Screening participants’ attitudes to the introduction of artificial intelligence in breast screening

Vries, Clarisse F. de; Morrissey, Brian; Duggan, Donna; Staff, Roger T.; Lip, Gerald

doi:10.1177/09691413211001405

Cited by 10 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results indicate that most users are likely to accept autonomous screening if there is a well-established, efficient process for follow-up with a clinician if needed. This agrees with studies to date indicating that people are more accepting of intelligent systems working symbiotically with physicians or HCPs [24,39] but remain ambivalent about those that function independently [25].…”

Section: Principal Findingssupporting

confidence: 90%

Acceptability of Automated Robotic Clinical Breast Examination: Survey Study

Jenkinson¹,

Houghton²,

Zalk³

et al. 2023

J Particip Med

View full text Add to dashboard Cite

Background In the United Kingdom, women aged 50 to 70 years are invited to undergo mammography. However, 10% of invasive breast cancers occur in women aged ≤45 years, representing an unmet need for young women. Identifying a suitable screening modality for this population is challenging; mammography is insufficiently sensitive, whereas alternative diagnostic methods are invasive or costly. Robotic clinical breast examination (R-CBE)—using soft robotic technology and machine learning for fully automated clinical breast examination—is a theoretically promising screening modality with early prototypes under development. Understanding the perspectives of potential users and partnering with patients in the design process from the outset is essential for ensuring the patient-centered design and implementation of this technology. Objective This study investigated the attitudes and perspectives of women regarding the use of soft robotics and intelligent systems in breast cancer screening. It aimed to determine whether such technology is theoretically acceptable to potential users and identify aspects of the technology and implementation system that are priorities for patients, allowing these to be integrated into technology design. Methods This study used a mixed methods design. We conducted a 30-minute web-based survey with 155 women in the United Kingdom. The survey comprised an overview of the proposed concept followed by 5 open-ended questions and 17 closed questions. Respondents were recruited through a web-based survey linked to the Cancer Research United Kingdom patient involvement opportunities web page and distributed through research networks’ mailing lists. Qualitative data generated via the open-ended questions were analyzed using thematic analysis. Quantitative data were analyzed using 2-sample Kolmogorov-Smirnov tests, 1-tailed t tests, and Pearson coefficients. Results Most respondents (143/155, 92.3%) indicated that they would definitely or probably use R-CBE, with 82.6% (128/155) willing to be examined for up to 15 minutes. The most popular location for R-CBE was at a primary care setting, whereas the most accepted method for receiving the results was an on-screen display (with an option to print information) immediately after the examination. Thematic analysis of free-text responses identified the following 7 themes: women perceive that R-CBE has the potential to address limitations in current screening services; R-CBE may facilitate increased user choice and autonomy; ethical motivations for supporting R-CBE development; accuracy (and users’ perceptions of accuracy) is essential; results management with clear communication is a priority for users; device usability is important; and integration with health services is key. Conclusions There is a high potential for the acceptance of R-CBE in its target user group and a high concordance between user expectations and technological feasibility. Early patient participation in the design process allowed the authors to identify key development priorities for ensuring that this new technology meets the needs of users. Ongoing patient and public involvement at each development stage is essential.

show abstract

Section: Principal Findingssupporting

confidence: 90%

Acceptability of Automated Robotic Clinical Breast Examination: Survey Study

Jenkinson¹,

Houghton²,

Zalk³

et al. 2023

J Particip Med

View full text Add to dashboard Cite

show abstract

“…4 Moreover, AI introduction in breast cancer screening mammogram interpretation could be essential even in high-income countries to face the current (and the expected) shortage of radiologists which is increasingly putting breast cancer screening under strain. 16 Finally, while radiologists' performance tends to decrease after 70 or 80 min of reading, 17 AI never gets tired and has consistent performance. Mammography screening supported by AI can help to reduce the radiologists' overload of work, decreasing the increasing burnout rate and making radiologists less anxious during their shifts.…”

Section: Discussionmentioning

confidence: 97%

Women’s perceptions and attitudes to the use of AI in breast cancer screening: a survey in a cancer referral centre

Pesapane

Rotili

Valconi³

et al. 2022

The British Journal of Radiology

View full text Add to dashboard Cite

Objective: Although breast cancer screening can benefit from Artificial Intelligence (AI), it is still unknown if, to which extent or under which conditions, the use of AI is going to be accepted by the general population. The aim of our study is to evaluate what the females who are eligible for breast cancer screening know about AI and how they perceive such innovation. Methods: We used a prospective survey consisting of a 11-multiple-choice questionnaire evaluating statistical associations with Chi-Square-test or Fisher-exact-test. Multinomial-logistic-regression was performed on items with more than two response categories. Odds ratio (OR) with 95% CI were computed to estimate the probability of a specific response according to patient’s characteristics. Results: In the 800 analysed questionnaires, 51% of respondents confirmed to have knowledge of AI. Of these, 88% expressed a positive opinion about its use in medicine. Non-Italian respondents were associated with the belief of having a deep awareness about AI more often than Italian respondents (OR = 1.91;95% CI[1.10–3.33]). Higher education level was associated with better opinions on the use of AI in medicine (OR = 4.69;95% CI[1.36–16.12]). According to 94% of respondents, the radiologists should always produce their own report on mammograms, whilst 77% agreed that AI should be used as a second reader. Most respondents (52%) considered that both the software developer and the radiologist should be held accountable for AI errors. Conclusions: Most of the females undergoing screening in our Institute approve the introduction of AI, although only as a support to radiologist, and not in substitution thereof. Yet, accountability in case of AI errors is still unsolved. advances in knowledge: This survey may be considered as a pilot-study for the development of large-scale studies to understand females’s demands and concerns about AI applications in breast cancer screening.

show abstract

“…When performance of AI in mammography will be assessed in real practice medicine (most of the studies published in literature are retrospective and they may not represent the clinical practice in real-world) [3], this technology may be the solution for accessing reliable breast cancer screening in low-/middle-income countries where cancer screening is limited due to equipment cost and the expert skill required for interpretation of mammography, and it may help reduce existing health inequalities [11,13]. The introduction of AI in breast cancer screening mammogram interpretation could be essential even in high-income countries to face the current (and the expected) shortage of radiologists [17].…”

Section: Why Do We Need Ai In Mammography?mentioning

confidence: 99%

“…Overall, the most acceptable approach for the interviewed women is currently the combination of a radiologist as a first reader and an AI system as a second reader. Specifically, women need to be fully informed about the use of AI in healthcare, and they want to retain human interaction in the diagnostic process [17].…”

Section: Patients' Perceptionsmentioning

confidence: 99%

Mammography in 2022, from Computer-Aided Detection to Artificial Intelligence Applications

Pesapane¹,

Trentin²,

Montesano³

et al. 2022

Clin. Exp. Obstet. Gynecol.

View full text Add to dashboard Cite

Objective: During the last decades, advances in computing power, structured data, and algorithm development, developed a technology based on Artificial intelligence (AI) which is currently applied in medicine. Nowadays, the main use of AI in breast imaging is in decision support in mammography, where it facilitates human decision-making as opposed to replacing radiologists. In this paper, we analyze how AI is currently involved in radiological decision-making and how will change both interpretation efficacy and workflow efficiency in breast imaging. Mechanism: We performed a non-systematic review on Pubmed and Scopus and Web of Science electronic databases from January 2001 to January 2022, using the following keywords: artificial intelligence, machine and deep learning, breast imaging and mammography. Findings in Brief: Many retrospective studies showed that AI can match or even enhance performances of radiologists in mammography interpretation. However, to assess the real role of AI in clinical practice compelling evidence from accurate perspective studies in large cohorts is needed. Breast imaging must face with the exponential growth in imaging requests (and consequently higher costs) and a predicted reduced number of trained radiologists to read imaging and provide reports. To mitigate these urges, solution is being sought with increasing investments in the application of AI to improve the radiology workflow efficiency as well as patient outcomes. Conslusions: This paper show the background on the evolution and the application of AI in breast imaging in 2022, in addition to exploring advantages and limitations of this innovative technology, as well as ethical and legal issues that have been identified so far.

show abstract

Screening participants’ attitudes to the introduction of artificial intelligence in breast screening

Cited by 10 publications

References 5 publications

Acceptability of Automated Robotic Clinical Breast Examination: Survey Study

Acceptability of Automated Robotic Clinical Breast Examination: Survey Study

Women’s perceptions and attitudes to the use of AI in breast cancer screening: a survey in a cancer referral centre

Mammography in 2022, from Computer-Aided Detection to Artificial Intelligence Applications

Contact Info

Product

Resources

About