AIMC Topic: Referral and Consultation

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Constructing a Hospital Department Development-Level Assessment Model: Machine Learning and Expert Consultation Approach in Complex Hospital Data Environments.

JMIR formative research
BACKGROUND: Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data.

Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help p...

AI-initiated second opinions: a framework for advanced caries treatment planning.

BMC oral health
Integrating artificial intelligence (AI) into medical and dental applications can be challenging due to clinicians' distrust of computer predictions and the potential risks associated with erroneous outputs. We introduce the idea of using AI to trigg...

Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.

Journal of assisted reproduction and genetics
PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.

Artificial Intelligence Methods for the Argenta Classification of Deformational Plagiocephaly to Predict Severity and Treatment Recommendation.

The Journal of craniofacial surgery
INTRODUCTION: Deformational plagiocephaly (DP) can be classified into 5 severity types using the Argenta scale (AS). Patients with type III or higher require referral to craniofacial surgery for management. Primary care pediatricians (PCPs) are often...

Machine learning and deep learning for classifying the justification of brain CT referrals.

European radiology
OBJECTIVES: To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and ...

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer.

European urology focus
BACKGROUND: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk...

A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals.

BMC medical informatics and decision making
BACKGROUND: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured...

Blepharoptosis Consultation with Artificial Intelligence: Aesthetic Surgery Advice and Counseling from Chat Generative Pre-Trained Transformer (ChatGPT).

Aesthetic plastic surgery
BACKGROUND: Chat generative pre-trained transformer (ChatGPT) is a publicly available extensive artificial intelligence (AI) language model that leverages deep learning to generate text that mimics human conversations. In this study, the performance ...