Informed consent for image-guided procedures: a nationwide survey of perceptions and current practices.

AIM: To characterise the current landscape of informed consent practices for image-guided procedures, including location of consent, guideline availability, and utility of decision-aid resources. MATERIALS AND METHODS: A survey of 159 interventional radiologists was conducted from April through June 2022. The survey evaluated participant demographics (gender, practice type, and level of training) and consent practices. Fifteen questions investigated discussion of benefits, risks, and alternatives, who obtained consent, location of consent conversations, how decision-making capacity is assessed, availability of formal guidance on consent discussions, and if and how decision-aids are used. RESULTS: Most respondents (93.7%) were "extremely" or "very" comfortable discussing the benefits and risks of image-guided procedures during informed consent. Most respondents were "very" comfortable discussing alternative treatments within radiology (86.8%) while fewer felt confident regarding alternatives outside radiology (46.5%). Most respondents indicated obtaining consent in a pre-procedure area (89.9%), while 12.7% of respondents obtained consent in the procedure room. Of the respondents, 66.7% did not have formal education or documented guidance on what providers should disclose during consent. Ninety-two respondents (57.9%) reported using decision aids. The type of decision aid varied, with most reporting using illustrations or drawings (46.6%). Decision aid utility was more prevalent in non-teaching/academic (71.4%) versus academic (61%) institutions (p=0.02). CONCLUSION: Regardless of demographics, interventionalists are confident in discussing benefits, risks, and alternative image-guided therapies, but are less confident discussing alternative treatment options outside of radiology. Formal education on informed consent is less common, and the use of decision aids varies between teaching and non-teaching institutions.

An Artificial Intelligence Tool for Clinical Decision Support and Protocol Selection for Brain MRI.

BACKGROUND AND PURPOSE: Protocolling, the process of determining the most appropriate acquisition parameters for an imaging study, is time-consuming and produces variable results depending on the performing physician. The purpose of this study was to assess the potential of an artificial intelligence-based semiautomated tool in reducing the workload and decreasing unwarranted variation in the protocolling process. MATERIALS AND METHODS: We collected 19,721 MR imaging brain examinations at a large academic medical center. Criterion standard labels were created using physician consensus. A model based on the Long Short-Term Memory network was trained to predict the most appropriate protocol for any imaging request. The model was modified into a clinical decision support tool in which high-confidence predictions, determined by the values the model assigns to each possible choice, produced the best protocol automatically and low confidence predictions provided a shortened list of protocol choices for review. RESULTS: The model achieved 90.5% accuracy in predicting the criterion standard labels and demonstrated higher agreement than the original protocol assignments, which achieved 85.9% accuracy (κ = 0.84 versus 0.72, P value < .001). As a clinical decision support tool, the model automatically assigned 70% of protocols with 97.3% accuracy and, for the remaining 30% of examinations, achieved 94.7% accuracy when providing the top 2 protocols. CONCLUSIONS: Our model achieved high accuracy on a standard based on physician consensus. It showed promise as a clinical decision support tool to reduce the workload by automating the protocolling of a sizeable portion of examinations while maintaining high accuracy for the remaining examinations.