LEADER 04221nam 2201057z- 450 001 9910557617803321 005 20220321 035 $a(CKB)5400000000045223 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/79618 035 $a(oapen)doab79618 035 $a(EXLCZ)995400000000045223 100 $a20202203d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aThe Convergence of Human and Artificial Intelligence on Clinical Care - Part I 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (188 p.) 311 08$a3-0365-3296-X 311 08$a3-0365-3295-1 330 $aThis 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. 606 $aMedicine$2bicssc 610 $aADHD 610 $aalpha-2-adrenergic agonists 610 $aaneurysm surgery 610 $aartificial intelligence 610 $aartificial neural network 610 $abariatric surgery 610 $aBayesian network 610 $aC. difficile infection 610 $acardiac ultrasound 610 $acerebrovascular disorders 610 $achronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia 610 $aclinical decision support system 610 $aclipping time 610 $acluster analysis 610 $acomorbidity 610 $acomplex diseases 610 $aconcordance between hematopathologists 610 $aCOVID-19 610 $adeep learning 610 $adigital imaging 610 $aechocardiography 610 $aEHR 610 $aelectronic health record 610 $aelectronic health records 610 $aexplainable machine learning 610 $ahealth-related quality of life 610 $ahealthcare 610 $ahuman factors 610 $aimproving diagnosis accuracy 610 $aimputation 610 $ainflammatory bowel disease 610 $ainterpretable machine learning 610 $aischemic stroke 610 $alaboratory measures 610 $alarynx cancer 610 $amachine learning 610 $amachine learning-enabled decision support system 610 $amechanical ventilation 610 $amedical informatics 610 $amonocytes 610 $anon-stimulants 610 $aosteoarthritis 610 $aoutcome prediction 610 $apassive adherence 610 $apharmacotherapy 610 $aportable ultrasound 610 $apromonocytes and monoblasts 610 $arecurrent stroke 610 $arespiratory failure 610 $arisk factors 610 $aSARS-CoV-2 610 $aseptic shock 610 $asocial media 610 $astimulants 610 $astroke 610 $atemporary artery occlusion 610 $atrust 610 $aTwitter 610 $avoice change 610 $avoice pathology classification 615 7$aMedicine 700 $aAbedi$b Vida$4edt$01303484 702 $aAbedi$b Vida$4oth 906 $aBOOK 912 $a9910557617803321 996 $aThe Convergence of Human and Artificial Intelligence on Clinical Care - Part I$93027105 997 $aUNINA