The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Medicine; ADHD; alpha-2-adrenergic agonists; aneurysm surgery; artificial intelligence; artificial neural network; bariatric surgery; Bayesian network; C. difficile infection; cardiac ultrasound; cerebrovascular disorders; chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia; clinical decision support system; clipping time; cluster analysis; comorbidity; complex diseases; concordance between hematopathologists; COVID-19; deep learning; digital imaging; echocardiography; EHR; electronic health record; electronic health records; explainable machine learning; health-related quality of life; healthcare; human factors; improving diagnosis accuracy; imputation; inflammatory bowel disease; interpretable machine learning; ischemic stroke; laboratory measures; larynx cancer; machine learning; machine learning-enabled decision support system; mechanical ventilation; medical informatics; monocytes; non-stimulants; osteoarthritis; outcome prediction; passive adherence; pharmacotherapy; portable ultrasound; promonocytes and monoblasts; recurrent stroke; respiratory failure; risk factors; SARS-CoV-2; septic shock; social media; stimulants; stroke; temporary artery occlusion; trust; Twitter; voice change; voice pathology classification
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication

 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.