Radiologists' perceived value and readiness for artificial intelligence in value-based radiology: a multicountry survey.

Radiology departments are increasingly tasked with managing growing demands on services including long waitlists for scanning and interventional procedures, human health resource shortages, equipment needs, and challenges incorporating advanced imaging solutions. The burden of system inefficiencies and the overuse of "low-value" imaging causes downstream impact on patients at the individual level, the economy and healthcare system at the societal level, and planetary health at an overarching level. Low value imaging includes those performed for an inappropriate clinical indication, with little to no value to the management of the patient, and resulting in healthcare resource waste; it is estimated that up to a quarter of advanced imaging studies in Canada meet this criterion. Strategies to reduce low-value imaging include the development and use of referral guidelines, use of appropriateness criteria, optimization of existing protocols, and integration of clinical decision support tools into the ordering provider's workflow. Additional means of optimizing system efficiency such as centralized intake models, improved access to electronic medical records and outside imaging, enhanced communication with patients and referrers, and the utilization of artificial intelligence will further increase the value of radiology provided to patients and care providers.

BackgroundThe field of dentistry is undergoing substantial changes due to advancements in dental AI, with its application gaining increasing attention within the profession. This study aimed to fill this gap by assessing the usage, and perceived barriers related to AI among dental students and professionals across different countries. Importantly, the focus is on educational contexts, involving both undergraduate and postgraduate dental students.MethodsA cross-sectional multi-country study was conducted via online questionnaire between March and July 2024 to assess perceptions of AI in dentistry. Individuals under 18 years old or lacking English language proficiency were excluded. The questionnaire had three sections: demographics, knowledge and awareness of AI in dentistry, and perceptions of AI’s effectiveness in various dental conditions. Descriptive statistics and Chi-square test were done categorizing participants into undergraduate and postgraduate groups with a significance level set at p < 0.05.ResultsHalf of the participants (50.2%) had prior AI experience, with a significantly higher percentage among Graduates. Graduates used AI more frequently in prosthodontics, restorative dentistry and implantology (65.7%, 56.6%, 45.8% respectively; p < 0.05). While dental students had higher AI usage in treatment planning, periodontics and orthodontics (38.7%20.4%, 19.1% respectively; p < 0.05).ConclusionsGraduates showed higher AI usage, particularly in restorative dentistry and prosthodontics. Implementation of AI training into undergraduate and postgraduate curricula is recommended to maximize its benefits in dental education and practice.

AI4AR: Reporting, Education, and Artificial Intelligence in the Era of Value-Based Radiology