AIMC Topic: Guideline Adherence
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"Dr. AI Will See You Now": How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?
Clinical orthopaedics and related research
Sep 6, 2024
BACKGROUND: Artificial intelligence (AI) is engineered to emulate tasks that have historically required human interaction and intellect, including learning, pattern recognition, decision-making, and problem-solving. Although AI models like ChatGPT-4 ...
Artificial intelligence and clinical guidance in male reproductive health: ChatGPT4.0's AUA/ASRM guideline compliance evaluation.
Andrology
Jul 17, 2024
BACKGROUND: Male infertility is defined as the inability of a male to achieve a pregnancy in a fertile female by the American Urological Association (AUA) and the American Society for Reproductive Medicine (ASRM). Artificial intelligence, particularl...
Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Jul 2, 2024
PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analy...
Comparative analysis of artificial intelligence chatbot recommendations for urolithiasis management: A study of EAU guideline compliance.
The French journal of urology
Jun 5, 2024
OBJECTIVES: Artificial intelligence (AI) applications are increasingly being utilized by both patients and physicians for accessing medical information. This study focused on the urolithiasis section (pertaining to kidney and ureteral stones) of the ...
Adequacy and Effectiveness of Watson For Oncology in the Treatment of Thyroid Carcinoma.
Frontiers in endocrinology
Mar 3, 2021
BACKGROUND: IBM's Watson for Oncology (WFO) is an artificial intelligence tool that trains by acquiring data from the Memorial Sloan Kettering Cancer Center and learns from test cases and experts. This study aimed to analyze the adequacy and effectiv...
Intelligent checklists improve checklist compliance in the intensive care unit: a prospective before-and-after mixed-method study.
British journal of anaesthesia
Nov 17, 2020
BACKGROUND: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds.
Attitude to Computers
Teaching Rounds
Benchmarking
Patient Safety
Critical Care
Humans
Prospective Studies
Quality Improvement
Intensive Care Units
Artificial Intelligence
Guideline Adherence
Checklist
Quality Indicators, Health Care
Health Status
Practice Patterns, Physicians'
Length of Stay
Practice Guidelines as Topic
Paper
Decision Support Systems, Clinical
Implementation of an Artificial Intelligence Algorithm for sepsis detection.
Revista brasileira de enfermagem
Apr 9, 2020
OBJECTIVES: to present the nurses' experience with technological tools to support the early identification of sepsis.
Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.
Journal of biomedical informatics
Apr 20, 2019
OBJECTIVE: Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated...
Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.
Journal of the American College of Radiology : JACR
Dec 29, 2018
PURPOSE: The aims of this study were to assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language...
Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy.
Artificial intelligence in medicine
Jul 9, 2018
Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also st...
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