The radiologist as a physician – artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians – a narrative review

Abstract

Background Large volumes of data increasing over time lead to a shortage of radiologistsʼ time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized for a radiological area. Radiologists must understand the basic features of its technical function in order to be able to assess the weaknesses and possible errors of the system and use the strengths of the system. This “explainability” creates trust in an AI system and shows its limits.

Method Based on an expanded Medline search for the key words “radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations”, subjective additional relevant articles were considered for this narrative review.

Results The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice – this is known as “overfitting”. It should also be noted that there are specific weak points in the systems, so-called “adversarial examples”, which lead to fatal misdiagnoses by the AI even though these cannot be visually distinguished from an unremarkable finding by the radiologist. The user must know which diseases the system is trained for, which organ systems are recognized and taken into account by the AI, and, accordingly, which are not properly assessed. This means that the user can and must critically review the results and adjust the findings if necessary. Correctly applied AI can result in a time savings for the radiologist. If he knows how the system works, he only has to spend a short amount of time checking the results. The time saved can be used for communication with patients and referring physicians and thus contribute to higher job satisfaction.

Conclusion Radiology is a constantly evolving specialty with enormous responsibility, as radiologists often make the diagnosis to be treated. AI-supported systems should be used consistently to provide relief and support. Radiologists need to know the strengths, weaknesses, and areas of application of these AI systems in order to save time. The time gained can be used for communication with patients and referring physicians.

Key Points 

• Explainable AI systems help to improve workflow and to save time.

• The physician must critically review AI results, under consideration of the limitations of the AI.

• The AI system will only provide useful results if it has been adapted to the data type and data origin.

• The communicating radiologist interested in the patient is important for the visibility of the discipline.

Citation Format

• Stueckle CA, Haage P. The radiologist as a physician – artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians – a narrative review. Fortschr Röntgenstr 2024; 196: 1115 – 1123

Keywords

diagnostic radiology - patient interaction - deep learning - artificial intelligence - doctor patient relationship

Publication History

Received: 26 July 2023

Accepted after revision: 27 January 2024

Article published online:
03 April 2024

• References

• 1 Thrall JH, Li X, Li Q. et al. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. Journal of the American College of Radiology: JACR 2018; 15: 504-508
• 2 Langlotz CP. Will Artificial Intelligence Replace Radiologists?. Radiol Artif Intell 2019; 1: e190058
• 3 Liu PR, Lu L, Zhang JY. et al. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci 2021; 41: 1105-1115
• 4 Kelly BS, Judge C, Bollard SM. et al. Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). European radiology 2022; 32: 7998-8007
• 5 Savadjiev P, Chong J, Dohan A. et al. Demystification of AI-driven medical image interpretation: past, present and future. European radiology 2019; 29: 1616-1624
• 6 Poplin R, Varadarajan AV, Blumer K. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2018; 2: 158-164
• 7 Chen P-HC, Liu Y, Peng L. How to develop machine learning models for healthcare. Nat Mater 2019; 18: 410-414
• 8 Feuerecker B, Heimer MM, Geyer T. et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105-114
• 9 Binczyk F, Prazuch W, Bozek P. et al. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021; 10: 1186-1199
• 10 Petrila O, Stefan AE, Gafitanu D. et al. The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies – A Systematic Review. Diagnostics (Basel) 2023; 13
• 11 Hussain Z, Gimenez F, Yi D. et al. Differential Data Augmentation Techniques for Medical Imaging Classification Tasks. AMIA Annu Symp Proc 2017; 2017: 979-984
• 12 Bundy A, Crowcroft J, Ghahramani Z. et al. Explainable AI: the basics. In. London: The royal society; 2019: 29
• 13 Phillips P, Hahn C, Fontana PYA. et al. Four Principles of Explainable Artificial Intelligence. Interagency or Internal Report 8312 2021.
• 14 Bradshaw TJ, Huemann Z, Hu J. et al. A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging. Radiol Artif Intell 2023; 5: e220232
• 15 Moassefi M, Rouzrokh P, Conte GM. et al. Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review. J Digit Imaging 2023;
• 16 DeGrave AJ, Janizek JD, Lee SI. AI for radiographic COVID-19 detection selects shortcuts over signal. medRxiv 2020;
• 17 Tibshirani R. Regression Shrinkage and Selection via the Lasso. JSTOR 1996; 58: 267-288
• 18 Seyyed-Kalantari L, Zhang H, McDermott MBA. et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med 2021; 27: 2176-2182
• 19 Kuhn R. Explainability, Verification, and Validation for Assured Autonomy and AI. In; 2022
• 20 Hedström A, Weber L, Bareeva D. et al. Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. Journal of Machine Learning Research 2023; 24: 1-11
• 21 Wang Q, Liu Q, Luo G. et al. Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study. BMC Med Inform Decis Mak 2020; 20: 317
• 22 Moses DA. Deep learning applied to automatic disease detection using chest X-rays. J Med Imaging Radiat Oncol 2021; 65: 498-517
• 23 Wang X, Yang S, Lan J. et al. Automatic Segmentation of Pneumothorax in Chest Radiographs Based on a Two-Stage Deep Learning Method. IEEE Transactions on Cognitive and Developmental Systems 2022; 14: 205-218
• 24 Baltazar LR, Manzanillo MG, Gaudillo J. et al. Artificial intelligence on COVID-19 pneumonia detection using chest xray images. PloS one 2021; 16: e0257884
• 25 Dey S, Bhattacharya R, Malakar S. et al. Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection. Comput Biol Med 2021; 135: 104585
• 26 Nasiri H, Alavi SA. A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images. Comput Intell Neurosci 2022; 2022: 4694567
• 27 Mongan J, Kalpathy-Cramer J, Flanders A. et al. RSNA-MICCAI Panel Discussion: Machine Learning for Radiology from Challenges to Clinical Applications. Radiol Artif Intell 2021; 3: e210118
• 28 Pooler BD, Garrett JW, Southard AM. et al. Technical Adequacy of Fully Automated Artificial Intelligence Body Composition Tools: Assessment in a Heterogeneous Sample of External CT Examinations. Am J Roentgenol American journal of roentgenology 2023; 221: 124-134
• 29 Szegedy CZW, Sutskever I. Intriguing properties of neural networks. In. arXiv:1312.6199: Google; 2013
• 30 Li D, Hu L, Peng X. et al. A proposed artificial intelligence workflow to address application challenges leveraged on algorithm uncertainty. iScience 2022; 25: 103961
• 31 Erdogan N, Imamoglu H, Gorkem SB. et al. Preferences of referring physicians regarding the role of radiologists as direct communicators of test results. Diagn Interv Radiol 2017; 23: 81-85
• 32 Dalla PalmaL, Stacul F, Meduri S. et al. Relationships between radiologists and clinicians: results from three surveys. Clin Radiol 2000; 55: 602-605
• 33 Cabarrus M, Naeger DM, Rybkin A. et al. Patients Prefer Results From the Ordering Provider and Access to Their Radiology Reports. Journal of the American College of Radiology: JACR 2015; 12: 556-562
• 34 Dendl LM, Teufel A, Schleder S. et al. Analysis of Radiological Case Presentations and their Impact on Therapy and Treatment Concepts in Internal Medicine. Fortschr Röntgenstr 2017; 189: 239-246
• 35 Stueckle CA, Talarczyk S, Hackert B. et al. [Patient satisfaction with radiologists in private practice]. Der Radiologe 2020; 60: 70-76
• 36 Reiner BI. Strategies for radiology reporting and communication part 3: patient communication and education. J Digit Imaging 2013; 26: 995-1000
• 37 Rosenkrantz AB, Pysarenko K. The Patient Experience in Radiology: Observations From Over 3,500 Patient Feedback Reports in a Single Institution. Journal of the American College of Radiology: JACR 2016; 13: 1371-1377
• 38 European Society of R. The identity and role of the radiologist in 2020: a survey among ESR full radiologist members. Insights Imaging 2020; 11: 130
• 39 Kemp JL, Mahoney MC, Mathews VP. et al. Patient-centered Radiology: Where Are We, Where Do We Want to Be, and How Do We Get There?. Radiology 2017; 285: 601-608
• 40 Flemming DJ, Gunderman RB. Should We Think of Radiologists as Nonclinicians?. Journal of the American College of Radiology: JACR 2016; 13: 875-877
• 41 Hardy M, Snaith B, Scally A. The impact of immediate reporting on interpretive discrepancies and patient referral pathways within the emergency department: a randomised controlled trial. Br J Radiol 2013; 86: 20120112
• 42 Stueckle CA, Hackert B, Talarczyk S. et al. The physician as a success determining factor in CT-guided pain therapy. BMC Med Imaging 2021; 21: 11
• 43 Bingel U, Wanigasekera V, Wiech K. et al. The effect of treatment expectation on drug efficacy: imaging the analgesic benefit of the opioid remifentanil. Sci Transl Med 2011; 3: 70ra14
• 44 Sinke C, Schmidt K, Forkmann K. et al. Expectation influences the interruptive function of pain: Behavioural and neural findings. European journal of pain 2017; 21: 343-356
• 45 Berkefeld J. Vaskuläre Zufallsbefunde in der MRT des Schädels. Radiologie up2date 2022; 22: 301-317
• 46 Espeland A, Baerheim A. General practitioners’ views on radiology reports of plain radiography for back pain. Scand J Prim Health Care 2007; 25: 15-19