In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.
Background and ObjectivesNarrative-based evaluations are increasingly used to discriminate between levels of trainee performance, yet barriers to high-quality narratives remain. Prior evidence shows mixed results regarding the effectiveness of faculty development efforts on improving narrative evaluation quality.MethodsWe used a quasi-experimental study incorporating a historical control group to examine the effectiveness of a pragmatic, multipronged, 4-year faculty development initiative on narrative evaluation quality in a neurology clerkship. We evaluated narrative evaluation quality using the narrative evaluation quality instrument (NEQI) in random samples of narrative evaluations from a historical control and intervention group. We used multilevel modeling to compare NEQI scores (and subscale scores) across groups. Informed by the theory of deliberate practice, our faculty development initiative included (1) annual grand rounds sessions focused on developing high-quality narratives and reporting evaluation metrics, (2) restructuring the clerkship assessment form to simplify and prioritize narratives, (3) recruiting key faculty to rotate on the clerkship grading committee to gain experience with and practice developing quality narratives, and (4) instituting a narrative evaluation excellence award to faculty and residents.ResultsThe faculty development initiative was associated with improvements in the quality of students' narrative evaluations. Specifically, the intervention group was a significant predictor of NEQI score, with means of 6.4 (95% CI 5.9–6.9) and 7.6 (95% CI 7.2–8.1) for the historical control and intervention groups, respectively. In addition, the intervention group was associated with significant improvement in the specificity and usefulness NEQI subscale scores, but not the performance domain subscale score.DiscussionA long-term, multipronged faculty development initiative can facilitate improvements in narrative evaluation quality. We attribute these findings to 2 factors: (1) pragmatic, solution-oriented efforts that balance focused didactics with programmatic shifts that promote deliberate practice and skill improvement and (2) departmental resources that prioritize and convey a commitment to improving trainee assessment.
Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.
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