Familiarity, confidence and preference of artificial intelligence feedback and prompts by Australian breast cancer screening readers
Phuong Dung (Yun) Trieu
A * , Melissa L. Barron A , Zhengqiang Jiang A , Seyedamir Tavakoli Taba A , Ziba Gandomkar A and Sarah J. Lewis A B
A
B
Abstract
This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection.
Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants’ responses to questions were compared using Pearson’s χ2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons.
Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were ‘Displaying suspicious areas on mammograms with the percentage of cancer possibility’ (67.8%) and ‘Automatic mammogram classification (normal, benign, cancer, uncertain)’ (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying ‘somewhat happy’ to ‘extremely happy’) over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001).
The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike.
Keywords: artificial intelligence, breast cancer, clinical application, early detection, mammography, radiology, screening, survey.
References
2 Nyström L, Bjurstam N, Jonsson H, Zackrisson S, Frisell J. Reduced breast cancer mortality after 20+ years of follow-up in the Swedish randomized controlled mammography trials in Malmo, Stockholm, and Goteborg. J Med Screen 2017; 24(1): 34-42.
| Crossref | Google Scholar | PubMed |
3 Saadatmand S, Bretveld R, Siesling S, Tilanus-Linthorst MM. Influence of tumour stage at breast cancer detection on survival in modern times: population based study in 173,797 patients. BMJ 2015; 351: h4901.
| Crossref | Google Scholar | PubMed |
4 Ahn S, Wooster M, Valente C, Moshier E, Meng R, Pisapati K, et al. Impact of Screening Mammography on Treatment in Women Diagnosed with Breast Cancer. Ann Surg Oncol 2018; 25(10): 2979-86.
| Crossref | Google Scholar | PubMed |
5 Kemp Jacobsen K, O’Meara ES, Key D, S M Buist D, Kerlikowske K, Vejborg I, et al. Comparing sensitivity and specificity of screening mammography in the United States and Denmark. Int J Cancer 2015; 137(9): 2198-207.
| Crossref | Google Scholar | PubMed |
6 Bolejko A, Hagell P, Wann-Hansson C, Zackrisson S. Prevalence, Long-term Development, and Predictors of Psychosocial Consequences of False-Positive Mammography among Women Attending Population-Based Screening. Cancer Epidemiol Biomarkers Prev 2015; 24(9): 1388-97.
| Crossref | Google Scholar | PubMed |
7 Nickson C, Velentzis LS, Brennan P, Mann GB, Houssami N. Improving breast cancer screening in Australia: a public health perspective. Public Health Res Pract 2019; 29(2): e2921911.
| Crossref | Google Scholar | PubMed |
8 Yoon JH, Strand F, Baltzer PAT, Conant EF, Gilbert FJ, Lehman CD, et al. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis. Radiology 2023; 307: 222639.
| Crossref | Google Scholar |
9 Shah SM, Khan RA, Arif S, Sajid U. Artificial intelligence for breast cancer analysis: Trends & directions. Comput Biol Med 2022; 142: 105221.
| Crossref | Google Scholar | PubMed |
10 European Society of Radiology (ESR).. Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 2019; 10(1): 105.
| Crossref | Google Scholar | PubMed |
11 Batchu S, Liu F, Amireh A, Waller J, Umair M. A Review of Applications of Machine Learning in Mammography and Future Challenges. Oncology 2021; 99(8): 483-90.
| Crossref | Google Scholar | PubMed |
12 van Leeuwen KG, Schalekamp S, Rutten M, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 2021; 31(6): 3797-804.
| Crossref | Google Scholar | PubMed |
13 Imaging Techonology News. Global Diagnostics Australia Incorporates AI Into Radiology Applications. 2019. Available at https://www.itnonline.com/content/global-diagnostics-australia-incorporates-ai-radiology-applications
14 Fenton JJ, Taplin SH, Carney PA, Abraham L, Sickles EA, D’Orsi C, et al. Influence of computer-aided detection on performance of screening mammography. N Engl J Med 2007; 356(14): 1399-409.
| Crossref | Google Scholar | PubMed |
15 Potnis KC, Ross JS, Aneja S, Gross CP, Richman IB. Artificial Intelligence in Breast Cancer Screening: Evaluation of FDA Device Regulation and Future Recommendations. JAMA Intern Med 2022; 182(12): 1306-12.
| Crossref | Google Scholar | PubMed |
16 Taylor-Phillips S, Seedat F, Kijauskaite G, Marshall J, Halligan S, Hyde C, et al. UK National Screening Committee’s approach to reviewing evidence on artificial intelligence in breast cancer screening. Lancet Digit Health 2022; 4(7): e558-65.
| Crossref | Google Scholar | PubMed |
17 Freeman K, Geppert J, Stinton C, Todkill D, Johnson S, Clarke A, et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ 2021; 374: n1872.
| Crossref | Google Scholar | PubMed |
18 Lewis SJ, Borecky N, Li T, Barron ML, Brennan P, Trieu PD. Radiologist Self-training: a Study of Cancer Detection when Reading Mammograms at Work Clinics or Workshops. J Cancer Educ 2022; 38: 571-7.
| Crossref | Google Scholar | PubMed |
19 Trieu P, Tapia K, Frazer H, Lee W, Brennan P. Improvement of Cancer Detection on Mammograms via BREAST Test Sets. Acad Radiol 2019; 26: e341-7.
| Crossref | Google Scholar | PubMed |
21 European Society of Radiology (ESR).. Current practical experience with artificial intelligence in clinical radiology: a survey of the European Society of Radiology. Insights Imaging 2022; 13(1): 107.
| Crossref | Google Scholar | PubMed |
22 Janda M, Soyer HP. Can clinical decision making be enhanced by artificial intelligence? Br J Dermatol 2019; 180(2): 247-8.
| Crossref | Google Scholar | PubMed |
24 Pacilè S, Lopez J, Chone P, Bertinotti T, Grouin JM, Fillard P. Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool. Radiol Artif Intell 2020; 2(6): e190208.
| Crossref | Google Scholar | PubMed |
25 Choi WJ, An JK, Woo JJ, Kwak HY. Comparison of Diagnostic Performance in Mammography Assessment: Radiologist with Reference to Clinical Information Versus Standalone Artificial Intelligence Detection. Diagnostics 2022; 13(1): 117.
| Crossref | Google Scholar | PubMed |
26 Fu Q, Dong H. Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection. Entropy 2022; 24(11): 1543.
| Crossref | Google Scholar | PubMed |
27 Chakraborty DP. Clinical relevance of the ROC and free-response paradigms for comparing imaging system efficacies. Radiat Prot Dosim 2010; 139(1–3): 37-41.
| Crossref | Google Scholar | PubMed |
28 Trieu PDY, Mello-Thoms CR, Barron ML, Lewis SJ. Look how far we have come: BREAST cancer detection education on the international stage. Front Oncol 2022; 12: 1023714.
| Crossref | Google Scholar | PubMed |
29 de Vries CF, Colosimo SJ, Boyle M, Lip G, Anderson LA, Staff RT, et al. AI in breast screening mammography: breast screening readers’ perspectives. Insights Imaging 2022; 13(1): 186.
| Crossref | Google Scholar | PubMed |
30 Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 2016; 375(13): 1216-9.
| Crossref | Google Scholar | PubMed |
31 Pesapane F, Tantrige P, Patella F, Biondetti P, Nicosia L, Ianniello A, et al. Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists. Med Oncol 2020; 37(5): 40.
| Crossref | Google Scholar | PubMed |
32 Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018; 2(1): 35.
| Crossref | Google Scholar | PubMed |
33 Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto de Santos D, et al. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol 2021; 31(9): 7058-66.
| Crossref | Google Scholar | PubMed |
34 Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C, et al. Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey. Radiol Med 2021; 126(1): 63-71.
| Crossref | Google Scholar | PubMed |
35 Youk JH, Kim EK. Research Highlight: Artificial Intelligence for Ruling Out Negative Examinations in Screening Breast MRI. Korean J Radiol 2022; 23(2): 153-5.
| Crossref | Google Scholar | PubMed |
36 Verburg E, van Gils CH, van der Velden BHM, Bakker MF, Pijnappel RM, Veldhuis WB, et al. Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial. Radiology 2022; 302(1): 29-36.
| Crossref | Google Scholar | PubMed |
37 Anderson AW, Marinovich ML, Houssami N, Lowry KP, Elmore JG, Buist DSM, et al. Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review. J Am Coll Radiol 2022; 19(2 Pt A): 259-73.
| Crossref | Google Scholar | PubMed |
38 Royal Australian and New Zealand College of Radiologists. RANZCR Position Statement on the Regulation of Artificial Intelligence in Medicine. 2022. Available at https://www.ranzcr.com/college/document-library/ranzcr-position-statement-on-the-regulation-of-artificial-intelligence-in-medicine
40 Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL, et al. Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement. Can Assoc Radiol J 2019; 70(4): 329-34.
| Crossref | Google Scholar | PubMed |
41 Kenny LM, Nevin M, Fitzpatrick K. Ethics and standards in the use of artificial intelligence in medicine on behalf of the Royal Australian and New Zealand College of Radiologists. J Med Imaging Radiat Oncol 2021; 65(5): 486-94.
| Crossref | Google Scholar | PubMed |
