The use of artificial intelligence in radiology: new possibilities for diagnostic imaging. A literature review
DOI:
https://doi.org/10.12775/QS.2024.16.52215Keywords
Artificial Intelligence, medical imaging, radiology, deep learning, diagnostic algorithmsAbstract
Introduction and purpose: Rapid advances in technology enable innovative solutions to be implemented in modern medicine, relieving healthcare workers by speeding up diagnosis and improving the quality of treatment. The subject of this review is Artificial Intelligence (AI), an innovative form of help in the daily practice of doctors. It offers the opportunity to relieve healthcare workers by accelerating the diagnosis process and improving the quality and effectiveness of treatment.
The aim of this paper is to present the current state of knowledge, assess the effectiveness of algorithms in the recognition and interpretation of abnormalities in medical images compared to specialists in radiology and discuss the challenges associated with the implementation of AI in various medical specialties.
Brief description of the state of knowledge: Artificial intelligence, especially through machine learning and deep learning techniques, has found wide application in radiology. Many facilities around the world use advanced AI systems in the day-to-day work of radiologists, including the Mayo Clinic, Massachusetts General Hospital, and the University of Tokyo Hospital.
Summary: Studies show that while AI algorithms can perform worse than radiologists in some areas, they are at the forefront of others, especially in detecting subtle abnormalities. The effective implementation of artificial intelligence requires addressing regulatory, ethical, and training issues. Despite these challenges, artificial intelligence has the potential to play a key role in the future of radiology and revolutionize medical practice, opening new perspectives and improving the quality of health care.
References
McCarthy J, Minsky ML, Rochester N, Shannon CE. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence 1955. AI Magazine. 2006; 27(4); 12. https://doi.org/10.1609/aimag.v27i4.1904.
Moawad AW, Fuentes DT, ElBanan MG, Shalaby AS, Guccione J, Kamel S, Jensen CT, Elsayes KM. Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities. J Comput Assist Tomogr. 2022; 46(1): 78-90. doi: 10.1097/RCT.0000000000001247.
Pianykh OS, Langs G, Dewey M, Enzmann DR, Herold CJ, Schoenberg SO, Brink JA. Continuous Learning AI in Radiology: Implementation Principles and Early Applications. Radiology. 2020; 297(1): 6-14. doi: 10.1148/radiol.2020200038.
Miller DD, Brown EW. How Cognitive Machines Can Augment Medical Imaging. AJR Am J Roentgenol. 2019; 212(1): 9-14. doi: 10.2214/AJR.18.19914.
Hosny A, Parmar C, Quackenbush J, Schwarz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018; 18(8): 500-510
doi: 10.1038/s41568-018-0016-5.
Shelmerdine SC, Martin H, Shirodkar K, Shamshuddin S, Weir-McCall JR; FRCR-AI Study Collaborators. Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study. BMJ. 2022; 379: e072826. doi: 10.1136/bmj-2022-072826.
Zhou Y, Ong H, Kennedy P, Wu CC, Kazam J, Hentel K, Flanders A, Shih G, Peng Y. Evaluating GPT-V4 (GPT-4 with Vision) on Detection of Radiologic Findings on Chest Radiographs. Radiology. 2024; 311(2): e233270. doi: 10.1148/radiol.233270.
Kwee TC, Yakar D, Sluijter TE, Pennings JP, Roest C. Can we revolutionize diagnostic imaging by keeping Pandora's box closed? Br J Radiol. 2023; 96(1152): 20230505. doi: 10.1259/bjr.20230505.
Lumbreras B, Donat L, Hernández-Aguado I. Incidental findings in imaging diagnostic tests: a systematic review. Br J Radiol. 2010; 83(988): 276-89. doi: 10.1259/bjr/98067945.
Elendu C, Amaechi DC, Elendu TC, Jingwa KA, Okoye OK, John Okah M, Ladele JA, Farah AH, Alimi HA. Ethical implications of AI and robotics in healthcare: A review. Medicine (Baltimore). 2023; 102(50): e36671. doi: 10.1097/MD.0000000000036671.
Suresh H, Guttag J. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. EAAMO. 2021; 1-9. doi: 10.1145/3465416.3483305.
Currie G. Intelligent Imaging: Anatomy of Machine Learning and Deep Learning. J Nucl Med Technol. 2019; 47(4): 273-281. doi: 10.2967/jnmt.119.232470.
McMillan AB. Making Your AI Smarter: Continuous Learning Artificial Intelligence for Radiology. Radiology. 2020; 297(1): 15-16. doi: 10.1148/radiol.2020202664.
Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, rink J. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol. 2018; 15(3 Pt B): 504-508. doi: 10.1016/j.jacr.2017.12.026.
Duong MT, Rauschecker AM, Rudie JD, Chen PH, Cook TS, Bryan RN, Mohan S. Artificial intelligence for precision education in radiology. Br J Radiol. 2019; 92(1103): 20190389. doi: 10.1259/bjr.20190389.
Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019; 8(7): 2328-2331. doi: 10.4103/jfmpc.jfmpc_440_19.
Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019; 28(2): 73-81. doi: 10.1080/13645706.2019.1575882.
Bizzo BC, Almeida RR, Alkasab TK. Artificial Intelligence Enabling Radiology Reporting. Radiol Clin North Am. 2021; 59(6): 1045-1052. doi: 10.1016/j.rcl.2021.07.004.
Gore JC. Artificial intelligence in medical imaging. Magn Reson Imaging. 2020; 68: A1-A4. doi: 10.1016/j.mri.2019.12.006.
Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, Omerzu T, Laird JR, Khanna NN, Mavrogeni S, Protogerou A, Sfikakis PP, Viswanathan V, Kitas GD, Nicolaides A, Gupta A, Suri JS. The present and future of deep learning in radiology. Eur J Radiol. 2019; 114: 14-24. doi: 10.1016/j.ejrad.2019.02.038.
Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2019; 49(4): 939-954. doi: 10.1002/jmri.26534.
Giger ML. Machine Learning in Medical Imaging. J Am Coll Radiol. 2018; 15(3 Pt B): 512-520. doi: 10.1016/j.jacr.2017.12.028.
A revolution in radiology: The groundbreaking application of GPT-4 in radiology. 2023. Polish. https://aioai.pl/rewolucja-w-radiologii-przelomowe-zastosowanie-gpt-4-w-radiologii/ (Access: 27.03.2024).
Wichmann JL, Willemink MJ, De Cecco CN. Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation. Invest Radiol. 2020; 55(9): 619-627. doi: 10.1097/RLI.0000000000000673.
Kelly BS, Judge C, Bollard SM, Clifford SM, Healy GM, Aziz A, Mathur P, Islam S, Yeom KW, Lawlor A, Killeen RP. Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol. 2022; 32(11): 7998-8007. doi: 10.1007/s00330-022-08784-6.
Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiat Sci. 2019; 50(4): 477-487. doi: 10.1016/j.jmir.2019.09.005.
Pouly M, Koller T, Gottfrois P, Lionetti S. Artificial intelligence in image analysis-fundamentals and new developments. Hautarzt. 2020; 71(9): 660-668. German. doi: 10.1007/s00105-020-04663-7.
Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D'Amico NC, Sardanelli F. AI applications to medical images: From machine learning to deep learning. Phys Med. 2021; 83: 9-24. doi: 10.1016/j.ejmp.2021.02.006.
Prinzi F, Currieri T, Gaglio S, Vitabile S. Shallow and deep learning classifiers in medical image analysis. Eur Radiol Exp. 2024; 8(1): 26. doi: 10.1186/s41747-024-00428-2.
Radanliev P, Santos O, Brandon-Jones A, Joinson A. Ethics and responsible AI deployment. Front Artif Intell. 2024; 7: 1377011. doi: 10.3389/frai.2024.1377011. PMID: 38601110; PMCID: PMC11004481.
Wang F, Preininger A. AI in Health: State of the Art, Challenges, and Future Directions. Yearb Med Inform. 2019; 28(1): 16-26. doi: 10.1055/s-0039-1677908. Epub 2019 Aug 16. PMID: 31419814; PMCID: PMC6697503.
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Copyright (c) 2024 Weronika Stawska, Magdalena Miłek, Ksenia Kwaśniak, Angelika Foryś, Mariola Banach, Anna Niemczyk, Monika Ślusarczyk, Agata Magierska, Zuzanna Kotowicz, Weronika Kmiotek
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