Healthcare Professionals’ Expectations of Medical Artificial Intelligence and Strategies for its Clinical Implementation: A Qualitative Study / 대한의료정보학회지

Objectives@#Although medical artificial intelligence (AI) systems that assist healthcare professionals in critical care settings are expected to improve healthcare, skepticism exists regarding whether their potential has been fully actualized. Therefore, we aimed to conduct a qualitative study with physicians and nurses to understand their needs, expectations, and concerns regarding medical AI; explore their expected responses to recommendations by medical AI that contradicted their judgments; and derive strategies to implement medical AI in practice successfully. @*Methods@#Semi-structured interviews were conducted with 15 healthcare professionals working in the emergency room and intensive care unit in a tertiary teaching hospital in Seoul. The data were interpreted using summative content analysis. In total, 26 medical AI topics were extracted from the interviews. Eight were related to treatment recommendation, seven were related to diagnosis prediction, and seven were related to process improvement. @*Results@#While the participants expressed expectations that medical AI could enhance their patients’ outcomes, increase work efficiency, and reduce hospital operating costs, they also mentioned concerns regarding distortions in the workflow, deskilling, alert fatigue, and unsophisticated algorithms. If medical AI decisions contradicted their judgment, most participants would consult other medical staff and thereafter reconsider their initial judgment. @*Conclusions@#Healthcare professionals wanted to use medical AI in practice and emphasized that artificial intelligence systems should be trustworthy from the standpoint of healthcare professionals. They also highlighted the importance of alert fatigue management and the integration of AI systems into the workflow.

Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models

Objective@#This study aimed to confirm the accuracy of a machine-learning-based model in predicting the 30-day mortality of patients with pneumonia and evaluating whether they were required to be admitted to the intensive care unit (ICU). @*Methods@#The study conducted a retrospective analysis of pneumonia patients at an emergency department (ED) in Seoul, Korea, from January 1, 2016 to December 31, 2017. Patients aged 18 years or older with a pneumonia registry designation on their electronic medical record were enrolled. We collected their demographic information, mental status, and laboratory findings. Three models were used: the pre-existing CURB-65 model, and the CURB-RF and Extensive CURB-RF models, which were machine-learning models that used a random forest algorithm. The primary outcomes were ICU admission from the ED or 30-day mortality. Receiver operating characteristic curves were constructed for the models, and the areas under these curves were compared. @*Results@#Out of the 1,974 pneumonia patients, 1,732 patients were eligible to be included in the study; from these, 473 patients died within 30 days or were initially admitted to the ICU from the ED. The area under receiver operating characteristic curves of CURB-65, CURB-RF, and extensive-CURB-RF were 0.615 (0.614–0.616), 0.701 (0.700–0.702), and 0.844 (0.843–0.845), respectively. @*Conclusion@#The proposed machine-learning models could predict the mortality of patients with pneumonia more accurately than the pre-existing CURB-65 model and can help decide whether the patient should be admitted to the ICU.

Feasibility of three-dimensional reconstruction and automated measurement of fetal long bones using 5D Long Bone

OBJECTIVE: To evaluate the feasibility of five-dimensional Long Bone (5D LB), a new technique that automatically archives, reconstructs images, and measures lengths of fetal long bones, to assess whether the direction of volume sweep influences fetal long bone measurements in three-dimensional (3D) ultrasound and 5D LB, and to compare measurements of fetal long bone lengths obtained with 5D LB and those obtained with conventional two-dimensional (2D) and manual 3D techniques. METHODS: This prospective study included 39 singleton pregnancies at 26+0 to 32+0 weeks of gestation. Multiple pregnancies, fetuses with multiple congenital anomalies, and mothers with underlying medical diseases were excluded. Fetal long bones of the lower extremities-the femur, tibia, and fibula were measured by 2D and 3D ultrasound, and 5D LB, by an expert and non-expert examiner. First, we analyzed the 3D ultrasound and 5D LB data according to 2 different sweeping angles. We analyzed intra- and inter-observer variability and agreement between ultrasound techniques. Paired t-test, interclass correlation coefficient, and Bland-Altman plot and Passing-Bablok regression were used for statistical analysis. RESULTS: There was no statistical difference between long bone measurements analyzed according to 2 different volume-sweeping angles by 3D ultrasound and 5D LB. Intra- and inter-observer variability were not significantly different among all 3 ultrasound techniques. Comparing 2D ultrasound and 5D LB, the interclass correlation coefficient for femur, tibia, and fibula was 0.91, 0.92, and 0.89, respectively. CONCLUSION: 5D LB is reproducible and comparable with conventional 2D and 3D ultrasound techniques for fetal long bone measurement.