Informatics

By Mark A. Musen, Blackford Middleton, R. Greenes. Laboratory Investigation, 2018

956 citations recorded by the source corpus.

The Division of Information Systems (IS) demonstrates a long history of integrating innovation, research and education at the intersection of people, information and computing technology. Our state-of-the-art curriculum, with a hands-on focus in web, social media, data science, business applications, and user experience, provides students with solid career knowledge, design and implementation skills, and leadership preparation. Students at all levels engage in research alongside distinguished professors, creating, applying and disseminating fundamental knowledge and innovative approaches. Research concentrates in two rigorous tracks -data-intensive research and human-centered computing -conducted by faculty who win teaching awards, highly competitive grants, best paper awards, write books, and publish extensively in very selective journals.

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Citations in the corpus

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021, background): “CDSS can help to improve patients’ safety, quality of care, and healthcare efﬁciency [23–25], as well as reducing the costs of healthcare [26].”
Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis (2018, background): “Coiera [10] identified provider’s lack of willingness and ability to use the technological system as one of the primary reasons.”
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, background): “Researchers have suggested and built several systems for clinical decision support since the mid-twentieth century [21,22].”
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes (2019, background): “In a review of deployed DSTs, healthcare researchers ranked the lack of HCI considerations as the most likely reason for failure [12, 23].”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “A distinct chapter was dedicated to “Ethical and Legal Issues in Decision Support” [139].”
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, background): “Despite the high-performing nature of these algorithms, prior work has shown that CDSSes can be difficult to successfully integrate into practice, citing a lack of HCI consideration as one of the primary reasons for failure [41, 44, 55, 76, 78].”
Review of health information technology usability study methodologies (2011, unknown): “26 We organized studies based on the usability speci ﬁ cation and evaluation framework (table 2).”
A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy (2020, background): “These obstacles include frequent lack of utility to clinicians and logistical hurdles that slow or block deployment [15, 27].”