To analyze the methodological quality of studies on deep learning (DL) in rib fracture imaging with the Must AI Criteria-10 (MAIC-10) checklist, and to report insights and experiences regarding the applicability of the MAIC-10 checklist. An electronic literature search was conducted on the PubMed database. After selection of articles, three radiologists independently rated the articles according to MAIC-10. Differences of the MAIC-10 score for each checklist item were assessed using the Fleiss' kappa coefficient. A total of 25 original articles discussing DL applications in rib fracture imaging were identified. Most studies focused on fracture detection (n = 21, 84%). In most of the research papers, internal cross-validation of the dataset was performed (n = 16, 64%), while only six studies (24%) conducted external validation. The mean MAIC-10 score of the 25 studies was 5.63 (SD, 1.84; range 1-8), with the item "clinical need" being reported most consistently (100%) and the item "study design" being most frequently reported incompletely (94.8%). The average inter-rater agreement for the MAIC-10 score was 0.771. The MAIC-10 checklist is a valid tool for assessing the quality of AI research in medical imaging with good inter-rater agreement. With regard to rib fracture imaging, items such as "study design", "explainability", and "transparency" were often not comprehensively addressed. AI in medical imaging has become increasingly common. Therefore, quality control systems of published literature such as the MAIC-10 checklist are needed to ensure high quality research output. Quality control systems are needed for research on AI in medical imaging. The MAIC-10 checklist is a valid tool to assess AI in medical imaging research quality. Checklist items such as "study design", "explainability", and "transparency" are frequently addressed incomprehensively.

Artificial intelligence (AI) has been growing in the field of medical imaging and clinical practice. It is essential to comprehend the perceptions, experiences, and expectations regarding AI implementation among medical radiation technologists (MRTs) working in radiology, nuclear medicine, and radiotherapy. Some global studies tend to inform about AI implementation, but there is almost no information from Central and South American professionals. This study aimed to understand the perceptions of the impact of AI on the MRTs, as well as the varying experiences and expectations these professionals have regarding its implementation. An online survey was conducted among Central and South American MRTs for the collection of qualitative data concerning the primary perceptions regarding the implementation of AI in radiology, nuclear medicine, and radiotherapy. The analysis considered descriptive statistics in closed-ended questions and dimension codification for open-ended responses. A total of 398 valid responses were obtained, and it was determined that 98.5 % (n = 392) of the respondents agreed with the implementation of AI in clinical practice. The primary contributions of AI that were identified were the optimization of processes, greater diagnostic accuracy, and the possibility of job expansion. On the other hand, concerns were raised regarding the delay in providing training opportunities and limited avenues for learning in this domain, the displacement of roles, and dehumanization in clinical practice. This sample of participants likely represents mostly professionals who have more AI knowledge than others. It is therefore important to interpret these results with caution. Our findings indicate strong professional confidence in AI's capacity to improve imaging quality while maintaining patient safety standards. However, user resistance may disturb implementation efforts. Our results highlight the dual need for (a) comprehensive professional training programs and (b) user education initiatives that demonstrate AI's clinical value in radiology. We therefore recommend a carefully structured, phased AI implementation approach, guided by evidence-based guidelines and validated training protocols from existing research. AI is already present in medical imaging, but its effective implementations depend on building acceptance and trust through education and training, enabling MRTs to use it safely for patient benefit.

Artificial Intelligence (AI) is rapidly being implemented into clinical practice to improve diagnostic accuracy and reduce provider burnout. However, patient self-perceived knowledge and perceptions of AI's role in their care remain unclear. This study aims to explore patient preferences regarding the use of and communication of AI in their care for patients undergoing cross-sectional imaging exams. This single-center cross-sectional study, a structured questionnaire recruited patients undergoing outpatient CT or MRI examinations between June and July 2024 to assess baseline self-perceived knowledge of AI, perspectives on AI in clinical care, preferences regarding AI-generated results, and economic considerations related to AI, using Likert scales and categorical questions. A total of 226 participants (143 females; mean age 53 years) were surveyed with 67.4% (151/224) reporting having minimal to no knowledge of AI in medicine, with lower knowledge levels associated with lower socioeconomic status (p < .001). 90.3% (204/226) believed they should be informed about the use of AI in their care, and 91.1% (204/224) supported the right to opt out. Additionally, 91.1% (204/224) of participants expressed a strong preference for being informed when AI was involved in interpreting their medical images. 65.6% (143/218) indicated that they would not accept a screening imaging exam exclusively interpreted by an AI algorithm. Finally, 91.1% (204/224) of participants wanted disclosure when AI was used and 89.1% (196/220) felt such disclosure and clarification of discrepancies should be considered standard care. To align AI adoption with patient preferences and expectations, radiology practices must prioritize disclosure, patient engagement, and standardized documentation of AI use without being overly burdensome to the diagnostic workflow. Patients prefer transparency for AI utilization in their care, and our study highlights the discrepancy between patient preferences and current clinical practice. Patients are not expected to determine the technical aspects of an image examination such as acquisition parameters or reconstruction kernel and must trust their providers to act in their best interest. Clear communication of how AI is being used in their care should be provided in ways that do not overly burden the radiologist.

This systematic review and meta-analysis aimed to summarize and evaluate the available information regarding the performance of deep learning methods for tooth detection and segmentation in orthopantomographies. Electronic databases (Medline, Embase and Cochrane) were searched up to September 2023 for relevant observational studies and both, randomized and controlled clinical trials. Two reviewers independently conducted the study selection, data extraction, and quality assessments. GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) assessment was adopted for collective grading of the overall body of evidence. From the 2,207 records identified, 20 studies were included in the analysis. Meta-analysis was conducted for the comparison of mesiodens detection and segmentation (n = 6) using sensitivity and specificity as the two main diagnostic parameters. A graphical summary of the analysis was also plotted and a Hierarchical Summary Receiver Operating Characteristic curve, prediction region, summary point, and confidence region were illustrated. The included studies quantitative analysis showed pooled sensitivity, specificity, positive LR, negative LR, and diagnostic odds ratio of 0.92 (95% confidence interval [CI], 0.84-0.96), 0.94 (95% CI, 0.89-0.97), 15.7 (95% CI, 7.6-32.2), 0.08 (95% CI, 0.04-0.18), and 186 (95% CI, 44-793), respectively. A graphical summary of the meta-analysis was plotted based on sensitivity and specificity. Hierarchical Summary Receiver Operating Characteristic curves showed a positive correlation between logit-transformed sensitivity and specificity (r = 0.886). Based on the results of the meta-analysis and GRADE assessment, a moderate recommendation is advised to dental operators when relying on AI-based tools for tooth detection and segmentation in panoramic radiographs.

To assess changes in peer-reviewed evidence on commercially available radiological artificial intelligence (AI) products from 2020 to 2023, as a follow-up to a 2020 review of 100 products. A literature review was conducted, covering January 2015 to March 2023, focusing on CE-certified radiological AI products listed on www.healthairegister.com . Papers were categorised using the hierarchical model of efficacy: technical/diagnostic accuracy (levels 1-2), clinical decision-making and patient outcomes (levels 3-5), or socio-economic impact (level 6). Study features such as design, vendor independence, and multicentre/multinational data usage were also examined. By 2023, 173 CE-certified AI products from 90 vendors were identified, compared to 100 products in 2020. Products with peer-reviewed evidence increased from 36% to 66%, supported by 639 papers (up from 237). Diagnostic accuracy studies (level 2) remained predominant, though their share decreased from 65% to 57%. Studies addressing higher-efficacy levels (3-6) remained constant at 22% and 24%, with the number of products supported by such evidence increasing from 18% to 31%. Multicentre studies rose from 30% to 41% (p < 0.01). However, vendor-independent studies decreased (49% to 45%), as did multinational studies (15% to 11%) and prospective designs (19% to 16%), all with p > 0.05. The increase in peer-reviewed evidence and higher levels of evidence per product indicate maturation in the radiological AI market. However, the continued focus on lower-efficacy studies and reductions in vendor independence, multinational data, and prospective designs highlight persistent challenges in establishing unbiased, real-world evidence. Question Evaluating advancements in peer-reviewed evidence for CE-certified radiological AI products is crucial to understand their clinical adoption and impact. Findings CE-certified AI products with peer-reviewed evidence increased from 36% in 2020 to 66% in 2023, but the proportion of higher-level evidence papers (~24%) remained unchanged. Clinical relevance The study highlights increased validation of radiological AI products but underscores a continued lack of evidence on their clinical and socio-economic impact, which may limit these tools' safe and effective implementation into clinical workflows.

Artificial intelligence (AI) holds significant promise for medical applications, particularly in coronary computed tomography angiography (CTA). We assessed the knowledge, attitudes, and practices (KAP) of cardiovascular health care personnel regarding coronary CTA and AI-assisted diagnosis. We conducted a cross-sectional survey from 1 July to 1 August 2024 at Tsinghua University Hospital, Beijing, China. Healthcare professionals, including both physicians and nurses, aged ≥18 years were eligible to participate. We used a structured questionnaire to collect demographic information and KAP scores. We analysed the data using correlation and regression methods, along with structural equation modelling. Among 496 participants, 58.5% were female, 52.6% held a bachelor's degree, and 40.7% worked in radiology. Mean KAP scores were 13.87 (standard deviation (SD) = 4.96, possible range = 0-20) for knowledge, 28.25 (SD = 4.35, possible range = 8-40) for attitude, and 31.67 (SD = 8.23, possible range = 10-50) for practice. Knowledge (r = 0.358; P < 0.001) and attitude positively correlated with practice (r = 0.489; P < 0.001). Multivariate logistic regression indicated that educational level, department affiliation, and job satisfaction were significant predictors of knowledge. Attitude was influenced by marital status, department, and years of experience, while practice was shaped by knowledge, attitude, departmental factors, and job satisfaction. Structural equation modelling showed that knowledge was directly affected by gender (β = -0.121; P = 0.009), workplace (β = -0.133; P = 0.004), department (β = -0.197; P < 0.001), employment status (β = -0.166; P < 0.001), and night shift frequency (β = 0.163; P < 0.001). Attitude was directly influenced by marriage (β = 0.124; P = 0.006) and job satisfaction (β = -0.528; P < 0.001). Practice was directly affected by knowledge (β = 0.389; P < 0.001), attitude (β = 0.533; P < 0.001), and gender (β = -0.092; P = 0.010). Additionally, gender (β = -0.051; P = 0.010) and marriage (β = 0.066; P = 0.007) had indirect effects on practice. Cardiovascular health care personnel exhibited suboptimal knowledge, positive attitudes, and relatively inactive practices regarding coronary CTA and AI-assisted diagnosis. Targeted educational efforts are needed to enhance knowledge and support the integration of AI into clinical workflows.

Early detection of lung cancer through low-dose CT lung cancer screening in a high-risk population has proven to reduce lung cancer-specific mortality. Nodule management plays a pivotal role in early detection and further diagnostic approaches. The European Society of Thoracic Imaging (ESTI) has established a nodule management recommendation to improve the handling of pulmonary nodules detected during screening. For solid nodules, the primary method for assessing the likelihood of malignancy is to monitor nodule growth using volumetry software. For subsolid nodules, the aggressiveness is determined by measuring the solid part. The ESTI-recommendation enhances existing protocols but puts a stronger focus on lesion aggressiveness. The main goals are to minimise the overall number of follow-up examinations while preventing the risk of a major stage shift and reducing the risk of overtreatment. KEY POINTS: Question Assessment of nodule growth and management according to guidelines is essential in lung cancer screening. Findings Assessment of nodule aggressiveness defines follow-up in lung cancer screening. Clinical relevance The ESTI nodule management recommendation aims to reduce follow-up examinations while preventing major stage shift and overtreatment.

The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with "AI + imaging" emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area-China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.

BackgroundArtificial Intelligence (AI) is rapidly changing the legal landscape of radiology. Results from a previous experiment suggested that providing AI error rates can reduce perceived radiologist culpability, as judged by mock jury members (4). The current study advances this work by examining whether the radiologists behavior also impacts perceptions of liability. Methods. Participants (n=282) read about a hypothetical malpractice case where a 50-year-old who visited the Emergency Department with acute neurological symptoms received a brain CT scan to determine if bleeding was present. An AI system was used by the radiologist who interpreted imaging. The AI system correctly flagged the case as abnormal. Nonetheless, the radiologist concluded no evidence of bleeding, and the blood-thinner t-PA was administered. Participants were randomly assigned to either a 1.) single-read condition, where the radiologist interpreted the CT once after seeing AI feedback, or 2.) a double-read condition, where the radiologist interpreted the CT twice, first without AI and then with AI feedback. Participants were then told the patient suffered irreversible brain damage due to the missed brain bleed, resulting in the patient (plaintiff) suing the radiologist (defendant). Participants indicated whether the radiologist met their duty of care to the patient (yes/no). Results. Hypothetical jurors were more likely to side with the plaintiff in the single-read condition (106/142, 74.7%) than in the double-read condition (74/140, 52.9%), p=0.0002. Conclusion. This suggests that the penalty for disagreeing with correct AI can be mitigated when images are interpreted twice, or at least if a radiologist gives an interpretation before AI is used.

Artificial intelligence (AI) enhances diagnostic accuracy, efficiency, and patient outcomes in radiology. Patient acceptance is essential for successful integration. This study examines patient perspectives on AI in radiology within the UAE, focusing on their knowledge, attitudes, and perceived barriers. Understanding these factors can address concerns, improve trust, and guide patient-centered AI implementation. The findings aim to support effective AI adoption in healthcare. A cross-sectional study involving 205 participants undergoing radiological imaging in the UAE. Data was collected through an online questionnaire, developed based on a literature review, and pre-tested for reliability and validity. Non-probability sampling methods, including convenience and snowball sampling, were employed. The questionnaire assessed participants' knowledge, attitudes, and perceived barriers regarding AI in radiology. Data was analyzed, and categorical variables were expressed as frequencies and percentages. Most participants (89.8 %) believed AI could improve diagnostic accuracy, and 87.8 % acknowledged its role in prioritizing urgent cases. However, only 22 % had direct experience with AI in radiology. While 81 % expressed comfort with AI-based technology, concerns about data security (80.5 %), lack of empathy in AI systems (82.9 %), and insufficient information about AI (85.8 %) were significant barriers. Additionally, (87.3 %) of participants were concerned about the cost of AI implementation. Despite these concerns, 86.3 % believed AI could improve the quality of radiological services, and 83.9 % were satisfied with its potential applications. UAE patients generally support AI in radiology, recognizing its potential for improved diagnostic accuracy. However, concerns about data security, empathy, and understanding of AI technologies necessitate improved patient education, transparent communication, and regulatory frameworks to foster trust and acceptance.