Background: The emerging field of artificial intelligence (AI) will probably affect the practice for the next generation of doctors. However, the students' views on AI have not been largely investigated.Methods: An anonymous electronic survey on AI was designed for medical and dental students to explore: (1) sources of information about AI, (2) AI applications and concerns, (3) AI status as a topic in medicine, and (4) students' feelings and attitudes. The questionnaire was advertised on social media platforms in 2020. Security measures were employed to prevent fraudulent responses. Mann-Whitney U-test was employed for all comparisons. A sensitivity analysis was also performed by binarizing responses to express disagreement and agreement using the Chi-squared test.Results: Three thousand one hundred thirty-three respondents from 63 countries from all continents were included. Most respondents reported having at least a moderate understanding of the technologies underpinning AI and of their current application, with higher agreement associated with being male (p < 0.0001), tech-savvy (p < 0.0001), pre-clinical student (p < 0.006), and from a developed country (p < 0.04). Students perceive AI as a partner rather than a competitor (72.2%) with a higher agreement for medical students (p = 0.002). The belief that AI will revolutionize medicine and dentistry (83.9%) with greater agreement for students from a developed country (p = 0.0004) was noted. Most students agree that the AI developments will make medicine and dentistry more exciting (69.9%), that AI shall be part of the medical training (85.6%) and they are eager to incorporate AI in their future practice (99%).Conclusion: Currently, AI is a hot topic in medicine and dentistry. Students have a basic understanding of AI principles, a positive attitude toward AI and would like to have it incorporated into their training.
Background The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students’ attitudes to online learning to provide a broad scientific basis to guide future development of medical education. Methods Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education. Results The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching. Conclusion This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum offers. Thus, the COVID-19 pandemic might be the long-awaited catalyst for a new “online era” in medical education.
PurposeVolumetric modulated arc therapy (VMAT) can deliver intensity modulated radiotherapy (IMRT) like dose distributions in a short time; this allows the expansion of IMRT treatments to palliative situations like brain metastases (BMs). VMAT can deliver whole brain radiotherapy (WBRT) with hippocampal avoidance and a simultaneous integrated boost (SIB) to achieve stereotactic radiotherapy (SRT) for BMs. This study is an audit of our experience in the treatment of brain metastases with VMAT in our institution.Methods and materialsMetastases were volumetrically contoured on fused diagnostic gadolinium enhanced T1 weighted MRI/planning CT images. Risk organs included hippocampus, optic nerve, optic chiasm, eye, and brain stem. The hippocampi were contoured manually as one paired organ with assistance from a neuroradiologist. WBRT and SIB were integrated into a single plan.ResultsThirty patients with 73 BMs were treated between March 2010 and February 2012 with VMAT. Mean follow up time was 3.5 months. For 26 patients, BMs arose from primary melanoma and for the remaining four patients from non-small cell lung cancer (n= 2), primary breast cancer, and sarcoma. Mean age was 60 years. The male to female ratio was 2:1. Five patients were treated without hippocampal avoidance (HA) intent. The median WBRT dose was 31 Gy with a median SIB dose for BMs of 50 Gy, given over a median of 15 fractions. Mean values for BMs were as follows: GTV = 6.9 cc, PTV = 13.3 cc, conformity index = 8.6, homogeneity index = 1.06. Mean and maximum hippocampus dose was 20.4 Gy, and 32.4 Gy, respectively, in patients treated with HA intent. Mean VMAT treatment time from beam on to beam off for one fraction was 3.43 minutes, which compared to WBRT time of 1.3 minutes. Twenty out of 25 assessable lesions at the time of analysis were controlled. Treatment was well tolerated; grade 4 toxicity was reported in one patient. The median overall survival was 9.40 monthsConclusionsVMAT for BMs is feasible, safe and associated with a similar survival times and toxicities to conventional SRT+/−WBRT. The advantage of VMAT is that WBRT and SRT can be delivered at the same time on one machine.
Tools for medical image analysis have been developed to reduce the time needed to detect abnormalities and to provide more accurate results. Particularly, tools based on artificial intelligence and machine learning techniques have led to significant improvements in medical imaging interpretation in the last decade. Automatic evaluation of acute ischemic stroke in medical imaging is one of the fields that witnessed a major development. Commercially available products so far aim to identify (and quantify) the ischemic core, the ischemic penumbra, the site of arterial occlusion and the collateral flow but they are not (yet) intended as standalone diagnostic tools. Their use can be complementary; they are intended to support physicians' interpretation of medical images and hence standardise selection of patients for acute treatment. This review provides an introduction into the field of computer-aided diagnosis and focuses on the automatic analysis of non-contrast-enhanced computed tomography, computed tomography angiography and perfusion imaging. Future studies are necessary that allow the evaluation and comparison of different imaging strategies and post-processing algorithms during the diagnosis process in patients with suspected acute ischemic stroke; which may further facilitate the standardisation of treatment and stroke management.
BackgroundSuspected recurrence of thyroid carcinoma is a diagnostic challenge when findings of both a radio iodine whole body scan and ultrasound are negative. PET/CT and MRI have shown to be feasible for detection of recurrent disease. However, the added value of a consensus reading by the radiologist and the nuclear medicine physician, which has been deemed to be helpful in clinical routines, has not been investigated. This study aimed to investigate the impact of combined FDG-PET/ldCT and MRI on detection of locally recurrent TC and nodal metastases in high-risk patients with special focus on the value of the multidisciplinary consensus reading.Materials and methodsForty-six patients with suspected locally recurrent thyroid cancer or nodal metastases after thyroidectomy and radio-iodine therapy were retrospectively selected for analysis. Inclusion criteria comprised elevated thyroglobulin blood levels, a negative ultrasound, negative iodine whole body scan, as well as combined FDG-PET/ldCT and MRI examinations.Neck compartments in FDG-PET/ldCT and MRI examinations were independently analyzed by two blinded observers for local recurrence and nodal metastases of thyroid cancer. Consecutively, the scans were read in consensus. To explore a possible synergistic effect, FDG-PET/ldCT and MRI results were combined. Histopathology or long-term follow-up served as a gold standard.For method comparison, sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy were calculated.ResultsFDG-PET/ldCT was substantially more sensitive and more specific than MRI in detection of both local recurrence and nodal metastases. Inter-observer agreement was substantial both for local recurrence (κ = 0.71) and nodal metastasis (κ = 0.63) detection in FDG-PET/ldCT. For MRI, inter-observer agreement was substantial for local recurrence (κ = 0.69) and moderate for nodal metastasis (κ = 0.55) detection. In contrast, FDG-PET/ldCT and MRI showed only slight agreement (κ = 0.21). However, both imaging modalities identified different true positive results. Thus, the combination created a synergistic effect. The multidisciplinary consensus reading further increased sensitivity, specificity, and diagnostic accuracy.ConclusionsFDG-PET/ldCT and MRI are complementary imaging modalities and should be combined to improve detection of local recurrence and nodal metastases of thyroid cancer in high-risk patients. The multidisciplinary consensus reading is a key element in the diagnostic approach.Electronic supplementary materialThe online version of this article (doi:10.1186/s40644-016-0096-y) contains supplementary material, which is available to authorized users.
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