Artificial Intelligence and the Medical Physicist: Welcome to the Machine
Source abstract
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Radiomics and Machine Learning in Medical Imaging · Artificial Intelligence in Healthcare and Education · Advanced X-ray and CT Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
Medical Physics Unit, Ospedale Santa Chiara APSS, 38122 Trento, Italy
Medical Physics Unit, Istituto Europeo di oncologia IRCCS, 20141 Milan, Italy
Department Biomedical Experimental and Clinical Science “Mario Serio”, University of Florence, 50134 Florence, Italy
Medical Physics Unit, A.O. Ordine Mauriziano di Torino, 10128 Torino, Italy
Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy
References (100+)
Show source-provided references
1. Artificial Intelligence in Surgery: Promises and Perils10.1097/SLA.0000000000002693 ↗
2. An introduction to deep learning in medical physics: Advantages. potential, and challenges10.1088/1361-6560/ab6f51 ↗
3. Artificial intelligence will soon change the landscape of medical physics research and practice10.1002/mp.12831 ↗
4. Why physics in medicine?10.1016/j.ejmp.2019.04.027 ↗
5. Redefining and reinvigorating the role of physics in clinical medicine: A Report from the AAPM Medical Physics 3.0 Ad Hoc Committee10.1002/mp.13087 ↗
6. Biehl, M., Caticha, N., Opper, M., and Villmann, T. (2019, January 24–26). Statistical Physics of Learning and Inference. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
7. Ramezanpour, A., Beam, A.L., Chen, J.H., and Mashaghi, A. (2020). Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms. Diagnostics, 10.10.3390/diagnostics10110972 ↗
8. Artificial intelligence will reduce the need for clinical medical physicists10.1002/acm2.12244 ↗
9. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?10.1016/j.radonc.2018.05.030 ↗
10. Machine and deep learning methods for radiomics10.1002/mp.13678 ↗
11. Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis10.1118/1.2776669 ↗
12. Beyond imaging: The promise of radiomics10.1016/j.ejmp.2017.05.071 ↗
13. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches10.1109/TSMCC.2011.2161285 ↗
14. Sensitivity Analysis in Bayesian Classification Models: Multiplicative Deviations10.1109/TPAMI.1980.4767015 ↗
15. Machine learning in prognosis of the femoral neck fracture recovery10.1016/S0933-3657(96)00351-X ↗
16. Machine Learning and Imaging Informatics in Oncology10.1159/000493575 ↗
17. Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology10.1016/j.jacr.2018.01.028 ↗
18. Towards the development of an error checker for radiotherapy treatment plans: A preliminary study10.1088/0031-9155/52/21/012 ↗
19. Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment10.1118/1.4961746 ↗
20. Visualizing Data using t-SNE
21. Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps10.1109/TMI.2016.2576899 ↗
22. Data mining information from electronic health records produced high yield and accuracy for current smoking status10.1016/j.jclinepi.2019.11.006 ↗
23. From vital signs to clinical outcomes for patients with sepsis: A machine learning basis for a clinical decision support system10.1136/amiajnl-2013-001815 ↗
24. The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement10.1016/j.jmir.2020.01.008 ↗
25. Natural Language Processing in Radiology: A Systematic Review10.1148/radiol.16142770 ↗
26. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review10.1016/j.jbi.2017.07.012 ↗
27. Natural language processing in pathology: A scoping review10.1136/jclinpath-2016-203872 ↗
28. Benke, K., and Benke, G. (2018). Artificial Intelligence and Big Data in Public Health. Int. J. Environ. Res. Public Health, 15.10.3390/ijerph15122796 ↗
29. AI-based applications in hybrid imaging: How to build smart and truly multi-parametric decision models for radiomics10.1007/s00259-019-04414-4 ↗
30. Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance10.1002/mp.13562 ↗
31. User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions10.1016/j.ejmp.2020.01.027 ↗
32. El Naqa, I., Li, R., and Murphy, M.J. (2015). Machine Learning in Radiation Oncology: Theory and Applications, Springer.10.1007/978-3-319-18305-3 ↗
33. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer10.1146/annurev-bioeng-071812-152416 ↗
34. CADx of mammographic masses and clustered microcalcifications: A review10.1118/1.3121511 ↗
35. Chen, C.H., Chang, C.K., Tu, C.Y., Liao, W.C., Wu, B.R., Chou, K.T., Chiou, Y.R., Yang, S.N., Zhang, G., and Huang, T.C. (2018). Radiomic features analysis in computed tomography images of lung nodule classification. PLoS ONE, 13.10.1371/journal.pone.0192002 ↗
36. A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules10.1016/j.crad.2019.07.026 ↗
37. Botta, F., Raimondi, S., Rinaldi, L., Bellerba, F., Corso, F., Bagnardi, V., Origgi, D., Minelli, R., Pitoni, G., and Petrella, F. (2020). Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival. Cancers, 12.10.3390/cancers12061432 ↗
38. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer10.1016/j.lungcan.2019.11.003 ↗
39. Radiomics and deep learning in lung cancer10.1007/s00066-020-01625-9 ↗
40. Prostate MRI radiomics: A systematic review and radiomic quality score assessment10.1016/j.ejrad.2020.109095 ↗
41. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings10.1002/jmri.25983 ↗
42. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images10.1007/s00330-017-5154-8 ↗
43. Radiomics in PET/CT: More Than Meets the Eye?10.2967/jnumed.116.184655 ↗
44. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma
45. Radiomics based detection and characterization of suspicious lesions on full field digital mammograms10.1016/j.cmpb.2018.05.017 ↗
46. Applications and limitations of machine learning in radiation oncology10.1259/bjr.20190001 ↗
47. Lung CT Image Segmentation USING Deep Neural Networks10.1016/j.procs.2018.01.104 ↗
48. Zhong, Z., Kim, Y., Zhou, L., Plichta, K., Allen, B., Buatti, J., and Wu, X. (2018, January 4–7). 3D fully convolutional networks for co-segmentation of tumors on PET-CT images. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.10.1109/ISBI.2018.8363561 ↗
49. A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing10.1002/mp.14131 ↗
50. A deep learning approach to radiation dose estimation10.1088/1361-6560/ab65dc ↗
51. Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: A Pilot Study10.1007/s10278-018-0150-3 ↗
52. Physics-driven learning of x-ray skin dose distribution in interventional procedures10.1002/mp.13758 ↗
53. Potential of a machine-learning model for dose optimization in CT quality assurance10.1007/s00330-019-6013-6 ↗
54. PET Image Denoising Using a Deep Neural Network Through Fine Tuning10.1109/TRPMS.2018.2877644 ↗
55. Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction10.1038/s41598-018-25153-w ↗
56. MR-based synthetic CT generation using a deep convolutional neural network method10.1002/mp.12155 ↗
57. Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium: A Feasibility Study10.1097/RLI.0000000000000583 ↗
58. A survey on deep learning in medical image analysis10.1016/j.media.2017.07.005 ↗
59. Real-time data-driven motion correction in PET10.1186/s40658-018-0240-9 ↗
60. Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma10.18632/oncotarget.7467 ↗
61. Value of texture analysis based on enhanced MRI for predicting an early therapeutic response to transcatheter arterial chemoembolisation combined with high-intensity focused ultrasound treatment in hepatocellular carcinoma10.1016/j.crad.2018.04.013 ↗
62. Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives10.1155/2019/6153041 ↗
63. Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence10.1016/j.radonc.2019.03.032 ↗
64. Improved prediction of outcome in Parkinson’s disease using radiomics analysis of longitudinal DAT SPECT images10.1016/j.nicl.2017.08.021 ↗
65. Artificial intelligence and deep learning in ophthalmology—Present and future (Review)
66. Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center10.1016/j.cmpb.2020.105343 ↗
67. Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images
68. Use of artificial intelligence in imaging in rheumatology—Current status and future perspectives10.1136/rmdopen-2019-001063 ↗
69. Artificial intelligence in digital pathology—New tools for diagnosis and precision oncology10.1038/s41571-019-0252-y ↗
70. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution10.1038/s41467-019-13858-z ↗
71. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence10.1146/annurev-biodatasci-011420-031537 ↗
72. Deep Learning for Computer Vision: A Brief Review10.1155/2018/7068349 ↗
73. Natural Language Processing Technologies in Radiology Research and Clinical Applications10.1148/rg.2016150080 ↗
74. Deep Learning in Neuroradiology10.3174/ajnr.A5543 ↗
75. Recent Development of Augmented Reality in Surgery: A Review
76. Development and evaluation of an online three-level proton vs photon decision support prototype for head and neck cancer—Comparison of dose, toxicity and cost-effectiveness10.1016/j.radonc.2015.12.029 ↗
77. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer10.1016/j.radonc.2017.11.012 ↗
78. Knowledge-based treatment planning: An inter-technique and inter-system feasibility study for prostate cancer10.1016/j.ejmp.2017.03.002 ↗
79. Pareto-optimal plans as ground truth for validation of a commercial system for knowledge-based DVH-prediction10.1016/j.ejmp.2018.11.002 ↗
80. Utilizing knowledge from prior plans in the evaluation of quality assurance10.1088/0031-9155/60/12/4873 ↗
81. Conventional vs machine learning-based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial10.1016/j.brachy.2020.03.004 ↗
82. Three-dimensional dose prediction for lung IMRT patients with deep neural networks: Robust learning from heterogeneous beam configurations10.1002/mp.13597 ↗
83. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture10.1088/1361-6560/ab039b ↗
84. RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy via Deep Learning10.1016/j.ijrobp.2020.04.045 ↗
85. Prediction of skin dose in low-kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry10.1002/mp.13379 ↗
86. Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis10.3389/fonc.2020.00490 ↗
87. Talamonti, C., Piffer, S., Greto, D., Mangoni, M., Ciccarone, A., Dicarolo, P., Fantacci, M.E., Fusi, F., Oliva, P., and Palumbo, L. (2019). Radiomic and Dosiomic Profiling of Paediatric Medulloblastoma Tumours Treated with Intensity Modulated Radiation Therapy. Commun. Comput. Inf. Sci., 56–64.10.1007/978-3-030-29930-9_6 ↗
88. Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer10.1088/1361-6560/ab3247 ↗
89. A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation10.1016/j.ejmp.2016.10.005 ↗
90. Radiomics in radiooncology—Challenging the medical physicist10.1016/j.ejmp.2018.03.012 ↗
91. Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy10.1186/s41824-020-00086-8 ↗
92. Stability of dosomics features extraction on grid resolution and algorithm for radiotherapy dose calculation10.1016/j.ejmp.2020.07.022 ↗
93. Moving beyond quality control in diagnostic radiology and the role of the clinically qualified medical physicist10.1016/j.ejmp.2017.04.007 ↗
94. Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning10.1002/mp.13445 ↗
95. Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy10.1016/j.ejmp.2020.03.022 ↗
96. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: An empirical study10.1111/nyas.13215 ↗
97. Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description10.1002/mp.13433 ↗
98. Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks10.1002/mp.13338 ↗
99. IMRT QA using machine learning: A multi-institutional validation10.1002/acm2.12161 ↗
100. Bizzego, A., Bussola, N., Chierici, M., Maggio, V., Francescatto, M., Cima, L., Cristoforetti, M., Jurman, G., and Furlanello, C. (2019). Evaluating reproducibility of AI algorithms in digital pathology with DAPPER. PLoS Comput. Biol., 15.10.1371/journal.pcbi.1006269 ↗