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Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, Bin Saleh K, Alowais SA, Alshaya OA, Rahman I, Al Yami MS, Albekairy AM. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Res Social Adm Pharm 2023:S1551-7411(23)00280-2. [PMID: 37321925 DOI: 10.1016/j.sapharm.2023.05.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Artificial Intelligence (AI) has revolutionized various domains, including education and research. Natural language processing (NLP) techniques and large language models (LLMs) such as GPT-4 and BARD have significantly advanced our comprehension and application of AI in these fields. This paper provides an in-depth introduction to AI, NLP, and LLMs, discussing their potential impact on education and research. By exploring the advantages, challenges, and innovative applications of these technologies, this review gives educators, researchers, students, and readers a comprehensive view of how AI could shape educational and research practices in the future, ultimately leading to improved outcomes. Key applications discussed in the field of research include text generation, data analysis and interpretation, literature review, formatting and editing, and peer review. AI applications in academics and education include educational support and constructive feedback, assessment, grading, tailored curricula, personalized career guidance, and mental health support. Addressing the challenges associated with these technologies, such as ethical concerns and algorithmic biases, is essential for maximizing their potential to improve education and research outcomes. Ultimately, the paper aims to contribute to the ongoing discussion about the role of AI in education and research and highlight its potential to lead to better outcomes for students, educators, and researchers.
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Affiliation(s)
- Tariq Alqahtani
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
| | - Hisham A Badreldin
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sahar S Alghamdi
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shuroug A Alowais
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Omar A Alshaya
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Ishrat Rahman
- Department of Basic Dental Sciences, College of Dentistry, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Majed S Al Yami
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
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Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study. Cancers (Basel) 2022; 14:cancers14092069. [PMID: 35565199 PMCID: PMC9104481 DOI: 10.3390/cancers14092069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 03/30/2022] [Accepted: 04/19/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Due to the fact that they take inter-slice information into account, 3D- and 2.5D-convolutional neural networks (CNNs) potentially perform better in tumor detection tasks than 2D-CNNs. However, this potential benefit is at the expense of increased computational power and the need for segmentations as an input. Therefore, in this study we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. We retrained (539 patients) and internally validated (94 patients) a pretrained CNN using contrast-enhanced MRI slices from one institution. Furthermore, we externally validated the CNN using contrast-enhanced MRI slices from another institution. This resulted in an accuracy of 0.949 (95% CI 0.935–0.963) and 0.912 (95% CI 0.866–0.958) for the internal and external validation, respectively. Our findings indicate that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased requirement for computational power and the fact that there is no need for segmentations. Abstract In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935–0.963) for the internal validation and 0.912 (95% CI 0.866–0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures.
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Sager P, Näf L, Vu E, Fischer T, Putora PM, Ehret F, Fürweger C, Schröder C, Förster R, Zwahlen DR, Muacevic A, Windisch P. Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices-A Feasibility Study. Diagnostics (Basel) 2021; 11:1676. [PMID: 34574017 PMCID: PMC8465488 DOI: 10.3390/diagnostics11091676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/04/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas (VS) as a computationally inexpensive alternative that does not require the creation of segmentations. Methods: A total of 2992 T1-weighted contrast-enhanced axial slices containing VS from the MRIs of 633 patients were labeled according to tumor location, of which 2538 slices from 539 patients were used for training a CNN (ResNet-34) to classify them according to the side of the tumor as a surrogate for detection and 454 slices from 94 patients were used for internal validation. The model was then externally validated on contrast-enhanced and non-contrast-enhanced slices from a different institution. Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results: The model achieved an accuracy of 0.928 (95% CI: 0.869-0.987) on contrast-enhanced slices and 0.795 (95% CI: 0.702-0.888) on non-contrast-enhanced slices from the external validation cohorts. The implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the focus of the model was not limited to the contrast-enhancing tumor but to a larger area of the cerebellum and the cerebellopontine angle. Conclusions: Single-slice predictions might constitute a computationally inexpensive alternative to training 2.5/3D-CNNs for certain detection tasks in medical imaging even without the use of segmentations. Head-to-head comparisons between 2D and more sophisticated architectures could help to determine the difference in accuracy, especially for more difficult tasks.
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Affiliation(s)
- Philipp Sager
- Department of Radiation Oncology, Kantonsspital Winterthur, 8400 Winterthur, Switzerland; (P.S.); (C.S.); (R.F.); (D.R.Z.)
| | - Lukas Näf
- Department of Radiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland; (L.N.); (T.F.)
| | - Erwin Vu
- Department of Radiation Oncology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland; (E.V.); (P.M.P.)
| | - Tim Fischer
- Department of Radiology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland; (L.N.); (T.F.)
| | - Paul M. Putora
- Department of Radiation Oncology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland; (E.V.); (P.M.P.)
- Department of Radiation Oncology, University of Bern, 3010 Bern, Switzerland
| | - Felix Ehret
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, 13353 Berlin, Germany;
- European Cyberknife Center, 81377 Munich, Germany; (C.F.); (A.M.)
| | - Christoph Fürweger
- European Cyberknife Center, 81377 Munich, Germany; (C.F.); (A.M.)
- Department of Stereotaxy and Functional Neurosurgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Christina Schröder
- Department of Radiation Oncology, Kantonsspital Winterthur, 8400 Winterthur, Switzerland; (P.S.); (C.S.); (R.F.); (D.R.Z.)
| | - Robert Förster
- Department of Radiation Oncology, Kantonsspital Winterthur, 8400 Winterthur, Switzerland; (P.S.); (C.S.); (R.F.); (D.R.Z.)
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Daniel R. Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8400 Winterthur, Switzerland; (P.S.); (C.S.); (R.F.); (D.R.Z.)
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | | | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8400 Winterthur, Switzerland; (P.S.); (C.S.); (R.F.); (D.R.Z.)
- European Cyberknife Center, 81377 Munich, Germany; (C.F.); (A.M.)
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