Introduction: Surveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students’ attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum. Methods: A digital survey instrument was developed based on a review of available literature and consultation with subject matter experts. The survey was piloted with a group of medical students and refined based on their feedback. We then sent this anonymous digital survey to all medical students in WA (approximately 1539 students). Responses were open from the 7^th of September 2021 to the 7^th of November 2021. Students’ categorical responses were qualitatively analysed, and free text comments from the survey were qualitatively analysed using open coding techniques. Results: Overall, 134 students answered one or more questions (8.9% response rate). The majority of students (82.0%) were 20–29 years old, studying medicine as a postgraduate degree (77.6%), and had started clinical rotations (62.7%). Students were interested in AI (82.6%), self-reported having a basic understanding of AI (84.8%), but few agreed that they had an understanding of the basic computational principles of AI (33.3%) or the limitations of AI (46.2%). Most students (87.5%) had not received teaching in AI. The majority of students (58.6%) agreed that AI should be part of medical training and most (72.7%) wanted more teaching focusing on AI in medicine. Medical students appeared optimistic regarding the role of AI in medicine, with most (74.4%) agreeing with the statement that AI will improve medicine in general. The majority (56.6%) of medical students were not concerned about the impact of AI on their job security as a doctor. Students selected radiology (72.6%), pathology (58.2%), and medical administration (44.8%) as the specialties most likely to be impacted by AI, and psychiatry (61.2%), palliative care (48.5%), and obstetrics and gynaecology (41.0%) as the specialties least likely to be impacted by AI. Qualitative analysis of free text comments identified the use of AI as a tool, and that doctors will not be replaced as common themes. Conclusion: Medical students in WA appear to be interested in AI. However, they have not received education about AI and do not feel they understand its basic computational principles or limitations. AI appears to be a current deficit in the medical curriculum in WA, and most students surveyed were supportive of its introduction. These results are consistent with previous surveys conducted internationally.

As an extension of the clinical examination and as a diagnostic and problem-solving tool, ultrasound has become an established technique for clinicians. A prerequisite for high-quality clinical ultrasound practice is adequate student ultrasound training. In light of the considerable heterogeneity of ultrasound curricula in medical studies worldwide, this review presents basic principles of modern medical student ultrasound education and advocates for the establishment of an ultrasound core curriculum embedded both horizontally and vertically in medical studies.

Objective: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine.Methods: We undertook a qualitative interview‐based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks. Interviews were transcribed, coded and themes presented.Results: Twenty‐five interviews were conducted between July 2021 and May 2022. Thematic saturation was achieved after 22 interviews. Most participants were from either Western Australia (52%) or Victoria (16%) and were consultants (96%). More participants reported feeling optimistic (10/25) than neutral (6/25), pessimistic (2/25) or mixed (7/25) towards the use of AI in the ED. A minority expressed scepticism regarding the feasibility or value of implementing AI into the ED. Multiple potential risks and ethical issues were discussed by participants including skill loss from overreliance on AI, algorithmic bias, patient privacy and concerns over liability. Participants also discussed perceived inadequacies in existing information technology systems. Participants felt that AI technologies would be used as decision support tools and not replace the roles of emergency clinicians. Participants were not concerned about the impact of AI on their job security. Most (17/25) participants thought that AI would impact emergency medicine within the next 10 years.Conclusions: Emergency clinicians interviewed were generally optimistic about the use of AI in emergency medicine, so long as it is used as a decision support tool and they maintain the ability to override its recommendations.

The digitization of medicine will play an increasingly significant role in future years. In particular, telemedicine, Virtual Reality (VR) and innovative Artificial Intelligence (AI) systems offer tremendous potential in imaging diagnostics and are expected to shape ultrasound diagnostics and teaching significantly. However, it is crucial to consider the advantages and disadvantages of employing these new technologies and how best to teach and manage their use. This paper provides an overview of telemedicine, VR and AI in student ultrasound education, presenting current perspectives and controversies.

Introduction: Patients with acute abdominal pain may have unsuspected pneumoperitoneum, and be investigated with ultrasound. Although not the primary imaging modality for this condition, ultrasound will usually demonstrate signs of free intraperitoneal or extra‐luminal gas.Methods: Sonographic signs of pneumoperitoneum are described, along with the limitations and pitfalls of the technique. The reported accuracy of these signs is described.Conclusion: The different signs that may be seen are described, and it is suggested that all who ultrasound patients for abdominal pain should be aware of these.

Objectives: To review the presentation, treatment and response of those runners from the London Marathon who presented to St Thomas’ Hospital with exercise induced hyponatraemia. Design: Observational case series. Setting: St Thomas’ Hospital, a tertiary hospital situated near the finish line of the 2003 London Marathon. Participants: All runners who presented to St Thomas’ Hospital on the day of the 2003 London Marathon with altered mental state whose serum sodium concentration was less than 135 mmol/L. Main outcome measures: Presenting symptoms, volume and type of fluids administered and response to treatment (biochemical and clinical). Results: Fourteen patients were diagnosed with exercise associated hyponatraemia with serum sodium concentrations ranging from 116 to 133 mmol/L. Eleven presented with confusion. There were long delays between the finish time and presentation time for some runners. Anecdotal descriptions suggested some runners finished the race with normal mental state then became confused. There was no correlation between running time and serum sodium level. All patients received 0.9% saline and six received 1.8% saline. Despite this, some patients demonstrated falls in serum sodium concentrations. Thirteen to fourteen patients were symptomatically well the following morning, with the remaining patient significantly improved. Conclusion: Presentation of exercise associated hyponatraemia may be delayed. Optimal treatment is controversial, but the use of isotonic saline may not result in rises of serum sodium and we would suggest the early use of hypertonic fluids in symptomatic patients.