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Melnyk O, Ismail A, Ghorashi NS, Heekin M, Javan R. Generative Artificial Intelligence Terminology: A Primer for Clinicians and Medical Researchers. Cureus 2023; 15:e49890. [PMID: 38174178 PMCID: PMC10762565 DOI: 10.7759/cureus.49890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
Generative artificial intelligence (AI) is rapidly transforming the medical field, as advanced tools powered by large language models (LLMs) make their way into clinical practice, research, and education. Chatbots, which can generate human-like responses, have gained attention for their potential applications. Therefore, familiarity with LLMs and other promising generative AI tools is crucial to harness their potential safely and effectively. As these AI-based technologies continue to evolve, medical professionals must develop a strong understanding of AI terminologies and concepts, particularly generative AI, to effectively tackle real-world challenges and create solutions. This knowledge will enable healthcare professionals to utilize AI-driven innovations for improved patient care and increased productivity in the future. In this brief technical report, we explore 20 of the most relevant terminology associated with the underlying technology behind LLMs and generative AI as they relate to the medical field and provide some examples of how these topics relate to healthcare applications to help in their understanding.
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Affiliation(s)
- Oleksiy Melnyk
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington D.C., USA
| | - Ahmed Ismail
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington D.C., USA
| | - Nima S Ghorashi
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington D.C., USA
| | - Mary Heekin
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington D.C., USA
| | - Ramin Javan
- Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington D.C., USA
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Chang X, Wang J, Zhang G, Yang M, Xi Y, Xi C, Chen G, Nie X, Meng B, Quan X. Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural network. Cell Rep Med 2023; 4:100914. [PMID: 36720223 PMCID: PMC9975100 DOI: 10.1016/j.xcrm.2022.100914] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/01/2022] [Accepted: 12/29/2022] [Indexed: 01/31/2023]
Abstract
This study develops a method combining a convolutional neural network model, INSIGHT, with a self-attention model, WiseMSI, to predict microsatellite instability (MSI) based on the tiles in colorectal cancer patients from a multicenter Chinese cohort. After INSIGHT differentiates tumor tiles from normal tissue tiles in a whole slide image, features of tumor tiles are extracted with a ResNet model pre-trained on ImageNet. Attention-based pooling is adopted to aggregate tile-level features into slide-level representation. INSIGHT has an area under the curve (AUC) of 0.985 for tumor patch classification. The Spearman correlation coefficient of tumor cell fraction given by expert pathologist and INSIGHT is 0.7909. WiseMSI achieves a specificity of 94.7% (95% confidence interval [CI] 93.7%-95.7%), a sensitivity of 84.7% (95% CI 82.6%-86.9%), and an AUC of 0.954 (95% CI 0.948-0.960). Comparative analysis shows that this method has better performance than the other five classic deep learning methods.
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Affiliation(s)
- Xiaona Chang
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Jianchao Wang
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Guanjun Zhang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Ming Yang
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Yanfeng Xi
- Department of Pathology, Shanxi Provincial Cancer Hospital, Taiyuan 030013, China
| | | | - Gang Chen
- Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Xiu Nie
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Bin Meng
- Department of Pathology, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
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Gleichgerrcht E, Munsell B, Keller SS, Drane DL, Jensen JH, Spampinato MV, Pedersen NP, Weber B, Kuzniecky R, McDonald C, Bonilha L. Radiological identification of temporal lobe epilepsy using artificial intelligence: a feasibility study. Brain Commun 2021; 4:fcab284. [PMID: 35243343 PMCID: PMC8887904 DOI: 10.1093/braincomms/fcab284] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 12/15/2022] Open
Abstract
Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal sclerosis. Identifying these MRI features is critical for the diagnosis and management of temporal lobe epilepsy. To date, this process relies on visual assessment by highly trained human experts (e.g. neuroradiologists, epileptologists). Artificial intelligence is increasingly recognized as a promising aid in the radiological evaluation of neurological diseases, yet its applications in temporal lobe epilepsy have been limited. Here, we applied a convolutional neural network to assess the classification accuracy of temporal lobe epilepsy based on structural MRI. We demonstrate that convoluted neural networks can achieve high accuracy in the identification of unilateral temporal lobe epilepsy cases even when the MRI had been originally interpreted as normal by experts. We show that accuracy can be potentiated by employing smoothed grey matter maps and a direct acyclic graphs approach. We further discuss the foundations for the development of computer-aided tools to assist with the diagnosis of epilepsy.
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Affiliation(s)
- Ezequiel Gleichgerrcht
- Department of Neurology, Medical University of South
Carolina, Charleston, SC 29425, USA
| | - Brent Munsell
- Department of Computer Science, University of North
Carolina, Chapel Hill, NC 27599, USA
- Department of Psychiatry, University of North
Carolina, Chapel Hill, NC 27599, USA
| | - Simon S Keller
- Institute of Systems, Molecular and Integrative
Biology, University of Liverpool, Liverpool L69 7BE, UK
- The Walton Centre NHS Foundation
Trust, Liverpool L9 7LJ, UK
| | - Daniel L Drane
- Department of Neurology, Emory
University, Atlanta, GA 30322, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of
South Carolina, Charleston, SC 29425, USA
| | - M Vittoria Spampinato
- Department of Radiology, Medical University of South
Carolina, Charleston, SC 29425, USA
| | - Nigel P Pedersen
- Department of Neurology, Emory
University, Atlanta, GA 30322, USA
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition
Research, University of Bonn, Bonn 53113, Germany
| | - Ruben Kuzniecky
- Department of Neurology, Hofstra
University/Northwell, New York, NY 10075, USA
| | - Carrie McDonald
- Department of Psychiatry, University of California
San Diego, La Jolla, CA 92093, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South
Carolina, Charleston, SC 29425, USA
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Garland J, Ondruschka B, Stables S, Morrow P, Kesha K, Glenn C, Tse R. Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study. J Forensic Sci 2020; 65:2019-2022. [PMID: 32639630 DOI: 10.1111/1556-4029.14502] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/07/2020] [Accepted: 06/15/2020] [Indexed: 12/27/2022]
Abstract
Postmortem computed tomography (PMCT) is a relatively recent advancement in forensic pathology practice that has been increasingly used as an ancillary investigation and screening tool. One area of clinical CT imaging that has garnered a lot of research interest recently is the area of "artificial intelligence" (AI), such as in screening and computer-assisted diagnostics. This feasibility study investigated the application of convolutional neural network, a form of deep learning AI, to PMCT head imaging in differentiating fatal head injury from controls. PMCT images of a transverse section of the head at the level of the frontal sinus from 25 cases of fatal head injury were combined with 25 nonhead-injury controls and divided into training and testing datasets. A convolutional neural network was constructed using Keras and was trained against the training data before being assessed against the testing dataset. The results of this study demonstrated an accuracy of between 70% and 92.5%, with difficulties in recognizing subarachnoid hemorrhage and in distinguishing congested vessels and prominent falx from head injury. These results are promising for potential applications as a screening tool or in computer-assisted diagnostics in the future.
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Affiliation(s)
- Jack Garland
- Forensic and Analytical Science Service, 480 Weeroona Rd, Lidcombe, NSW, 2141, Australia
| | - Benjamin Ondruschka
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52 20251, Hamburg, Germany
| | - Simon Stables
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Paul Morrow
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Kilak Kesha
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Charley Glenn
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Rexson Tse
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023.,University of Auckland Faculty of Medical and Health Sciences, 85 Park Road, Grafton, Auckland, New Zealand, 1023
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