AIMC Topic: Physicians
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Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.
The Journal of clinical psychiatry
Apr 21, 2020
OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implement...
Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.
Journal of pharmacokinetics and pharmacodynamics
Apr 11, 2020
The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and S...
The views of physicians and nurses on the potentials of an electronic assessment system for recognizing the needs of patients in palliative care.
BMC palliative care
Apr 4, 2020
OBJECTIVES: Patients in oncological and palliative care (PC) often have complex needs, which require a comprehensive treatment approach. The assessment of patient-reported outcomes (PROs) has been shown to improve identification of patient needs and ...
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.
BMJ (Clinical research ed.)
Mar 25, 2020
OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.
[Comments on Gratzer et al.: ``Des guidelines pour l'intelligence artificielle !''].
La Revue de medecine interne
Feb 19, 2020
[Do artificial intelligence systems reason in the same way as clinicians when making diagnoses?].
La Revue de medecine interne
Jan 25, 2020
Clinical reasoning is at the heart of physicians' competence, as it allows them to make diagnoses. However, diagnostic errors are common, due to the existence of reasoning biases. Artificial intelligence is undergoing unprecedented development in thi...
[Artificial intelligence: Guidelines for internists].
La Revue de medecine interne
Dec 31, 2019
Following the emergence of open public databases and connected objects, big data and artificial intelligence are developing rapidly, especially in medicine, with many opportunities ranging from complex diagnostic assistance to real-time statistical a...
The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma.
The Journal of allergy and clinical immunology
Dec 25, 2019
Re-examining physician-scientist training through the prism of the discovery-invention cycle.
F1000Research
Dec 19, 2019
The training of physician-scientists lies at the heart of future medical research. In this commentary, we apply Narayanamurti and Odumosu's framework of the "discovery-invention cycle" to analyze the structure and outcomes of the integrated MD/PhD pr...
Implementation of artificial intelligence in medicine: Status analysis and development suggestions.
Artificial intelligence in medicine
Dec 18, 2019
The general public's attitudes, demands, and expectations regarding medical AI could provide guidance for the future development of medical AI to satisfy the increasing needs of doctors and patients. The objective of this study is to investigate publ...
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