1
|
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; 36:596-605. [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] [MESH Headings] [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.
Collapse
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
| |
Collapse
|
2
|
Kibrom BT, Manyazewal T, Demma BD, Feleke TH, Kabtimer AS, Ayele ND, Korsa EW, Hailu SS. Emerging technologies in pediatric radiology: current developments and future prospects. Pediatr Radiol 2024; 54:1428-1436. [PMID: 39012407 DOI: 10.1007/s00247-024-05997-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/17/2024]
Abstract
Radiological imaging is a crucial diagnostic tool for the pediatric population. However, it is associated with several unique challenges in this age group compared to adults. These challenges mainly come from the fact that children are not small-sized adults and differ in development, anatomy, physiology, and pathology compared to adults. This paper reviews relevant articles published between January 2015 and October 2023 to analyze challenges associated with imaging technologies currently used in pediatric radiology, emerging technologies, and their role in resolving the challenges and future prospects of pediatric radiology. In recent decades, imaging technologies have advanced rapidly, developing advanced ultrasound, computed tomography, magnetic resonance, nuclear imaging, teleradiology, artificial intelligence, machine learning, three-dimensional printing, radiomics, and radiogenomics, among many others. By prioritizing the unique needs of pediatric patients while developing such technologies, we can significantly alleviate the challenges faced in pediatric radiology.
Collapse
Affiliation(s)
- Bethlehem T Kibrom
- Centre for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, P.O. Box 9086, Addis Ababa, Ethiopia.
| | - Tsegahun Manyazewal
- Centre for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, P.O. Box 9086, Addis Ababa, Ethiopia
| | - Biruk D Demma
- College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Tesfahunegn H Feleke
- Centre for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, P.O. Box 9086, Addis Ababa, Ethiopia
- Potomac Urology Clinic, Alexandria, VA, USA
| | | | - Nitsuh D Ayele
- College of Health Sciences, Wolkite University, Wolkite, Ethiopia
| | - Eyasu W Korsa
- Department of Radiology, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Samuel S Hailu
- Department of Radiology, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| |
Collapse
|
3
|
Warren BE, Bilbily A, Gichoya JW, Conway A, Li B, Fawzy A, Barragán C, Jaberi A, Mafeld S. An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge. Can Assoc Radiol J 2024; 75:558-567. [PMID: 38445497 DOI: 10.1177/08465371241236376] [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] [Indexed: 03/07/2024] Open
Abstract
Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).
Collapse
Affiliation(s)
- Blair Edward Warren
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- 16 Bit Inc., Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Aaron Conway
- Prince Charles Hospital, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ben Li
- Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Aly Fawzy
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Camilo Barragán
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Arash Jaberi
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Sebastian Mafeld
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| |
Collapse
|
4
|
Arif WM. Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology. Int J Gen Med 2024; 17:3129-3136. [PMID: 39049835 PMCID: PMC11268710 DOI: 10.2147/ijgm.s465944] [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: 04/06/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
Study Purpose This study aims to analyze radiologic technology student's perceptions of artificial intelligence (AI) and its applications in radiology. Methods A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students. Results Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI. Conclusion The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.
Collapse
Affiliation(s)
- Wejdan M Arif
- King Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi Arabia
| |
Collapse
|
5
|
Linguraru MG, Bakas S, Aboian M, Chang PD, Flanders AE, Kalpathy-Cramer J, Kitamura FC, Lungren MP, Mongan J, Prevedello LM, Summers RM, Wu CC, Adewole M, Kahn CE. Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts. Radiol Artif Intell 2024; 6:e240225. [PMID: 38984986 PMCID: PMC11294958 DOI: 10.1148/ryai.240225] [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: 04/13/2024] [Revised: 04/13/2024] [Accepted: 04/25/2024] [Indexed: 07/11/2024]
Abstract
The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.
Collapse
Affiliation(s)
- Marius George Linguraru
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Spyridon Bakas
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Mariam Aboian
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Peter D. Chang
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Adam E. Flanders
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Jayashree Kalpathy-Cramer
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Felipe C. Kitamura
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Matthew P. Lungren
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - John Mongan
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Luciano M. Prevedello
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Ronald M. Summers
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Carol C. Wu
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Maruf Adewole
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| | - Charles E. Kahn
- From the Sheikh Zayed Institute for Pediatric Surgical Innovation,
Children’s National Hospital, Washington, DC (M.G.L.); Divisions of
Radiology and Pediatrics, George Washington University School of Medicine and
Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology,
Department of Pathology & Laboratory Medicine, School of Medicine,
Indiana University, Indianapolis, Ind (S.B.); Department of Radiology,
Children’s Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department
of Radiological Sciences, University of California Irvine, Irvine, Calif
(P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa
(A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical
Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI,
Diagnósticos da América SA (DasaInova), São Paulo, Brazil
(F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São
Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass
(M.P.L.); Department of Radiology and Biomedical Imaging and Center for
Intelligent Imaging, University of California San Francisco, San Francisco,
Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical
Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences,
National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division
of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston,
Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos
College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology,
University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA
19104-6243 (C.E.K.)
| |
Collapse
|
6
|
Stogiannos N, Litosseliti L, O'Regan T, Scurr E, Barnes A, Kumar A, Malik R, Pogose M, Harvey H, McEntee MF, Malamateniou C. Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK. Int J Med Inform 2024; 186:105423. [PMID: 38531254 DOI: 10.1016/j.ijmedinf.2024.105423] [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: 08/15/2023] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future. METHODS This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions. RESULTS Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.
Collapse
Affiliation(s)
- Nikolaos Stogiannos
- Department of Radiography, City, University of London, UK; Magnitiki Tomografia Kerkyras, Greece.
| | - Lia Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - Tracy O'Regan
- The Society and College of Radiographers, London, UK.
| | | | - Anna Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | | | | | | | | | - Mark F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - Christina Malamateniou
- Department of Radiography, City, University of London, UK; European Society of Medical Imaging Informatics, Vienna, Austria.
| |
Collapse
|
7
|
Culp ML, Mahmoud S, Liu D, Haworth IS. An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum. AMERICAN JOURNAL OF PHARMACEUTICAL EDUCATION 2024; 88:100696. [PMID: 38574998 DOI: 10.1016/j.ajpe.2024.100696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVE This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area. METHODS AI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity. RESULTS There was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum. CONCLUSION This is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.
Collapse
Affiliation(s)
- Megan L Culp
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
| | - Sara Mahmoud
- University of the Pacific Thomas J. Long School of Pharmacy, Department of Pharmacy Practice, Stockton, CA, USA.
| | - Daniel Liu
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
| | - Ian S Haworth
- University of Southern California, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology & Pharmaceutical Sciences, Los Angeles, CA, USA
| |
Collapse
|
8
|
Doherty G, McLaughlin L, Hughes C, McConnell J, Bond R, McFadden S. A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff. Radiography (Lond) 2024; 30:474-482. [PMID: 38217933 DOI: 10.1016/j.radi.2023.12.019] [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: 10/26/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/15/2024]
Abstract
INTRODUCTION Medical imaging is arguably the most technologically advanced field in healthcare, encompassing a range of technologies which continually evolve as computing power and human knowledge expand. Artificial Intelligence (AI) is the next frontier which medical imaging is pioneering. The rapid development and implementation of AI has the potential to revolutionise healthcare, however, to do so, staff must be competent and confident in its application, hence AI readiness is an important precursor to AI adoption. Research to ascertain the best way to deliver this AI-enabled healthcare training is in its infancy. The aim of this scoping review is to compare existing studies which investigate and evaluate the efficacy of AI educational interventions for medical imaging staff. METHODS Following the creation of a search strategy and keyword searches, screening was conducted to determine study eligibility. This consisted of a title and abstract scan, then subsequently a full-text review. Articles were included if they were empirical studies wherein an educational intervention on AI for medical imaging staff was created, delivered, and evaluated. RESULTS Of the initial 1309 records returned, n = 5 (∼0.4 %) of studies met the eligibility criteria of the review. The curricula and delivery in each of the five studies shared similar aims and a 'flipped classroom' delivery was the most utilised method. However, the depth of content covered in the curricula of each varied and measured outcomes differed greatly. CONCLUSION The findings of this review will provide insights into the evaluation of existing AI educational interventions, which will be valuable when planning AI education for healthcare staff. IMPLICATIONS FOR PRACTICE This review highlights the need for standardised and comprehensive AI training programs for imaging staff.
Collapse
Affiliation(s)
- G Doherty
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom.
| | - L McLaughlin
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - C Hughes
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - J McConnell
- Leeds Teaching Hospitals NHS Trust, United Kingdom
| | - R Bond
- Ulster University, School of Computing, Faculty of Computing, Engineering and the Built Environment, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - S McFadden
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| |
Collapse
|
9
|
van Kooten MJ, Tan CO, Hofmeijer EIS, van Ooijen PMA, Noordzij W, Lamers MJ, Kwee TC, Vliegenthart R, Yakar D. A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist. Insights Imaging 2024; 15:15. [PMID: 38228800 DOI: 10.1186/s13244-023-01595-3] [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: 06/22/2023] [Accepted: 12/06/2023] [Indexed: 01/18/2024] Open
Abstract
OBJECTIVES To present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists. METHODS The AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents' knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants' expectations and progress and were analyzed using a Wilcoxon rank-sum test. RESULTS There was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants' confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful. CONCLUSION Designing an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants' perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization. CRITICAL RELEVANCE STATEMENT The framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care. KEY POINTS • AI education is necessary to prepare a new generation of AI-conscious radiologists. • The AI curriculum increased participants' perception of AI knowledge and skills in radiology. • This five-step framework can assist integrating AI education into radiology residency programs.
Collapse
Affiliation(s)
- Maria Jorina van Kooten
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
| | - Can Ozan Tan
- Robotics and Mechatronics Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Elfi Inez Saïda Hofmeijer
- Robotics and Mechatronics Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Peter Martinus Adrianus van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Walter Noordzij
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Maria Jolanda Lamers
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Thomas Christian Kwee
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Derya Yakar
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| |
Collapse
|
10
|
Bradley D, Harrison J, Goodall M, Dobrashian R. Are Advanced Clinical Practitioners perfectly placed to re-report neuroimages to support clinical diagnosis of dementia? INTERNATIONAL JOURNAL FOR ADVANCING PRACTICE 2023; 1:146-150. [PMID: 38229770 PMCID: PMC7615529 DOI: 10.12968/ijap.2023.1.3.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
With the ageing population, the prevalence of dementia is increasing worldwide. There is an emphasis on early, timely diagnosis and treatment options for people with a dementia yet wait times from referral to diagnosis have increased. Neuroimaging performed by radiologists is utilised to support dementia diagnosis and some patients will already have a CT scan from a pre-existing condition such as stroke. The purpose of this commentary is to consider whether ACPs who specialise in dementia, are perfectly placed to re-report on pre-existing neuroimages to support the clinical diagnosis of dementia.
Collapse
Affiliation(s)
| | - Joanna Harrison
- Synthesis Economic Evaluation and Decision Science (SEEDS) Group, University of Central Lancashire
| | - Mark Goodall
- Institute of Population Health, University of Liverpool
| | | |
Collapse
|
11
|
Yeung AWK, Torkamani A, Butte AJ, Glicksberg BS, Schuller B, Rodriguez B, Ting DSW, Bates D, Schaden E, Peng H, Willschke H, van der Laak J, Car J, Rahimi K, Celi LA, Banach M, Kletecka-Pulker M, Kimberger O, Eils R, Islam SMS, Wong ST, Wong TY, Gao W, Brunak S, Atanasov AG. The promise of digital healthcare technologies. Front Public Health 2023; 11:1196596. [PMID: 37822534 PMCID: PMC10562722 DOI: 10.3389/fpubh.2023.1196596] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 09/04/2023] [Indexed: 10/13/2023] Open
Abstract
Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.
Collapse
Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, University of Hong Kong, Hong Kong, China
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Ali Torkamani
- Department of Integrative Structural and Computational Biology, Scripps Research Translational Institute, La Jolla, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Benjamin S. Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Björn Schuller
- Department of Computing, Imperial College London, London, United Kingdom
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Daniel S. W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - David Bates
- Department of General Internal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Eva Schaden
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Harald Willschke
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Josip Car
- Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
- Centre for Population Health Sciences, LKC Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kazem Rahimi
- Deep Medicine Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), Lodz, Poland
- Department of Cardiology and Adult Congenital Heart Diseases, Polish Mother’s Memorial Hospital Research Institute (PMMHRI), Lodz, Poland
| | - Maria Kletecka-Pulker
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute for Ethics and Law in Medicine, University of Vienna, Vienna, Austria
| | - Oliver Kimberger
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Roland Eils
- Digital Health Center, Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | | | - Stephen T. Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, T. T. and W. F. Chao Center for BRAIN, Houston Methodist Academic Institute, Houston Methodist Hospital, Houston, TX, United States
- Departments of Radiology, Pathology and Laboratory Medicine and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
| | - Tien Yin Wong
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Atanas G. Atanasov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland
| |
Collapse
|
12
|
Alkhulaifat D, Rafful P, Khalkhali V, Welsh M, Sotardi ST. Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development. J Am Coll Radiol 2023; 20:724-729. [PMID: 37352995 DOI: 10.1016/j.jacr.2023.04.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/22/2023] [Accepted: 04/06/2023] [Indexed: 06/25/2023]
Abstract
Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum focused on pediatric radiology is needed to offer targeted education material on AI model development and performance evaluation. There are inherent differences between pediatric and adult practice patterns, which may hinder the application of adult AI models in pediatric cohorts. Such differences include the different imaging modality utilization, imaging acquisition parameters, lower radiation doses, the rapid growth of children and changes in their body composition, and the presence of unique pathologies and diseases, which differ in prevalence from adults. Thus, to enhance radiologists' knowledge of the applications of AI models in pediatric patients, curricula should be structured keeping in mind the unique pediatric setting and its challenges, along with methods to overcome these challenges, and pediatric-specific data governance and ethical considerations. In this report, the authors highlight the salient aspects of pediatric radiology that are necessary for AI education in the pediatric setting, including the challenges for research investigation and clinical implementation.
Collapse
Affiliation(s)
- Dana Alkhulaifat
- Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Patricia Rafful
- Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Vahid Khalkhali
- Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael Welsh
- Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Susan T Sotardi
- Director, CHOP Radiology Informatics and Artificial Intelligence, Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
| |
Collapse
|
13
|
Walsh G, Stogiannos N, van de Venter R, Rainey C, Tam W, McFadden S, McNulty JP, Mekis N, Lewis S, O'Regan T, Kumar A, Huisman M, Bisdas S, Kotter E, Pinto dos Santos D, Sá dos Reis C, van Ooijen P, Brady AP, Malamateniou C. Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe. BJR Open 2023; 5:20230033. [PMID: 37953871 PMCID: PMC10636340 DOI: 10.1259/bjro.20230033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/27/2023] [Accepted: 05/30/2023] [Indexed: 11/14/2023] Open
Abstract
Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.
Collapse
Affiliation(s)
- Gemma Walsh
- Division of Midwifery & Radiography, City University of London, London, United Kingdom
| | | | | | - Clare Rainey
- School of Health Sciences, Ulster University, Derry~Londonderry, Northern Ireland
| | - Winnie Tam
- Division of Midwifery & Radiography, City University of London, London, United Kingdom
| | - Sonyia McFadden
- School of Health Sciences, Ulster University, Coleraine, United Kingdom
| | | | - Nejc Mekis
- Medical Imaging and Radiotherapy Department, University of Ljubljana, Faculty of Health Sciences, Ljubljana, Slovenia
| | - Sarah Lewis
- Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Tracy O'Regan
- The Society and College of Radiographers, London, United Kingdom
| | - Amrita Kumar
- Frimley Health NHS Foundation Trust, Frimley, United Kingdom
| | - Merel Huisman
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | | | | | - Cláudia Sá dos Reis
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | | | | | | |
Collapse
|
14
|
Hashmi OU, Chan N, de Vries CF, Gangi A, Jehanli L, Lip G. Artificial intelligence in radiology: trainees want more. Clin Radiol 2023; 78:e336-e341. [PMID: 36746724 DOI: 10.1016/j.crad.2022.12.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 11/08/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023]
Abstract
AIM To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education. MATERIALS AND METHODS A survey, which ran over a period of 2 months, was created using the Google Forms platform and distributed via email to all UK training programmes. RESULTS The survey was completed by 149 trainee radiologists with at least one response from all UK training programmes. Of the responses, 83.7% were interested in AI use in radiology but 71.4% had no experience of working with AI and 79.9% would like to be involved in AI-based projects. Almost all (98.7%) felt that AI should be taught during their training, yet only one respondent stated that their training programme had implemented AI teaching. Respondents indicated that basic understanding, implementation, and critical appraisal of AI software should be prioritized in teaching. Of the trainees, 74.2% agreed that AI would enhance the job of diagnostic radiologists over the next 20 years. The main concerns raised were information technology/implementation and ethical/regulatory issues. CONCLUSION Despite the current limited availability of AI-based activities and teaching within UK training programmes, UK trainees' attitudes towards AI are mostly positive with many showing interest in being involved with AI-based projects, activities, and teaching.
Collapse
Affiliation(s)
- O-U Hashmi
- East of England Imaging Academy, The Cotman Centre, Norfolk and Norwich University Hospital, Norwich, NR4 7UB, UK.
| | - N Chan
- Department of Interventional Neuroradiology, The Royal London Hospital, Whitechapel Road, London, UK
| | - C F de Vries
- Aberdeen Centre for Health Data Science, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - A Gangi
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | - L Jehanli
- North West School of Radiology, Manchester, UK
| | - G Lip
- National Health Service Grampian (NHSG), Aberdeen Royal Infirmary, Aberdeen, UK
| |
Collapse
|
15
|
Hernández-Rodríguez J, Rodríguez-Conde MJ, Santos-Sánchez JÁ, Cabrero-Fraile FJ. Development and validation of an educational software based in artificial neural networks for training in radiology (JORCAD) through an interactive learning activity. Heliyon 2023; 9:e14780. [PMID: 37025816 PMCID: PMC10070709 DOI: 10.1016/j.heliyon.2023.e14780] [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: 07/22/2022] [Revised: 02/22/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023] Open
Abstract
The use of Computer Aided Detection (CAD) software has been previously documented as a valuable tool to improve specialist training in Radiology. This research assesses the utility of an educational software tool aimed to train residents in Radiology and other related medical specialties and students from Medicine degree. This in-house developed software, called JORCAD, integrates a CAD system based in Convolutional Neural Networks (CNNs) with annotated cases from radiological image databases. The methodology followed for software validation was expert judgement after completing an interactive learning activity. Participants received a theoretical session and a software usage tutorial and afterwards utilized the application in a dedicated workstation to analyze a series of proposed cases of thorax computed tomography (CT) and mammography. A total of 26 expert participants from the Radiology Department at Salamanca University Hospital (15 specialists and 11 residents) fulfilled the activity and evaluated different aspects through a series of surveys: software usability, case navigation tools, CAD module utility for learning and JORCAD educational capabilities. Participants also graded imaging cases to establish JORCAD usefulness for training radiology residents. According to the statistical analysis of survey results and expert cases scoring, along with their opinions, it can be concluded that JORCAD software is a useful tool for training future specialists. The combination of CAD with annotated cases from validated databases enhances learning, offering a second opinion and changing the usual training paradigm. Including software as JORCAD in residency training programs of Radiology and other medical specialties would have a positive effect on trainees' background knowledge.
Collapse
Affiliation(s)
- Jorge Hernández-Rodríguez
- Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain
- Department of Medical Physics and Radiation Protection. Salamanca University Hospital. Paseo de San Vicente 58-182 (37007), Salamanca, Spain
| | - María-José Rodríguez-Conde
- University Institute of Educational Sciences (IUCE). Grupo de Investigación en InterAcción y ELearning (GRIAL). University of Salamanca, Paseo de Canalejas 169 (37008), Salamanca, Spain
| | - José-Ángel Santos-Sánchez
- Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain
- Department of Radiology. Salamanca University Hospital. Paseo de San Vicente 58-182 (37007), Salamanca, Spain
| | - Francisco-Javier Cabrero-Fraile
- Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain
| |
Collapse
|
16
|
Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. Insights Imaging 2023; 14:25. [PMID: 36735172 PMCID: PMC9897152 DOI: 10.1186/s13244-023-01372-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/15/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. RESULTS Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. CONCLUSIONS The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.
Collapse
|
17
|
Neubauer M, Moser L, Neugebauer J, Raudner M, Wondrasch B, Führer M, Emprechtinger R, Dammerer D, Ljuhar R, Salzlechner C, Nehrer S. Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings. J Clin Med 2023; 12:jcm12030744. [PMID: 36769394 PMCID: PMC9917552 DOI: 10.3390/jcm12030744] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Radiographic knee osteoarthritis (OA) severity and clinical severity are often dissociated. Artificial intelligence (AI) aid was shown to increase inter-rater reliability in radiographic OA diagnosis. Thus, AI-aided radiographic diagnoses were compared against AI-unaided diagnoses with regard to their correlations with clinical severity. METHODS Seventy-one DICOMs (m/f = 27:42, mean age: 27.86 ± 6.5) (X-ray format) were used for AI analysis (KOALA software, IB Lab GmbH). Subjects were recruited from a physiotherapy trial (MLKOA). At baseline, each subject received (i) a knee X-ray and (ii) an assessment of five main scores (Tegner Scale (TAS); Knee Injury and Osteoarthritis Outcome Score (KOOS); International Physical Activity Questionnaire; Star Excursion Balance Test; Six-Minute Walk Test). Clinical assessments were repeated three times (weeks 6, 12 and 24). Three physicians analyzed the presented X-rays both with and without AI via KL grading. Analyses of the (i) inter-rater reliability (IRR) and (ii) Spearman's Correlation Test for the overall KL score for each individual rater with clinical score were performed. RESULTS We found that AI-aided diagnostic ratings had a higher association with the overall KL score and the KOOS. The amount of improvement due to AI depended on the individual rater. CONCLUSION AI-guided systems can improve the ratings of knee radiographs and show a stronger association with clinical severity. These results were shown to be influenced by individual readers. Thus, AI training amongst physicians might need to be increased. KL might be insufficient as a single tool for knee OA diagnosis.
Collapse
Affiliation(s)
- Markus Neubauer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Lukas Moser
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Johannes Neugebauer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Marcus Raudner
- Medical University of Vienna, High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Währinger-Gürtel 18-20, 1090 Vienna, Austria
| | - Barbara Wondrasch
- Department of Health and Social Sciences, St. Poelten University of Applied Sciences, Campus-Platz 1, 3100 St. Poelten, Austria
| | - Magdalena Führer
- Department of Health and Social Sciences, St. Poelten University of Applied Sciences, Campus-Platz 1, 3100 St. Poelten, Austria
| | - Robert Emprechtinger
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
| | - Dietmar Dammerer
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Richard Ljuhar
- ImageBiopsy Lab GmbH, Zehetnergasse 6/2/2, 1140 Vienna, Austria
| | | | - Stefan Nehrer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
- Correspondence:
| |
Collapse
|
18
|
Barreiro-Ares A, Morales-Santiago A, Sendra-Portero F, Souto-Bayarri M. Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1589. [PMID: 36674348 PMCID: PMC9867061 DOI: 10.3390/ijerph20021589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
The rise of artificial intelligence (AI) in medicine, and particularly in radiology, is becoming increasingly prominent. Its impact will transform the way the specialty is practiced and the current and future education model. The aim of this study is to analyze the perception that undergraduate medical students have about the current situation of AI in medicine, especially in radiology. A survey with 17 items was distributed to medical students between 3 January to 31 March 2022. Two hundred and eighty-one students correctly responded the questionnaire; 79.3% of them claimed that they knew what AI is. However, their objective knowledge about AI was low but acceptable. Only 24.9% would choose radiology as a specialty, and only 40% of them as one of their first three options. The applications of this technology were valued positively by most students, who give it an important Support Role, without fear that the radiologist will be replaced by AI (79.7%). The majority (95.7%) agreed with the need to implement well-established ethical principles in AI, and 80% valued academic training in AI positively. Surveyed medical students have a basic understanding of AI and perceive it as a useful tool that will transform radiology.
Collapse
Affiliation(s)
- Andrés Barreiro-Ares
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
| | - Annia Morales-Santiago
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
| | - Francisco Sendra-Portero
- Department of Radiology and Physical Medicine, School of Medicine, University of Malaga, 29010 Málaga, Spain
| | - Miguel Souto-Bayarri
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
| |
Collapse
|
19
|
Tejani AS, Elhalawani H, Moy L, Kohli M, Kahn CE. Artificial Intelligence and Radiology Education. Radiol Artif Intell 2023; 5:e220084. [PMID: 36721409 PMCID: PMC9885376 DOI: 10.1148/ryai.220084] [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: 04/27/2022] [Revised: 09/18/2022] [Accepted: 11/02/2022] [Indexed: 06/18/2023]
Abstract
Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education.
Collapse
|
20
|
Shiang T, Garwood E, Debenedectis CM. Artificial intelligence-based decision support system (AI-DSS) implementation in radiology residency: Introducing residents to AI in the clinical setting. Clin Imaging 2022; 92:32-37. [DOI: 10.1016/j.clinimag.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 09/06/2022] [Accepted: 09/13/2022] [Indexed: 11/28/2022]
|
21
|
Pareek A, Lungren MP, Halabi SS. The requirements for performing artificial-intelligence-related research and model development. Pediatr Radiol 2022; 52:2094-2100. [PMID: 35996023 DOI: 10.1007/s00247-022-05483-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/06/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022]
Abstract
Artificial intelligence research in health care has undergone tremendous growth in the last several years thanks to the explosion of digital health care data and systems that can leverage large amounts of data to learn patterns that can be applied to clinical tasks. In addition, given broad acceleration in machine learning across industries like transportation, media and commerce, there has been a significant growth in demand for machine-learning practitioners such as engineers and data scientists, who have skill sets that can be applied to health care use cases but who simultaneously lack important health care domain expertise. The purpose of this paper is to discuss the requirements of building an artificial-intelligence research enterprise including the research team, technical software/hardware, and procurement and curation of health care data.
Collapse
Affiliation(s)
- Anuj Pareek
- Stanford AIMI Center, Stanford University, 1701 Page Mill Road, Palo Alto, CA, 94304, USA.
| | - Matthew P Lungren
- Stanford AIMI Center, Stanford University, 1701 Page Mill Road, Palo Alto, CA, 94304, USA
| | - Safwan S Halabi
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| |
Collapse
|
22
|
Gowda V, Jordan SG, Awan OA. Artificial Intelligence in Radiology Education: A Longitudinal Approach. Acad Radiol 2022; 29:788-790. [PMID: 34561165 DOI: 10.1016/j.acra.2021.08.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Vrushab Gowda
- Medical Student, UNC School of Medicine, Chapel Hill, North Carolina
| | - Sheryl Gillikin Jordan
- Professor of Radiology, University of North Carolina Chapel Hill, Chapel Hill, North Carolina
| | - Omer A Awan
- Associate Vice Chair of Education, University of Maryland School of Medicine, Baltimore, Maryland.
| |
Collapse
|
23
|
Artificial intelligence and Multidisciplinary Team Meetings; A communication challenge for radiologists' sense of agency and position as spider in a web? Eur J Radiol 2022; 155:110231. [DOI: 10.1016/j.ejrad.2022.110231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 11/19/2022]
|
24
|
Ehrmann D, Harish V, Morgado F, Rosella L, Johnson A, Mema B, Mazwi M. Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care. Front Pediatr 2022; 10:864755. [PMID: 35620143 PMCID: PMC9127438 DOI: 10.3389/fped.2022.864755] [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: 01/28/2022] [Accepted: 04/18/2022] [Indexed: 12/02/2022] Open
Abstract
Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.
Collapse
Affiliation(s)
- Daniel Ehrmann
- Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.,Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Vinyas Harish
- Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.,MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Felipe Morgado
- Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.,MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Laura Rosella
- MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Alistair Johnson
- MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
| | - Briseida Mema
- Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada
| | - Mjaye Mazwi
- Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.,MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
25
|
Rainey C, O'Regan T, Matthew J, Skelton E, Woznitza N, Chu KY, Goodman S, McConnell J, Hughes C, Bond R, McFadden S, Malamateniou C. Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers. Front Digit Health 2021; 3:739327. [PMID: 34859245 PMCID: PMC8631824 DOI: 10.3389/fdgth.2021.739327] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/19/2021] [Indexed: 12/19/2022] Open
Abstract
Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term “AI” was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.
Collapse
Affiliation(s)
- Clare Rainey
- Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, Newtownabbey, United Kingdom
| | - Tracy O'Regan
- The Society and College of Radiographers, London, United Kingdom
| | - Jacqueline Matthew
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom
| | - Emily Skelton
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.,Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, United Kingdom
| | - Nick Woznitza
- University College London Hospitals, London, United Kingdom.,School of Allied and Public Health Professions, Canterbury Christ Church University, Canterbury, United Kingdom
| | - Kwun-Ye Chu
- Department of Oncology, Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United Kingdom.,Radiotherapy Department, Churchill Hospital, Oxford University Hospitals NHS FT, Oxford, United Kingdom
| | - Spencer Goodman
- The Society and College of Radiographers, London, United Kingdom
| | | | - Ciara Hughes
- Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, Newtownabbey, United Kingdom
| | - Raymond Bond
- Faculty of Computing, Engineering and the Built Environment, School of Computing, Ulster University, Newtownabbey, United Kingdom
| | - Sonyia McFadden
- Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, Newtownabbey, United Kingdom
| | - Christina Malamateniou
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.,Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, United Kingdom
| |
Collapse
|
26
|
Li D, Yi PH. Artificial Intelligence in Radiology: A Canadian Environmental Scan. Can Assoc Radiol J 2021; 73:428-429. [PMID: 34569300 DOI: 10.1177/08465371211038940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- David Li
- University of Ottawa Faculty of Medicine, Ottawa, Ontario, Canada
| | - Paul H Yi
- University of Maryland Intelligent Imaging (UMII) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| |
Collapse
|
27
|
Malamateniou C, Knapp KM, Pergola M, Woznitza N, Hardy M. Artificial intelligence in radiography: Where are we now and what does the future hold? Radiography (Lond) 2021; 27 Suppl 1:S58-S62. [PMID: 34380589 DOI: 10.1016/j.radi.2021.07.015] [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: 05/20/2021] [Revised: 07/10/2021] [Accepted: 07/21/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES This paper will outline the status and basic principles of artificial intelligence (AI) in radiography along with some thoughts and suggestions on what the future might hold. While the authors are not always able to separate the current status from future developments in this field, given the speed of innovation in AI, every effort has been made to give a view to the present with projections to the future. KEY FINDINGS AI is increasingly being integrated within radiography and radiographers will increasingly be working with AI based tools in the future. As new AI tools are developed it is essential that robust validation is undertaken in unseen data, supported by more prospective interdisciplinary research. A framework of stronger, more comprehensive approvals are recommended and the involvement of service users, including practitioners, patients and their carers in the design and implementation of AI tools is essential. Clearer accountability and medicolegal frameworks are required in cases of erroneous results from the use of AI-powered software and hardware. Clearer career pathways and role extension provision for healthcare practitioners, including radiographers, are required along with education in this field where AI will be central. CONCLUSION With the current growth rate of AI tools it is expected that many of the applications in medical imaging will continue to develop to more accurate, less expensive and more readily available versions moving from the bench to the bedside. The hope is that, alongside efficiency and increased patient throughput, patient centred care and precision medicine will find their way in, so we will not only deliver a faster, safer, seamless clinical service but also one that will have the patients at its heart. IMPACT FOR PRACTICE AI is already reaching clinical practice in many forms and its presence will continue to increase over the short and long-term future. Radiographers must learn to work with AI, embracing it and maximising the positive outcomes from this new technology.
Collapse
Affiliation(s)
| | | | - M Pergola
- American Society of Radiologic Technologists, NM, USA.
| | - N Woznitza
- University College London Hospitals, UK; Canterbury Christ Church University, UK
| | - M Hardy
- University of Bradford, Bradford, UK
| |
Collapse
|