IGV Short Scale to Assess Implicit Value of Visualizations through Explicit Interaction

This paper reports the assessment of the infographics-value (IGV) short scale, designed to measure the value in the use of infographics. The scale was made to assess the implicit quality dimensions of infographics. These dimensions were experienced during the execution of tasks in a contextualized scenario. Users were asked to retrieve a piece of information by explicitly interacting with the infographics. After usage, they were asked to rate quality dimensions of infographics, namely, usefulness, intuitiveness, clarity, informativity, and beauty; the overall value perceived from interacting with infographics was also included in the survey. Each quality dimension was coded as a six-point rating scale item, with overall value included. The proposed IGV short scale model was validated with 650 people. Our analysis confirmed that all considered dimensions in our scale were independently significant and contributed to assessing the implicit value of infographics. The IGV short scale is a lightweight but exhaustive tool to rapidly assess the implicit value of an explicit interaction with infographics in daily tasks, where value in use is crucial to measuring the situated effectiveness of visual tools. View Full-Tex

Enhancing human-AI collaboration: The case of colonoscopy

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond

Improving the accuracy of the preoperative diagnosis of long head of the biceps pathology: the biceps resisted flexion test

Purpose: the purpose of this study was to describe a new test for identifying lesions of the long head of the biceps (LHB) and to evaluate its diagnostic performance in comparison with selected traditional clinical tests. Methods: one hundred and nine consecutive candidates for arthroscopic rotator cuff repair were prospectively recruited.The BRF test, which measures biceps resisted flexion strength, was performed with the patient seated (armat the side and elbow flexed at 90°). The patient was asked to maintain maximal resistance for five seconds while strength was assessed with a digital dynamometer. Since the dynamometer expresses a continuous variable in kilograms, the measure was dichotomized using a threshold value (cut-off ) valueable to simultaneously maximize the sensitivity and specificity. This cut-off value was derived from receiver operating characteristic (ROC) curve analysis. Speed’s test and the O’Brien test were also performed. During arthroscopy the presence of LHB pathology was assessed. Results: biceps resisted flexion strength was significantly higher in patients without associated LHB lesions [median (range): 3 kg (0-9.5 kg) versus 0 kg (0-8.5 kg); p< 0.001]. The cut-off level able to simultaneously maximize the sensitivity and specificity of the test was 1.1kg. The area under the ROC curve was 0.745 (p<0.001) for the dichotomic BRF test (dBRF), 0.562 (p=0.3) for the O’Brien test, and 0.602 (p=0.113) for Speed’s test. A significant good level of accuracy was achieved only by the dBRF test. Specificity and the positive predictive value were significantly higher for the dBRF test than for Speed’s and O’Brien’s tests (p<0.02). Age and the dBRF test were both found to be significant predictors of LHB lesions. Conclusions: the dBRF test showed higher accuracy than traditional clinical tests in diagnosing LHB lesions. This novel test for biceps pathology may be advantageous because it is objective and therefore likely reproducible. Level of Evidence: Level II, Development of diagnostic test on basis of consecutive patients (with universally applied reference “gold” standard)

Residues of Some Pesticides in Fresh and Dried Apricots

The persistence of three pesticides (fenitrothion, dimethoate, and ziram) in apricots in field conditions and their fate during the drying process were studied. After the treatments, the pesticides showed fast decay rates with pseudo-first-order kinetics and half-lives ranging from 6.9 to 9.9 days. The drying process showed a different effect on residue concentrations in dried apricots: omethoate (metabolite of dimethoate) and ziram residues had almost doubled, while fenitrothion disappeared and dimethoate remained constant