A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…This type of monitoring often relies on the use of navigation systems such as the Automatic Identification System (AIS). …”

Decision switching before any AI feedback and after all AI feedback, reported as % difference in diagnostic accuracy of participants (i.e., difference in diagnostic accuracy before... by Clare Rainey (11677747)

Trust perception of student radiographers and radiographers at the beginning of the study, before accessing any of the AI feedback, following exposure to the AI and at the end of t... by Clare Rainey (11677747)

Impact of AI feedback on <i>all</i> participants’ diagnostic accuracy, when the AI feedback is <i>correct</i> and <i>INcorrect.</i> by Clare Rainey (11677747)

Impact of AI feedback on <i>student</i> participants’ diagnostic accuracy, when the AI feedback is <i>correct</i> and <i>INcorrect.</i> by Clare Rainey (11677747)

Impact of AI feedback on <i>radiographer</i> participants’ diagnostic accuracy, when the AI feedback is <i>correct</i> and <i>INcorrect.</i> by Clare Rainey (11677747)

Students’ and radiographers’ trust perception before, during and after AI feedback. by Clare Rainey (11677747)

Patient 11 – Pathological examination: AI correct (83.6% confidence). by Clare Rainey (11677747)

Impact of AI feedback on <i>all</i> participants’ diagnostic accuracy. by Clare Rainey (11677747)

Impact of AI feedback on <i>radiographer</i> participants’ diagnostic accuracy. by Clare Rainey (11677747)

Impact of AI feedback on <i>student</i> participants’ diagnostic accuracy. by Clare Rainey (11677747)

Impact of A.I feedback on student and qualified diagnostic radiographers’ diagnostic accuracy. by Clare Rainey (11677747)

Patient 16 – Non-pathological examination: AI correct (97.0% confidence). by Clare Rainey (11677747)

t-tests comparing students and radiographers’ accuracy in determining diagnosis from radiographic images, following AI decision support, across four conditions: AI correct/incorrec... by Clare Rainey (11677747)

Patient 2 – Pathological examination: AI incorrect (99.3% confidence in <i>incorrect</i> diagnosis). by Clare Rainey (11677747)

Patient 12 – Non-pathological examination: Ai incorrect (65.24% confidence in <i>incorrect</i> diagnosis). by Clare Rainey (11677747)

Interaction plots for probability of in-hospital death by management group and spleen severity in patients (n = 7210) with AIS≤ = 2 in head/neck, face, chest, extremity, and extern... by Randeep S. Jawa (20489181)

Subjects:

Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

Impact of AI feedback on <i>student</i> participants’ diagnostic accuracy in <i>pathological</i> and <i>non-pathological</i> cases. by Clare Rainey (11677747)