, integrating LLMs requires careful attention to security and privacy concerns, protecting patient data, and complying with regulations like the Health Insurance Portability and Accountability Act (HIPAA). It is crucial to acknowledge that LLMs should supplement, not replace, the human connection and care provided by healthcare professionals. CONCLUSION By prudently utilizing LLMs alongside human expertise, healthcare organizations can improve patient care and outcomes. Implementation should be approached with caution and consideration to ensure the safe and effective use of LLMs in the clinical setting.
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Affiliation(s)
- Satvik Tripathi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Rithvik Sukumaran
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Tessa S Cook
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Katwaroo AR, Adesh VS, Lowtan A, Umakanthan S. The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgrad Med J 2024; 100:289-296. [PMID: 38159301 DOI: 10.1093/postmj/qgad135] [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: 10/27/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.
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Affiliation(s)
- Arun Rabindra Katwaroo
- Department of Medicine, Trinidad Institute of Medical Technology, St Augustine, Trinidad and Tobago
| | | | - Amrita Lowtan
- Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Srikanth Umakanthan
- Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
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Treat RM, Hsiao SK, Ismail A, Javan R. The US Government's Latest Presidential Executive Order on Artificial Intelligence: Potential Implications in Radiology. J Am Coll Radiol 2024:S1546-1440(24)00355-7. [PMID: 38599359 DOI: 10.1016/j.jacr.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/28/2024] [Accepted: 04/04/2024] [Indexed: 04/12/2024]
Affiliation(s)
| | - Sabrina Kelly Hsiao
- George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Ahmed Ismail
- George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Ramin Javan
- Associate Professor of Radiology; Director of Advanced Brain Imaging, GenAI and 3D Innovations Lab; and Medical Student Radiology Clerkship Director, Department of Radiology, George Washington University Hospital, Washington, DC.
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Weeks WB, Rizk RC, Rowe SP, Fishman EK, Chu LC. Unraveled: Prescriptions to Repair a Broken Health System. J Am Coll Radiol 2024:S1546-1440(24)00131-5. [PMID: 38295920 DOI: 10.1016/j.jacr.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 01/27/2024] [Indexed: 03/09/2024]
Affiliation(s)
- William B Weeks
- Director of the AI for Health Research at Microsoft AI for Good Research Lab, Redmond, Washington
| | - Ryan C Rizk
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Steven P Rowe
- Division Chief of Molecular Imaging and Therapeutics, Department of Radiology, University of North Carolina, Chapel Hill, North Carolina
| | - Elliot K Fishman
- Director of Body CT, Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | - Linda C Chu
- Director of Body MRI and Associate Division Director of the Diagnostic Division, Department of Radiology, Johns Hopkins University, Baltimore, Maryland.
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Tripathi S, Tabari A, Mansur A, Dabbara H, Bridge CP, Daye D. From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer. Diagnostics (Basel) 2024; 14:174. [PMID: 38248051 PMCID: PMC10814554 DOI: 10.3390/diagnostics14020174] [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: 09/19/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.
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Affiliation(s)
- Satvik Tripathi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Arian Mansur
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Harika Dabbara
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
| | - Christopher P. Bridge
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
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Cook TS, Elahi A. Artificial Intelligence Has Its Foot on the Gas Pedal-What It Means for Imaging. J Am Coll Radiol 2023; 20:823-824. [PMID: 37467872 DOI: 10.1016/j.jacr.2023.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 06/14/2023] [Indexed: 07/21/2023]
Affiliation(s)
- Tessa S Cook
- Department of Information Services, Penn Medicine, Philadelphia, Pennsylvania.
| | - Ameena Elahi
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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