Are the Pilots Onboard? Equipping Radiologists for Clinical Implementation of AI.

The incorporation of artificial intelligence into radiological clinical workflow is on the verge of being realized. To ensure that these tools are effective, measures must be taken to educate radiologists on tool performance and failure modes. Additionally, radiology systems should be designed to avoid automation bias and the potential decline in radiologist performance. Designed solutions should cater to every level of expertise so that patient care can be enhanced and risks reduced. Ultimately, the radiology community must provide education so that radiologists can learn about algorithms, their inputs and outputs, and potential ways they may fail. This manuscript will present suggestions on how to train radiologists to use these new digital systems, how to detect AI errors, and how to maintain underlying diagnostic competency when the algorithm fails.

Radiologists Help Each Other to Improve Efficiency by Decreasing the Time to Look Up a Patient's Relevant Clinical History in the Electronic Medical Record.

The study aims to prove that it takes less time to look up relevant clinical history from an electronic medical record (EMR) if the information is already provided in a specific space in the EMR by a fellow radiologist. Patients with complex oncological and surgical histories need frequent imaging, and every time a radiologist may spend a significant amount of time looking up the same clinical information as their peers. In collaboration with ACMIO and Radiant Epic team, a space labeled "Specialty Comments" was added to the SNAPSHOT of patient's chart in EMR. For our research purpose, the specialty comment was labeled as boxed history as a variable for data analysis. If the history was not provided in that particular space, it was labeled as without boxed history. Inclusion criteria included outpatients with complex oncological histories undergoing CT chest, abdomen, and pelvis with IV contrast. The time to look up history (LUT) was documented in minutes and seconds. Two assistant professors from Abdominal Imaging provided LUT. A total of 85 cases were included in the study, 39 with boxed history and 46 without boxed history. Comparing averages of the individual reader means for history, mean LUT differed by 2.03 min (without boxed history) versus 0.57 min (with boxed history), p < 0.0001. The t-test and the nonparametric Wilcoxon tests for a difference in the population means were highly significant (p < 0.0001). A history directed to radiologist's needs resulted in a statistically significant decrease in time spent by interpreting radiologists to look through the electronic medical records for patients with complex oncological histories. Availability of history pertinent to radiology has wide-ranging advantages, including quality reporting, decrease in turnaround time, reduction in interpretation errors, and radiologists' continued learning. The space for documenting clinical history may be reproduced, or some similar area may be developed by optimizing the electronic medical records.

Enhancing Patient Communication With Chat-GPT in Radiology: Evaluating the Efficacy and Readability of Answers to Common Imaging-Related Questions.

PURPOSE: To assess ChatGPT's accuracy, relevance, and readability in answering patients' common imaging-related questions and examine the effect of a simple prompt. METHODS: A total of 22 imaging-related questions were developed from categories previously described as important to patients, as follows: safety, the radiology report, the procedure, preparation before imaging, meaning of terms, and medical staff. These questions were posed to ChatGPT with and without a short prompt instructing the model to provide an accurate and easy-to-understand response for the average person. Four board-certified radiologists evaluated the answers for accuracy, consistency, and relevance. Two patient advocates also reviewed responses for their utility for patients. Readability was assessed using the Flesch Kincaid Grade Level. Statistical comparisons were performed using χ2 and paired t tests. RESULTS: A total of 264 answers were assessed for both unprompted and prompted questions. Unprompted responses were accurate 83% of the time (218 of 264), which did not significantly change for prompted responses (87% [229 of 264]; P = .2). The consistency of the responses increased from 72% (63 of 88) to 86% (76 of 88) when prompts were given (P = .02). Nearly all responses (99% [261 of 264]) were at least partially relevant for both question types. Fewer unprompted responses were considered fully relevant at 67% (176 of 264), although this increased significantly to 80% when prompts were given (210 of 264; P = .001). The average Flesch Kincaid Grade Level was high at 13.6 [CI, 12.9-14.2], unchanged with the prompt (13.0 [CI, 12.41-13.60], P = .2). None of the responses reached the eighth-grade readability level recommended for patient-facing materials. DISCUSSION: ChatGPT demonstrates the potential to respond accurately, consistently, and relevantly to patients' imaging-related questions. However, imperfect accuracy and high complexity necessitate oversight before implementation. Prompts reduced response variability and yielded more-targeted information, but they did not improve readability. ChatGPT has the potential to increase accessibility to health information and streamline the production of patient-facing educational materials; however, its current limitations require cautious implementation and further research.

A phase II study of temsirolimus and liposomal doxorubicin for patients with recurrent and refractory bone and soft tissue sarcomas.

BACKGROUND: Relapsed and refractory sarcomas continue to have poor survival rates. The cancer stem cell (CSC) theory provides a tractable explanation for the observation that recurrences occur despite dramatic responses to upfront chemotherapy. Preclinical studies demonstrated that inhibition of the mechanistic target of rapamycin (mTOR) sensitizes the CSC population to chemotherapy. METHODS: Here we present the results of the Phase II portion of a Phase I/II clinical trial that aimed to overcome the chemoresistance of sarcoma CSC by combining the mTOR inhibitor temsirolimus (20 mg/m2 weekly) with the chemotherapeutic agent liposomal doxorubicin (30 mg/m2 monthly). RESULTS: Fifteen patients with relapsed/refractory sarcoma were evaluable at this recommended Phase 2 dose level. The median progression free survival was 315 days (range 27-799). Response rate, defined as stable disease or better for 60 days, was 53%. Nine of the patients had been previously treated with doxorubicin. Therapy was well tolerated. In a small number of patients, pre- and post- treatment tumor biopsies were available for assessment of ALDH expression as a marker of CSCs and showed a correlation between response and decreased ALDH expression. We also found a correlation between biopsy-proven inhibition of mTOR and response. CONCLUSIONS: Our study adds to the literature supporting the addition of mTOR inhibition to chemotherapy agents for the treatment of sarcomas, and proposes that a mechanism by which mTOR inhibition enhances the efficacy of chemotherapy may be through sensitizing the chemoresistant CSC population. Further study, ideally with pre- and post-therapy assessment of ALDH expression in tumor cells, is warranted.Trial registration The trial was registered on clinicaltrials.gov (NCT00949325) on 30 July 2009. http://www.editorialmanager.com/csrj/default.aspx.