The Convergence of Human and Artificial Intelligence on Clinical Care - Part I
Abedi, Vida (editor)
This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all.
Keywordsmachine learning-enabled decision support system; improving diagnosis accuracy; Bayesian network; bariatric surgery; health-related quality of life; comorbidity; voice change; larynx cancer; machine learning; deep learning; voice pathology classification; imputation; electronic health records; EHR; laboratory measures; medical informatics; inflammatory bowel disease; C. difficile infection; osteoarthritis; complex diseases; healthcare; artificial intelligence; interpretable machine learning; explainable machine learning; septic shock; clinical decision support system; electronic health record; cerebrovascular disorders; stroke; SARS-CoV-2; COVID-19; cluster analysis; risk factors; ischemic stroke; outcome prediction; recurrent stroke; cardiac ultrasound; echocardiography; portable ultrasound; aneurysm surgery; temporary artery occlusion; clipping time; artificial neural network; digital imaging; monocytes; promonocytes and monoblasts; chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia; concordance between hematopathologists; mechanical ventilation; respiratory failure; ADHD; social media; Twitter; pharmacotherapy; stimulants; alpha-2-adrenergic agonists; non-stimulants; trust; passive adherence; human factors
Webshop linkhttps://mdpi.com/books/pdfview ...
Publication date and placeBasel, 2022