METHODS: A three-station OSCE set in a hospital and community pharmacy was designed and mapped to the World Health Organisation's AMS intervention practical guide. This OSCE comprised 39 unique cases and was implemented across two campuses (Malaysia and Australia) at one institute. Stations were 8 min long and consisted of problem-solving and applying AMS principles to drug therapy management (Station 1), counselling on key antimicrobials (Station 2) or managing infectious diseases in primary care (Station 3). Primary outcome measure to assess viability was the proportion of students who were able to pass each case.
KEY FINDINGS: Other than three cases with pass rates of 50, 52.8 and 66. 7%, all cases had pass rates of 75% or more. Students were most confident with referral to medical practitioner cases and switching from intravenous to oral or empirical to directed therapy.
CONCLUSIONS: An AMS-based OSCE is a viable assessment tool in pharmacy education. Further research should explore whether similar assessments can help improve students' confidence at recognising opportunities for AMS intervention in the workplace.
OBJECTIVE: This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology.
METHODS: To organize this review comprehensively, articles and reviews were collected using the following keywords: ("Glaucoma," "optic disc," "blood vessels") and ("receptive field," "loss function," "GAN," "Generative Adversarial Network," "Deep learning," "CNN," "convolutional neural network" OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020.
RESULTS: We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease.
CONCLUSIONS: Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.
METHODS: A cross-sectional study was conducted from March to April 2021. An online survey, consisting of socio-demographic characteristics, Internet use, eHealth Literacy Scale and mobile health application utilisation, was distributed amongst pharmacy undergraduates in public and private universities in Malaysia. Data analysis included descriptive statistics, one-way analysis of variance test, Mann-Whitney U test and Kruskal-Wallis test.
RESULTS: A total of 415 participants completed the survey (response rate = 82.5%). The median eHealth Literacy Scale score (out of 40) was 31.0 ± 3.0 (interquartile range). More than one-third of participants (34.7%) were found to have low eHealth literacy. Many lacked confidence in making health decisions from online information (42.4%) and skills in distinguishing between high-quality and low-quality health resources (35.2%). Only 70.4% of the participants had mobile health applications installed on their smartphones and/or tablets. Some students felt that they were neither knowledgeable nor skilful enough to utilise mobile health applications (24.8%), whereas 23.9% were unaware of the mobile health applications available.
CONCLUSION: In summary, the eHealth literacy of Malaysian pharmacy students can be further enhanced by incorporating eHealth literacy-focused programmes into the curriculum. Moreover, pharmacy students' mobile health application utilisation can be improved through increased awareness and support from universities.