The effects of gaze-display feedback on medical students' self-monitoring and learning in radiology.

Self-monitoring is essential for effectively regulating learning, but difficult in visual diagnostic tasks such as radiograph interpretation. Eye-tracking technology can visualize viewing behavior in gaze displays, thereby providing information about visual search and decision-making. We hypothesized that individually adaptive gaze-display feedback improves posttest performance and self-monitoring of medical students who learn to detect nodules in radiographs. We investigated the effects of: (1) Search displays, showing which part of the image was searched by the participant; and (2) Decision displays, showing which parts of the image received prolonged attention in 78 medical students. After a pretest and instruction, participants practiced identifying nodules in 16 cases under search-display, decision-display, or no feedback conditions (n = 26 per condition). A 10-case posttest, without feedback, was administered to assess learning outcomes. After each case, participants provided self-monitoring and confidence judgments. Afterward, participants reported on self-efficacy, perceived competence, feedback use, and perceived usefulness of the feedback. Bayesian analyses showed no benefits of gaze displays for post-test performance, monitoring accuracy (absolute difference between participants' estimated and their actual test performance), completeness of viewing behavior, self-efficacy, and perceived competence. Participants receiving search-displays reported greater feedback utilization than participants receiving decision-displays, and also found the feedback more useful when the gaze data displayed was precise and accurate. As the completeness of search was not related to posttest performance, search displays might not have been sufficiently informative to improve self-monitoring. Information from decision displays was rarely used to inform self-monitoring. Further research should address if and when gaze displays can support learning.

Eye tracking: empirical foundations for a minimal reporting guideline.

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

Chest X-ray evaluation training: impact of normal and abnormal image ratio and instructional sequence.

CONTEXT: Medical image perception training generally focuses on abnormalities, whereas normal images are more prevalent in medical practice. Furthermore, instructional sequences that let students practice prior to expert instruction (inductive) may lead to improved performance compared with methods that give students expert instruction before practice (deductive). This study investigates the effects of the proportion of normal images and practice-instruction order on learning to interpret medical images. It is hypothesised that manipulation of the proportion of normal images will lead to a sensitivity-specificity trade-off and that students in practice-first (inductive) conditons need more time per practice case but will correctly identify more test cases. METHODS: Third-year medical students (n = 103) learned radiograph interpretation by practising cases with, respectively, 30% or 70% normal radiographs prior to expert instruction (practice-first order) or after expert instruction (instruction-first order). After training, students performed a test (60% normal) and sensitivity (% of correctly identified abnormal radiographs), specificity (% of correctly identified normal radiographs), diagnostic performance (% of correct diagnoses) and case duration were measured. RESULTS: The conditions with 30% of normal images scored higher on sensitivity but the conditions with 70% of normal images scored higher on specificity, indicating a sensitivity and specificity trade-off. Those who participated in inductive conditions took less time per practice case but more per test case. They had similar test sensitivity, but scored lower on test specificity. CONCLUSIONS: The proportion of normal images impacted the sensitivity-specificity trade-off. This trade-off should be an important consideration for the alignment of training with future practice. Furthermore, the deductive conditions unexpectedly scored higher on specificity when participants took less time per case. An inductive approach did not lead to higher diagnostic performance, possibly because participants might already have relevant prior knowledge. Deductive approaches are therefore advised for the training of advanced learners.

Before your very eyes: the value and limitations of eye tracking in medical education.

CONTEXT: Medicine is a highly visual discipline. Physicians from many specialties constantly use visual information in diagnosis and treatment. However, they are often unable to explain how they use this information. Consequently, it is unclear how to train medical students in this visual processing. Eye tracking is a research technique that may offer answers to these open questions, as it enables researchers to investigate such visual processes directly by measuring eye movements. This may help researchers understand the processes that support or hinder a particular learning outcome. AIM: In this article, we clarify the value and limitations of eye tracking for medical education researchers. For example, eye tracking can clarify how experience with medical images mediates diagnostic performance and how students engage with learning materials. Furthermore, eye tracking can also be used directly for training purposes by displaying eye movements of experts in medical images. CONCLUSIONS: Eye movements reflect cognitive processes, but cognitive processes cannot be directly inferred from eye-tracking data. In order to interpret eye-tracking data properly, theoretical models must always be the basis for designing experiments as well as for analysing and interpreting eye-tracking data. The interpretation of eye-tracking data is further supported by sound experimental design and methodological triangulation.

Even if I showed you where you looked, remembering where you just looked is hard.

People know surprisingly little about their own visual behavior, which can be problematic when learning or executing complex visual tasks such as search of medical images. We investigated whether providing observers with online information about their eye position during search would help them recall their own fixations immediately afterwards. Seventeen observers searched for various objects in "Where's Waldo" images for 3 s. On two-thirds of trials, observers made target present/absent responses. On the other third (critical trials), they were asked to click twelve locations in the scene where they thought they had just fixated. On half of the trials, a gaze-contingent window showed observers their current eye position as a 7.5° diameter "spotlight." The spotlight "illuminated" everything fixated, while the rest of the display was still visible but dimmer. Performance was quantified as the overlap of circles centered on the actual fixations and centered on the reported fixations. Replicating prior work, this overlap was quite low (26%), far from ceiling (66%) and quite close to chance performance (21%). Performance was only slightly better in the spotlight condition (28%, p = 0.03). Giving observers information about their fixation locations by dimming the periphery improved memory for those fixations modestly, at best.

Does the Use of a Checklist Help Medical Students in the Detection of Abnormalities on a Chest Radiograph?

The interpretation of chest radiographs is a complex task that is prone to diagnostic error, especially for medical students. The aim of this study is to investigate the extent to which medical students benefit from the use of a checklist regarding the detection of abnormalities on a chest radiograph. We developed a checklist based on literature and interviews with experienced thorax radiologists. Forty medical students in the clinical phase assessed 18 chest radiographs during a computer test, either with (n = 20) or without (n = 20) the checklist. We measured performance and asked participants for feedback using a survey. Participants that used a checklist detected more abnormalities on images with multiple abnormalities (M = 50.1%) than participants that could not use a checklist (M = 41.9%), p = 0.04. The post-experimental survey shows that on average, participants considered the checklist helpful (M = 3.25 on a five-point scale), but also time consuming (M = 3.30 on a five-point scale). In conclusion, a checklist can help medical students to detect abnormalities in chest radiographs. Moreover, students tend to appreciate the use of a checklist as a helpful tool during the interpretation of a chest radiograph. Therefore, a checklist is a potentially important tool to improve radiology education in the medical curriculum.

Systematic viewing in radiology: seeing more, missing less?

To prevent radiologists from overlooking lesions, radiology textbooks recommend "systematic viewing," a technique whereby anatomical areas are inspected in a fixed order. This would ensure complete inspection (full coverage) of the image and, in turn, improve diagnostic performance. To test this assumption, two experiments were performed. Both experiments investigated the relationship between systematic viewing, coverage, and diagnostic performance. Additionally, the first investigated whether systematic viewing increases with expertise; the second investigated whether novices benefit from full-coverage or systematic viewing training. In Experiment 1, 11 students, ten residents, and nine radiologists inspected five chest radiographs. Experiment 2 had 75 students undergo a training in either systematic, full-coverage (without being systematic) or non-systematic viewing. Eye movements and diagnostic performance were measured throughout both experiments. In Experiment 1, no significant correlations were found between systematic viewing and coverage, r = -.10, p = .62, and coverage and performance, r = -.06, p = .74. Experts were significantly more systematic than students F2,25 = 4.35, p = .02. In Experiment 2, significant correlations were found between systematic viewing and coverage, r = -.35, p < .01, but not between coverage and performance, r = .13, p = .31. Participants in the full-coverage training performed worse compared with both other groups, which did not differ between them, F2,71 = 3.95, p = .02. In conclusion, the data question the assumption that systematic viewing leads to increased coverage, and, consequently, to improved performance. Experts inspected cases more systematically, but students did not benefit from systematic viewing training.

Did You Get That? Predicting Learners' Comprehension of a Video Lecture from Visualizations of Their Gaze Data.

In online lectures, unlike in face-to-face lectures, teachers lack access to (nonverbal) cues to check if their students are still "with them" and comprehend the lecture. The increasing availability of low-cost eye-trackers provides a promising solution. These devices measure unobtrusively where students look and can visualize these data to teachers. These visualizations might inform teachers about students' level of "with-me-ness" (i.e., do students look at the information that the teacher is currently talking about) and comprehension of the lecture, provided that (1) gaze measures of "with-me-ness" are related to comprehension, (2) people not trained in eye-tracking can predict students' comprehension from gaze visualizations, (3) we understand how different visualization techniques impact this prediction. We addressed these issues in two studies. In Study 1, 36 students watched a video lecture while being eye-tracked. The extent to which students looked at relevant information and the extent to which they looked at the same location as the teacher both correlated with students' comprehension (score on an open question) of the lecture. In Study 2, 50 participants watched visualizations of students' gaze (from Study 1), using six visualization techniques (dynamic and static versions of scanpaths, heatmaps, and focus maps) and were asked to predict students' posttest performance and to rate their ease of prediction. We found that people can use gaze visualizations to predict learners' comprehension above chance level, with minor differences between visualization techniques. Further research should investigate if teachers can act on the information provided by gaze visualizations and thereby improve students' learning.

Holistic processing only? The role of the right fusiform face area in radiological expertise.

Radiologists can visually detect abnormalities on radiographs within 2s, a process that resembles holistic visual processing of faces. Interestingly, there is empirical evidence using functional magnetic resonance imaging (fMRI) for the involvement of the right fusiform face area (FFA) in visual-expertise tasks such as radiological image interpretation. The speed by which stimuli (e.g., faces, abnormalities) are recognized is an important characteristic of holistic processing. However, evidence for the involvement of the right FFA in holistic processing in radiology comes mostly from short or artificial tasks in which the quick, 'holistic' mode of diagnostic processing is not contrasted with the slower 'search-to-find' mode. In our fMRI study, we hypothesized that the right FFA responds selectively to the 'holistic' mode of diagnostic processing and less so to the 'search-to-find' mode. Eleven laypeople and 17 radiologists in training diagnosed 66 radiographs in 2s each (holistic mode) and subsequently checked their diagnosis in an extended (10-s) period (search-to-find mode). During data analysis, we first identified individual regions of interest (ROIs) for the right FFA using a localizer task. Then we employed ROI-based ANOVAs and obtained tentative support for the hypothesis that the right FFA shows more activation for radiologists in training versus laypeople, in particular in the holistic mode (i.e., during 2s trials), and less so in the search-to-find mode (i.e., during 10-s trials). No significant correlation was found between diagnostic performance (diagnostic accuracy) and brain-activation level within the right FFA for both, short-presentation and long-presentation diagnostic trials. Our results provide tentative evidence from a diagnostic-reasoning task that the FFA supports the holistic processing of visual stimuli in participants' expertise domain.

Redefining the structure of structured reporting in radiology.

Structured reporting is advocated as a means of improving reporting in radiology to the ultimate benefit of both radiological and clinical practice. Several large initiatives are currently evaluating its potential. However, with numerous characterizations of the term in circulation, "structured reporting" has become ambiguous and is often confused with "standardization," which may hamper proper evaluation and implementation in clinical practice. This paper provides an overview of interpretations of structured reporting and proposes a clear definition that differentiates structured reporting from standardization. Only a clear uniform definition facilitates evidence-based implementation, enables evaluation of its separate components, and supports (meta-)analyses of literature reports.

How Experts Adapt Their Gaze Behavior When Modeling a Task to Novices.

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts' (natural or didactic) problem-solving behavior as well as their eye movements to learners. While research on expert-novice communication mainly focused on experts' changes in explicit, verbal communication behavior, it is as yet unclear whether and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts' natural problem-solving behavior by contrasting it with that of novices. Then, we explored the changes in experts' behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts' automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically.

Reversal of the hanging protocol of Contrast Enhanced Mammography leads to similar diagnostic performance yet decreased reading times.

OBJECTIVES: Contrast-enhanced mammography (CEM) was found superior to Full-Field Digital Mammography (FFDM) for breast cancer detection. Current hanging protocols show low-energy (LE, similar to FFDM) images first, followed by recombined (RC) images. However, evidence regarding which hanging protocol leads to the most efficient reading process and highest diagnostic performance is lacking. This study investigates the effects of hanging-protocol ordering on the reading process and diagnostic performance of breast radiologists using eye-tracking methodology. Furthermore, it investigates differences in reading processes and diagnostic performance between LE, RC and FFDM images. MATERIALS AND METHODS: Twenty-seven breast radiologists were randomized into three reading groups: LE-RC (commonly used hangings), RC-LE (reversed hangings) and FFDM. Thirty cases (nine malignant) were used. Fixation count, net dwell time and time-to-first fixation on malignancies as visual search measures were registered by the eye-tracker. Reading time per image was measured. Participants clicked on suspicious lesions to determine sensitivity and specificity. Area-under-the-ROC-curve (AUC) values were calculated. RESULTS: RC-LE scored identical on visual search measures, t(16)= -1.45, p = .17 or higher-p values, decreased reading time with 31%, t(16)= -2.20, p = .04, while scoring similar diagnostic performance compared to LE-RC, t(13.2) = -1.39, p - .20 or higher p-values. The reading process was more efficient on RC compared to LE. Diagnostic performance of CEM was superior to FFDM; F (2,26) = 16.1, p < .001. Average reading time did not differ between the three groups, F (2,25) = 3.15, p = .06. CONCLUSION: The reversed CEM hanging protocol (RC-LE) scored similar on diagnostic performance compared to LE-RC, while reading time was a third faster. Abnormalities were interpreted quicker on RC images. A RC-LE hanging protocol is therefore recommended for clinical practice and training. Diagnostic performance of CEM was (again) superior to FFDM.

Radiology education: a radiology curriculum for all medical students?

Diagnostic errors in radiology are frequent and can cause severe patient harm. Despite large performance differences between radiologists and non-radiology physicians, the latter often interpret medical images because electronic health records make images available throughout the hospital. Some people argue that non-radiologists should not diagnose medical images at all, and that medical school should focus on teaching ordering skills instead of image interpretation skills. We agree that teaching ordering skills is crucial as most physicians will need to order medical images in their professional life. However, we argue that the availability of medical images is so ubiquitous that it is important that non-radiologists are also trained in the basics of medical image interpretation and, additionally in recognizing when radiological consultancy should be sought. In acute situations, basic image interpretations skills can be life-saving. We plead for a radiology curriculum for all medical students. This should include the interpretation of common abnormalities on chest and skeletal radiographs and a basic distinction of normal from abnormal images. Furthermore, substantial attention should be given to the correct ordering of radiological images. Finally, it is critical that students are trained in deciding when to consult a radiologist.

Teaching Systematic Viewing to Final-Year Medical Students Improves Systematicity but Not Coverage or Detection of Radiologic Abnormalities.

PURPOSE: Systematic viewing of images is widely advocated in radiology; it is expected to lead to complete coverage of images and consequently more detection of abnormalities. Evidence on the efficacy of teaching systematic viewing to students is conflicting. The aim of this study was to investigate the effects of teaching systematic viewing to final-year medical students on systematicity of viewing behavior, coverage of the image, and detection. METHODS: Final-year medical students (n = 60) viewed 10 chest radiographs in a first series before training and another 10 radiographs in a second series after training. Between series, students were taught basic chest radiographic viewing, in either a systematic or a nonsystematic manner. With eye tracking, systematicity (Levenshtein distances), coverage (percentage of image viewed), and detection (sensitivity and specificity) were measured. RESULTS: In a mixed two-by-two design, significantly higher sensitivity was found after training compared with before training (F1,55 = 6.68, P = .012, ηp2 = .11) but no significant effect for type of training (F1,55 = 1.24, P = .30) and no significant interaction effect (F1,55 = 0.12, P = .73). Thus, training in systematic viewing was not superior to training in nonsystematic viewing. A significant interaction of training type and time of viewing was found on systematicity (F1,49 = 20.0, P < .01, ηp2 = .29) in favor of the systematic viewing group. No significant interaction was found for coverage (F1,49 = 0.43, P = .51) or specificity (F1,55 = .124, P = .73). CONCLUSIONS: Both training types showed similar increases in sensitivity. Therefore, it might be advisable to pay less attention to systematic viewing and more attention to content, such as the radiologic appearances of diseases.

What We Do and Do Not Know about Teaching Medical Image Interpretation.

Educators in medical image interpretation have difficulty finding scientific evidence as to how they should design their instruction. We review and comment on 81 papers that investigated instructional design in medical image interpretation. We distinguish between studies that evaluated complete offline courses and curricula, studies that evaluated e-learning modules, and studies that evaluated specific educational interventions. Twenty-three percent of all studies evaluated the implementation of complete courses or curricula, and 44% of the studies evaluated the implementation of e-learning modules. We argue that these studies have encouraging results but provide little information for educators: too many differences exist between conditions to unambiguously attribute the learning effects to specific instructional techniques. Moreover, concepts are not uniformly defined and methodological weaknesses further limit the usefulness of evidence provided by these studies. Thirty-two percent of the studies evaluated a specific interventional technique. We discuss three theoretical frameworks that informed these studies: diagnostic reasoning, cognitive schemas and study strategies. Research on diagnostic reasoning suggests teaching students to start with non-analytic reasoning and subsequently applying analytic reasoning, but little is known on how to train non-analytic reasoning. Research on cognitive schemas investigated activities that help the development of appropriate cognitive schemas. Finally, research on study strategies supports the effectiveness of practice testing, but more study strategies could be applicable to learning medical image interpretation. Our commentary highlights the value of evaluating specific instructional techniques, but further evidence is required to optimally inform educators in medical image interpretation.

Case Comparisons: An Efficient Way of Learning Radiology.

RATIONALE AND OBJECTIVES: Radiologists commonly use comparison films to improve their differential diagnosis. Educational literature suggests that this technique might also be used to bolster the process of learning to interpret radiographs. We investigated the effectiveness of three comparison techniques in medical students, whom we invited to compare cases of the same disease (same-disease comparison), cases of different diseases (different-disease comparison), disease images with normal images (disease/normal comparison), and identical images (no comparison/control condition). Furthermore, we used eye-tracking technology to investigate which elements of the two cases were compared by the students. MATERIALS AND METHODS: We randomly assigned 84 medical students to one of four conditions and had them study different diseases on chest radiographs, while their eye movements were being measured. Thereafter, participants took two tests that measured diagnostic performance and their ability to locate diseases, respectively. RESULTS: Students studied most efficiently in the same-disease and different-disease comparison conditions: test 1, F(3, 68) = 3.31, P = .025, ηp(2) = 0.128; test 2, F(3, 65) = 2.88, P = .043, ηp(2) = 0.117. We found that comparisons were effected in 91% of all trials (except for the control condition). Comparisons between normal anatomy were particularly common (45.8%) in all conditions. CONCLUSIONS: Comparing cases can be an efficient way of learning to interpret radiographs, especially when the comparison technique used is specifically tailored to the learning goal. Eye tracking provided insight into the comparison process, by showing that few comparisons were made between abnormalities, for example.