Enriching the Medical Student Radiology Clerkship: Simulating the Radiologist’s Experience

Background Current radiology training in medical schools is still predominantly limited to passively observing the radiologist at the workstation and through lectures, textbooks and online sources. Evaluation is also mainly limited on still image interpretation or knowledge-based multiple-choice questions. Furthermore, students may have specific interests based on their choice of residency. In order to create a tailored and active learning experience, and to evaluate students’ ability in image interpretation, we utilized an open-source web-based Picture archiving and communication system (PACS) named Weasis and integrated a report system. Method We establish a new PACS teaching system by utilizing the open-source PACS system “dcm4chee” and integrating Weasis as imaging viewing browser, MySQL as database and JBOSS as application server. The developmental environment is MyEclipse, developmental language is JAVA. We use WADO (Web Access to Digital Imaging and Communications in Medicine (DICOM) Object) to achieve web-client DICOM images access. Java applets are used via a browser to serve as a DICOM viewer without special software required, and all functions (window width and level, zoom, measurement, etc.) are provided as controls within the server application. Thus we built a reporting system using the same method for student reporting and preceptor commenting and grading. Following the establishing and implementation of a reporting system using the same way as a plug-in, students can write up very brief reports in the form of impression points. Result Attending radiologists can send desired anonymized studies from hospital PACS during read-out to a shared secure server on the hospital network. Cases can then be immediately accessed by trainees on any computer in the hospital. Students, even simultaneously, can simulate being a radiologist and independently formulate an opinion and write up a brief report, without the need for occupying an expensive PACS workstation. The cases can be categorized into different subspecialties, difficulty levels, and imaging modalities. In addition, this can be also used for examination purposes, both for radiology rotation evaluation of medical students and as part of the pre-call Objective Structured Clinical Examinations (OSCEs) of first year residents. Conclusion By implementing Weasis and add-on reporting system, a real-time, easy-access, sophisticated Image Database can be established. for learning, didactic and evaluation purposes. Teaching cases can easily accumulate, thus to provide a new opportunity for both versatile training and evaluation purposes for radiology programs

The impact of AI on radiographic image reporting – perspectives of the UK reporting radiographer population

Background: It is predicted that medical imaging services will be greatly impacted by AI in the future. Developments in computer vision have allowed AI to be used for assisted reporting. Studies have investigated radiologists' opinions of AI for image interpretation (Huisman et al., 2019 a/b) but there remains a paucity of information in reporting radiographers' opinions on this topic.Method: A survey was developed by AI expert radiographers and promoted via LinkedIn/Twitter and professional networks for radiographers from all specialities in the UK. A sub analysis was performed for reporting radiographers only.Results: 411 responses were gathered to the full survey (Rainey et al., 2021) with 86 responses from reporting radiographers included in the data analysis. 10.5% of respondents were using AI tools? as part of their reporting role. 59.3% and 57% would not be confident in explaining an AI decision to other healthcare practitioners and 'patients and carers' respectively. 57% felt that an affirmation from AI would increase confidence in their diagnosis. Only 3.5% would not seek second opinion following disagreement from AI. A moderate level of trust in AI was reported: mean score = 5.28 (0 = no trust; 10 = absolute trust). 'Overall performance/accuracy of the system', 'visual explanation (heatmap/ROI)', 'Indication of the confidence of the system in its diagnosis' were suggested as measures to increase trust.Conclusion: AI may impact reporting professionals' confidence in their diagnoses. Respondents are not confident in explaining an AI decision to key stakeholders. UK radiographers do not yet fully trust AI. Improvements are suggested

An evaluation of a training tool and study day in chest image interpretation

Background: With the use of expert consensus a digital tool was developed by the research team which proved useful when teaching radiographers how to interpret chest images. The training tool included A) a search strategy training tool and B) an educational tool to communicate the search strategies using eye tracking technology. This training tool has the potential to improve interpretation skills for other healthcare professionals.Methods: To investigate this, 31 healthcare professionals i.e. nurses and physiotherapists, were recruited and participants were randomised to receive access to the training tool (intervention group) or not to have access to the training tool (control group) for a period of 4-6 weeks. Participants were asked to interpret different sets of 20 chest images before and after the intervention period. A study day was then provided to all participants following which participants were again asked to interpret a different set of 20 chest images (n=1860). Each participant was asked to complete a questionnaire on their perceptions of the training provided. Results: Data analysis is in progress. 50% of participants did not have experience in image interpretation prior to the study. The study day and training tool were useful in improving image interpretation skills. Participants perception of the usefulness of the tool to aid image interpretation skills varied among respondents.Conclusion: This training tool has the potential to improve patient diagnosis and reduce healthcare costs

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

Oral Paper S26 - What are students frightened of?

Background Despite extensive consistent integrated early clinical experience at HYMS, students have often been noted to struggle in making the transition from the largely University-based Phase I (2 years) to immersion in the clinically-based Phase II. Tutors report student difficulties in adopting an appropriate attitude to learning in this environment; some are noted to respond to this by minimising the time spent on the wards with obvious consequences for their experience and education. Presentation A new “Core Clinical Skills and Professional Expectations” course, lasting 2 weeks was introduced in August 2014 for students making this transition. This block aimed to address many areas which students have been noted to struggle with, including professionalism and development of clinical diagnostic reasoning and skills for independent learning. Evaluation Students were asked to identify their own fears and anxieties about moving into the clinical environment. All students completed a brief survey at both the beginning and the end of this two week period which included identification of their own sources of anxiety in approaching immersion in the clinical environment. Results of this survey are presented and discussed with implications for clinical teaching

Oral Paper SP63. Learner Centred Communication Masterclasses

Background HYMS 3rd and 4th Year MB ChB students frequently encountered communication challenges on clinical placements, despite extensive communication skills teaching in the first two (university based) years of the course. PresentationCompulsory Communication Masterclasses were introduced for 3rd and 4th year students to provide an opportunity for them to address Communication and Professionalism challenges they have encountered on clinical placement. The student-centred Masterclasses are led by Primary /Secondary Care clinicians working with experienced Simulated Patients. They provide an opportunity for students to role play Communication/Professionalism challenges and receive feedback from their peers, Simulated Patient and tutor to help identify strategies for dealing with similar challenges in their future career. Evaluation Students are required to complete an online evaluation which includes descriptive and Likert scale feedback. Students give consistently positive feedback on these sessions, and highlight appreciating the opportunity to reflect and learn from clinician tutors about real-life communication/ professionalism challenges. This student evaluation informs Staff Development Masterclasses for tutors, tutored by faculty and run similarly to the Student Communication Masterclasses. These provide an opportunity to address challenges that tutors have encountered when tutoring Masterclasses and ensure that tutors deliver a consistently high quality student-learning experience

Roundtable RT06. Clinical Reasoning skills: Something that can be taught or just a matter of seeing lots of patients?

There is considerable literature regarding the complex nature of clinical reasoning for clinicians. Norman (2005) stated “there is no such thing as clinical reasoning - there is no best way through a problem. The more one studies the clinical expert, the more one marvels at the complex and multidimensional components of knowledge and skill that he brings to bear on the problem, and the amazing adaptability he must possess to achieve the goals of effective care”.For novices to become experts they need extensive deliberate practice to facilitate the availability of conceptual knowledge and add to their storehouse of already solved problems (Norman 2005).The authors are aware that previously students learnt how to reason clinically by clerking lots of patients and constructing lists of likely differential diagnoses. Students were repeatedly interrogated by doctors to justify their differential diagnoses. Changes in working time directives and increased shift working mean that students are less likely to have to justify their thinking on several occasions to the same doctor who then helps them develop their reasoning skills.Today’s students face further challenges, as modern medical curricula generally focus on delivering clinical experience in system-specific rotations leaving students unable to organise information effectively when patients present with complex, multisystem illnesses. A limitation of systems based curricula is that it does not encourage the development of clinical reasoning skills.There is now extensive literature regarding the need to explicitly teach clinical reasoning skills to students in addition to them having lots of practice in clerking patients and then constructing lists of the most likely differential diagnoses.Delegates at this round table discussion will be encouraged to debate whether they believe that students can be explicitly taught clinical reasoning skills or whether it is just a case of ‘seeing lots of patients’