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Stogiannos N, O'Regan T, Scurr E, Litosseliti L, Pogose M, Harvey H, Kumar A, Malik R, Barnes A, McEntee MF, Malamateniou C. AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers. Radiography (Lond) 2024; 30:612-621. [PMID: 38325103 DOI: 10.1016/j.radi.2024.01.019] [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: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption. METHODS An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables. RESULTS In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers. CONCLUSION AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks. IMPLICATIONS FOR PRACTICE The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training.
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Affiliation(s)
- N Stogiannos
- Division of Midwifery & Radiography, City, University of London, UK; Medical Imaging Department, Corfu General Hospital, Greece.
| | - T O'Regan
- The Society and College of Radiographers, London, UK.
| | - E Scurr
- The Royal Marsden NHS Foundation Trust, UK.
| | - L Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - M Pogose
- Quality Assurance and Regulatory Affairs, Hardian Health, UK.
| | | | - A Kumar
- Frimley Health NHS Foundation Trust, UK.
| | - R Malik
- Bolton NHS Foundation Trust, UK.
| | - A Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | - M F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - C Malamateniou
- Division of Midwifery & Radiography, City, University of London, UK; Society and College of Radiographers AI Advisory Group, London, UK; European Society of Medical Imaging Informatics, Vienna, Austria; European Federation of Radiographer Societies, Cumieira, Portugal.
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Lee BM, Kim JS, Chang Y, Choi SH, Park JW, Byun HK, Kim YB, Lee IJ, Chang JS. Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00352-3. [PMID: 38431232 DOI: 10.1016/j.ijrobp.2024.02.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 02/12/2024] [Accepted: 02/18/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments. METHODS AND MATERIALS The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95). RESULTS We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection. CONCLUSIONS In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.
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Affiliation(s)
- Byung Min Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Radiation Oncology, Uijeongbu St. Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | | | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Won Park
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hwa Kyung Byun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ik Jae Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Rayn K, Gokhroo G, Jeffers B, Gupta V, Chaudhari S, Clark R, Magliari A, Beriwal S. Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis. Adv Radiat Oncol 2024; 9:101326. [PMID: 38405314 PMCID: PMC10885554 DOI: 10.1016/j.adro.2023.101326] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/18/2023] [Indexed: 02/27/2024] Open
Abstract
Purpose The autosegmentation algorithm of Siemens Healthineers version VA 30 (AASH) (Siemens Healthineers, Erlangen, Germany) was trained and developed in the male pelvis, with no published data on its usability in the female pelvis. This is the first multi-institutional study to describe and evaluate an artificial intelligence algorithm for autosegmentation of the pelvic nodal region by gender. Methods and Materials We retrospectively evaluated AASH pelvic nodal autosegmentation in both male and female patients treated at our network of institutions. The automated pelvic nodal contours generated by AASH were evaluated by 1 board-certified radiation oncologist. A 4-point scale was used for each nodal region contour: a score of 4 is clinically usable with minimal edits; a score of 3 requires minor edits (missing nodal contour region, cutting through vessels, or including bowel loops) in 3 or fewer computed tomography slices; a score of 2 requires major edits, as previously defined but in 4 or more computed tomography slices; and a score of 1 requires complete recontouring of the region. Pelvic nodal regions included the right and left side of the common iliac, external iliac, internal iliac, obturator, and midline presacral nodes. In addition, patients were graded based on their lowest nodal contour score. Statistical analysis was performed using Fisher exact tests and Yates-corrected χ2 tests. Results Fifty-two female and 51 male patients were included in the study, representing a total of 468 and 447 pelvic nodal regions, respectively. Ninety-six percent and 99% of contours required minor edits at most (score of 3 or 4) for female and male patients, respectively (P = .004 using Fisher exact test; P = .007 using Yates correction). No nodal regions had a statistically significant difference in scores between female and male patients. The percentage of patients requiring no more than minor edits was 87% (45 patients) and 92% (47 patients) for female and male patients, respectively (P = .53 using Fisher exact test; P = .55 using Yates correction). Conclusions AASH pelvic nodal autosegmentation performed very well in both male and female pelvic nodal regions, although with better male pelvic nodal autosegmentation. As autosegmentation becomes more widespread, it may be important to have equal representation from all sexes in training and validation of autosegmentation algorithms.
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Affiliation(s)
- Kareem Rayn
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, New York
- Varian Medical Systems Inc, Palo Alto, California
| | | | - Brian Jeffers
- Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Vibhor Gupta
- American Oncology Institute, Hyderabad, CA, India
| | | | - Ryan Clark
- Varian Medical Systems Inc, Palo Alto, California
| | | | - Sushil Beriwal
- Varian Medical Systems Inc, Palo Alto, California
- Division of Radiation Oncology, Allegheny Health Network Cancer Institute, Pittsburgh, Pennsylvania
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Beddok A, Lim R, Thariat J, Shih HA, El Fakhri G. A Comprehensive Primer on Radiation Oncology for Non-Radiation Oncologists. Cancers (Basel) 2023; 15:4906. [PMID: 37894273 PMCID: PMC10605284 DOI: 10.3390/cancers15204906] [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: 09/14/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
Background: Multidisciplinary management is crucial in cancer diagnosis and treatment. Multidisciplinary teams include specialists in surgery, medical therapies, and radiation therapy (RT), each playing unique roles in oncology care. One significant aspect is RT, guided by radiation oncologists (ROs). This paper serves as a detailed primer for non-oncologists, medical students, or non-clinical investigators, educating them on contemporary RT practices. Methods: This report follows the process of RT planning and execution. Starting from the decision-making in multidisciplinary teams to the completion of RT and subsequent patient follow-up, it aims to offer non-oncologists an understanding of the RO's work in a comprehensive manner. Results: The first step in RT is a planning session that includes obtaining a CT scan of the area to be treated, known as the CT simulation. The patients are imaged in the exact position in which they will receive treatment. The second step, which is the primary source of uncertainty, involves the delineation of treatment targets and organs at risk (OAR). The objective is to ensure precise irradiation of the target volume while sparing the OARs as much as possible. Various radiation modalities, such as external beam therapy with electrons, photons, or particles (including protons and carbon ions), as well as brachytherapy, are utilized. Within these modalities, several techniques, such as three-dimensional conformal RT, intensity-modulated RT, volumetric modulated arc therapy, scattering beam proton therapy, and intensity-modulated proton therapy, are employed to achieve optimal treatment outcomes. The RT plan development is an iterative process involving medical physicists, dosimetrists, and ROs. The complexity and time required vary, ranging from an hour to a week. Once approved, RT begins, with image-guided RT being standard practice for patient alignment. The RO manages acute toxicities during treatment and prepares a summary upon completion. There is a considerable variance in practices, with some ROs offering lifelong follow-up and managing potential late effects of treatment. Conclusions: Comprehension of RT clinical effects by non-oncologists providers significantly elevates long-term patient care quality. Hence, educating non-oncologists enhances care for RT patients, underlining this report's importance.
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Affiliation(s)
- Arnaud Beddok
- Department of Radiation Oncology, Institut Godinot, 51100 Reims, France
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Ruth Lim
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Juliette Thariat
- Department of Radiation Oncology, Centre François-Baclesse, 14000 Caen, France
| | - Helen A. Shih
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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Heilemann G, Buschmann M, Lechner W, Dick V, Eckert F, Heilmann M, Herrmann H, Moll M, Knoth J, Konrad S, Simek IM, Thiele C, Zaharie A, Georg D, Widder J, Trnkova P. Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy. Phys Imaging Radiat Oncol 2023; 28:100515. [PMID: 38111502 PMCID: PMC10726238 DOI: 10.1016/j.phro.2023.100515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 12/20/2023] Open
Abstract
Background and purpose Tools for auto-segmentation in radiotherapy are widely available, but guidelines for clinical implementation are missing. The goal was to develop a workflow for performance evaluation of three commercial auto-segmentation tools to select one candidate for clinical implementation. Materials and Methods One hundred patients with six treatment sites (brain, head-and-neck, thorax, abdomen, and pelvis) were included. Three sets of AI-based contours for organs-at-risk (OAR) generated by three software tools and manually drawn expert contours were blindly rated for contouring accuracy. The dice similarity coefficient (DSC), the Hausdorff distance, and a dose/volume evaluation based on the recalculation of the original treatment plan were assessed. Statistically significant differences were tested using the Kruskal-Wallis test and the post-hoc Dunn Test with Bonferroni correction. Results The mean DSC scores compared to expert contours for all OARs combined were 0.80 ± 0.10, 0.75 ± 0.10, and 0.74 ± 0.11 for the three software tools. Physicians' rating identified equivalent or superior performance of some AI-based contours in head (eye, lens, optic nerve, brain, chiasm), thorax (e.g., heart and lungs), and pelvis and abdomen (e.g., kidney, femoral head) compared to manual contours. For some OARs, the AI models provided results requiring only minor corrections. Bowel-bag and stomach were not fit for direct use. During the interdisciplinary discussion, the physicians' rating was considered the most relevant. Conclusion A comprehensive method for evaluation and clinical implementation of commercially available auto-segmentation software was developed. The in-depth analysis yielded clear instructions for clinical use within the radiotherapy department.
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Affiliation(s)
- Gerd Heilemann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Martin Buschmann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Wolfgang Lechner
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Vincent Dick
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Franziska Eckert
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Martin Heilmann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Harald Herrmann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Matthias Moll
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Johannes Knoth
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Stefan Konrad
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Inga-Malin Simek
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Christopher Thiele
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Alexandru Zaharie
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Joachim Widder
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - Petra Trnkova
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
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Court L, Aggarwal A, Burger H, Cardenas C, Chung C, Douglas R, du Toit M, Jaffray D, Jhingran A, Mejia M, Mumme R, Muya S, Naidoo K, Ndumbalo J, Nealon K, Netherton T, Nguyen C, Olanrewaju N, Parkes J, Shaw W, Trauernicht C, Xu M, Yang J, Zhang L, Simonds H, Beadle BM. Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant. JCO Glob Oncol 2023; 9:e2200431. [PMID: 37471671 PMCID: PMC10581646 DOI: 10.1200/go.22.00431] [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: 12/18/2022] [Revised: 02/08/2023] [Accepted: 04/24/2023] [Indexed: 07/22/2023] Open
Abstract
PURPOSE Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.
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Affiliation(s)
- Laurence Court
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ajay Aggarwal
- Guy's and St Thomas' Hospital, London, United Kingdom
| | - Hester Burger
- Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | | | - Christine Chung
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Raphael Douglas
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Monique du Toit
- Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | - David Jaffray
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Anuja Jhingran
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael Mejia
- Benavides Cancer Institute, University of Santo Tomas, Manila, Philippines
| | - Raymond Mumme
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Komeela Naidoo
- Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | | | - Kelly Nealon
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | - Niki Olanrewaju
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jeannette Parkes
- Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Willie Shaw
- University of the Free State, Bloemfontein, South Africa
| | | | - Melody Xu
- University of California San Francisco, San Francisco, CA
| | - Jinzhong Yang
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Lifei Zhang
- The University of Texas MD Anderson Cancer Center, Houston, TX
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Potočnik J, Foley S, Thomas E. Current and potential applications of artificial intelligence in medical imaging practice: A narrative review. J Med Imaging Radiat Sci 2023; 54:376-385. [PMID: 37062603 DOI: 10.1016/j.jmir.2023.03.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
BACKGROUND AND PURPOSE Artificial intelligence (AI) is present in many areas of our lives. Much of the digital data generated in health care can be used for building automated systems to bring improvements to existing workflows and create a more personalised healthcare experience for patients. This review outlines select current and potential AI applications in medical imaging practice and provides a view of how diagnostic imaging suites will operate in the future. Challenges associated with potential applications will be discussed and healthcare staff considerations necessary to benefit from AI-enabled solutions will be outlined. METHODS Several electronic databases, including PubMed, ScienceDirect, Google Scholar, and University College Dublin Library Database, were used to identify relevant articles with a Boolean search strategy. Textbooks, government sources and vendor websites were also considered. RESULTS/DISCUSSION Many AI-enabled solutions in radiographic practice are available with more automation on the horizon. Traditional workflow will become faster, more effective, and more user friendly. AI can handle administrative or technical types of work, meaning it is applicable across all aspects of medical imaging practice. CONCLUSION AI offers significant potential to automate most of the manual tasks, ensure service consistency, and improve patient care. Radiographers, radiation therapists, and clinicians should ensure they have adequate understanding of the technology to enable ethical oversight of its implementation.
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Affiliation(s)
- Jaka Potočnik
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland.
| | - Shane Foley
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
| | - Edel Thomas
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
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