Structured Exposure Achieves High Acceptance of Immersive Technology Among Medical Students and Educators.

Virtual reality (VR) is a potent educational tool with untapped potential in medical training. However, its integration into medical schools faces challenges such as cybersickness and hesitancy among medical students and professionals. Notably, there has been no systematic assessment of the acceptance of medical educational VR applications by both students and educators. In our single-center study, we enrolled 133 medical students and 14 medical educators. Following a practical demonstration of the established VR anatomy application, Sharecare YOU VR, participants completed a self-reporting survey based on the Technology Acceptance Model (TAM), exploring user acceptance of information technologies and focusing on perceived usefulness (PU), perceived ease of use (PEU), and attitude toward using (ATU). We also sought insights into potential future applications of VR in medical education. Our findings indicate a high level of acceptance among medical students and educators upon structured exposure to VR with significantly positive responses for all three TAM variables (PU, PEU, and ATU). Intriguingly, hands-on experience influenced acceptance. Students envisioned VR's benefits in anatomy, surgery, emergency medicine, and communication skill training with patients. Both students and educators believed that VR could enhance traditional approaches and complement the existing curriculum, anticipating improved preparedness for medical students through VR training applications. In conclusion, our results demonstrate the receptivity of both students and educators to immersive technologies, including VR, in medical education. Importantly, the data suggest that the adoption of VR in this field would be welcomed rather than resisted, potentially enhancing students' self-efficacy and enriching the medical school curriculum.

[How trustworthy is artificial intelligence? : A model for the conflict between objectivity and subjectivity]. / Wie vertrauenswürdig ist künstliche Intelligenz? : Ein Modell für das Spannungsfeld zwischen Objektivität und Subjektivität.

For the integration of artificial intelligence (AI) systems into medical processes it is decisive to address both the trustworthiness of these systems and the trust that physicians and patients have in those systems. Too much trust can result in physicians uncritically relying on this technology, while too little trust may result in physicians not taking advantage of the full potential of AI-based technology in making decisions. To strike a balance between these extremes it is crucial to correctly assess the trustworthiness of a system. Only in this way is it possible to decide whether or the system can be trusted or not. This article describes these relationships for the medical context. We show why trustworthiness and trust are important in the use of AI-based systems and how individuals can come to an accurate assessment of the trustworthiness of AI-based systems.

Health economic benefits through the use of diagnostic support systems and expert knowledge.

BACKGROUND: Rare diseases are difficult to diagnose. Due to their rarity, heterogeneity, and variability, rare diseases often result not only in extensive diagnostic tests and imaging studies, but also in unnecessary repetitions of examinations, which places a greater overall burden on the healthcare system. Diagnostic decision support systems (DDSS) optimized by rare disease experts and used early by primary care physicians and specialists are able to significantly shorten diagnostic processes. The objective of this study was to evaluate reductions in diagnostic costs incurred in rare disease cases brought about by rapid referral to an expert and diagnostic decision support systems. METHODS: Retrospectively, diagnostic costs from disease onset to diagnosis were analyzed in 78 patient cases from the outpatient clinic for rare inflammatory systemic diseases at Hannover Medical School. From the onset of the first symptoms, all diagnostic measures related to the disease were taken from the patient files and documented for each day. The basis for the health economic calculations was the Einheitlicher Bewertungsmaßstab (EBM) used in Germany for statutory health insurance, which assigns a fixed flat rate to the various medical services. For 76 cases we also calculated the cost savings that would have been achieved by the diagnosis support system Ada DX applied by an expert. RESULTS: The expert was able to achieve significant savings for patients with long courses of disease. On average, the expert needed only 27 % of the total costs incurred in the individual treatment odysseys to make the correct diagnosis. The expert also needed significantly less time and avoided unnecessary examination repetitions. If a DDSS had been applied early in the 76 cases studied, only 51-68 % of the total costs would have incurred and the diagnosis would have been made earlier. Earlier diagnosis would have significantly reduced costs. CONCLUSION: The study showed that significant savings in the diagnostic process of rare diseases can be achieved through rapid referral to an expert and the use of DDSS. Faster diagnosis not only achieves savings, but also enables the right therapy and thus an increase in the quality of life for patients.

Introduction of a Pathophysiology-Based Diagnostic Decision Support System and Its Potential Impact on the Use of AI in Healthcare.

Many currently available Diagnostic Decision Support Systems (DDSS) are based on causal condition-symptom relations that exhibit certain shortcomings. Ada's new approach explores the capabilities of DDSS based on pathophysiology, describing a disease as a dynamically evolving process. We generated a pathophysiology model for 8 conditions and 68 findings suitable to assess this approach. Preliminary results meet our expectations while leaving space for further improvement.

Introduction of a Pathophysiology-Based Diagnostic Decision Support System.

Many current Clinical Decision Support Systems which assist clinical diagnosis, are based on a causal condition-symptom relation. To reach more diagnostic precision Ada's Deep Reasoning is substituting this approach with the use of a model based on pathophysiology.

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study.

BACKGROUND: Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. RESULTS: Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001). CONCLUSION: Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system's accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.

Noise-induced precursors of tonic-to-bursting transitions in hypothalamic neurons and in a conductance-based model.

The dynamics of neurons is characterized by a variety of different spiking patterns in response to external stimuli. One of the most important transitions in neuronal response patterns is the transition from tonic firing to burst discharges, i.e., when the neuronal activity changes from single spikes to the grouping of spikes. An increased number of interspike-interval sequences of specific temporal correlations was detected in anticipation of temperature induced tonic-to-bursting transitions in both, experimental impulse recordings from hypothalamic brain slices and numerical simulations of a stochastic model. Analysis of the modelling data elucidates that the appearance of such patterns can be related to particular system dynamics in the vicinity of the period-doubling bifurcation. It leads to a nonlinear response on de- and hyperpolarizing perturbations introduced by noise. This explains why such particular patterns can be found as reliable precursors of the neurons' transition to burst discharges.