AIMC Topic: Referral and Consultation
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Economic Evaluation of Robot-Based Telemedicine Consultation Services.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Jan 22, 2020
Through information and communication technology, telemedicine can deliver medical care without time and place restrictions, increasing health care accessibility in medically underdeveloped regions. Although there is growing interest in using robots...
Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations.
Patient education and counseling
Dec 9, 2019
OBJECTIVE: Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights abo...
Artificial intelligence and the future of psychiatry: Insights from a global physician survey.
Artificial intelligence in medicine
Nov 18, 2019
BACKGROUND: Futurists have predicted that new autonomous technologies, embedded with artificial intelligence (AI) and machine learning (ML), will lead to substantial job losses in many sectors disrupting many aspects of healthcare. Mental health appe...
Triaging ophthalmology outpatient referrals with machine learning: A pilot study.
Clinical & experimental ophthalmology
Nov 14, 2019
IMPORTANCE: Triaging of outpatient referrals to ophthalmology services is required for the maintenance of patient care and appropriate resource allocation. Machine learning (ML), in particular natural language processing, may be able to assist with t...
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.
Ophthalmology
Sep 24, 2019
PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referr...
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Epilepsia
Aug 23, 2019
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluation...
Artificial intelligence could bring relevant guidelines into every consultation.
BMJ (Clinical research ed.)
Aug 7, 2019
Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication.
Journal of biomedical informatics
Apr 5, 2019
Radiologists are expected to expediently communicate critical and unexpected findings to referring clinicians to prevent delayed diagnosis and treatment of patients. However, competing demands such as heavy workload along with lack of administrative ...
A data-driven approach to referable diabetic retinopathy detection.
Artificial intelligence in medicine
Mar 27, 2019
UNLABELLED: Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize.
Considering patient safety in autonomous e-mental health systems - detecting risk situations and referring patients back to human care.
BMC medical informatics and decision making
Mar 18, 2019
BACKGROUND: Digital health interventions can fill gaps in mental healthcare provision. However, autonomous e-mental health (AEMH) systems also present challenges for effective risk management. To balance autonomy and safety, AEMH systems need to dete...
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