Strengthen Your Teaching Framework: Using Self-Assessment of Instruction as a Structural Support

What role does self-assessment play in improving your teaching? The University of Illinois Undergraduate Library shares their self-assessment rubric, based on the ACRL Standards for Proficiencies for Instruction Librarians and Coordinators. Such a tool provides an important framework for self-assessment and can significantly impact the instruction of librarians at multiple points in their careers. Hear how an instruction coordinator, an early career librarian, and a library school graduate assistant use self-assessment to reflect and improve their effectiveness as teacher librarians. Learn strategies for using self-assessment that can help you become a more effective teacher, too

Toward fairness in artificial intelligence for medical image analysis: Identification and mitigation of potential biases in the roadmap from data collection to model deployment

Purpose: To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups. Approach: Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development. Results: Five main steps along the roadmap of medical imaging AI/ML were identified: (1) data collection, (2) data preparation and annotation, (3) model development, (4) model evaluation, and (5) model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies. Conclusions: Our findings provide a valuable resource to researchers, clinicians, and the public at large.</p

Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons

Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify longitudinal representativeness of the demographic characteristics of the primary MIDRC dataset compared to the United States general population (US Census) and COVID-19 positive case counts from the Centers for Disease Control and Prevention (CDC). Approach: The Jensen-Shannon distance (JSD), a measure of similarity of two distributions, was used to longitudinally measure the representativeness of the distribution of (1) all unique patients in the MIDRC data to the 2020 US Census and (2) all unique COVID-19 positive patients in the MIDRC data to the case counts reported by the CDC. The distributions were evaluated in the demographic categories of age at index, sex, race, ethnicity, and the combination of race and ethnicity. Results: Representativeness of the MIDRC data by ethnicity and the combination of race and ethnicity was impacted by the percentage of CDC case counts for which this was not reported. The distributions by sex and race have retained their level of representativeness over time. Conclusion: The representativeness of the open medical imaging datasets in the curated public data commons at MIDRC has evolved over time as the number of contributing institutions and overall number of subjects have grown. The use of metrics, such as the JSD support measurement of representativeness, is one step needed for fair and generalizable AI algorithm development.</p

Development and assessment of a clinically viable system for breast ultrasound computer-aided diagnosis.

Development and assessment of a clinically viable system for breast ultrasound computer-aided diagnosis

Repeatability in computer-aided diagnosis: Application to breast cancer diagnosis on sonography

Purpose: The aim of this study was to investigate the concept of repeatability in a case-based performance evaluation of two classifiers commonly used in computer-aided diagnosis in the task of distinguishing benign from malignant lesions

Breast US Computer-aided Diagnosis Workstation: Performance with a Large Clinical Diagnostic Population1

The computer performance was largely unaffected by the inclusion of large numbers of lesions that did not undergo biopsy in the analysis, achieving overall good lesion characterization performance at area under the receiver operating characteristic curve value of 0.90

Breast US Computer-aided Diagnosis System: Robustness across Urban Populations in South Korea and the United States1

In general, the breast US computer-aided diagnosis system appears to be effective across different patient populations, but further investigation is warranted