Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 741 to 750 of 213,137 articles

Automated detection of periapical lesions in pediatric panoramic radiographs using YOLOv7: a retrospective internal validation study.

BMC pediatrics
AIM: This study aimed to assess the diagnostic capability of a YOLOv7 deep learning algorithm for the computerized detection of periapical lesions from pediatric panoramic radiographs. Its potential utility as a supportive diagnostic tool and accurat... read more 

Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.

BMC medical informatics and decision making
OBJECTIVE: To develop and validate a machine learning model for predicting ICU mortality in CHF patients with pulmonary infection. METHODS: Clinical data were extracted from the MIMIC-IV database, and ICU mortality within 15 days was defined as the p... read more 

Screening of shared molecular markers between cervical cancer and major depressive disorder and the mechanism of CRAT/CLIC4 regulating EMT in cervical cancer cells.

BMC cancer
BACKGROUND: Cervical cancer (CC) ranks among the most prevalent malignant neoplasms affecting women worldwide. Tumor recurrence, distant metastases, and chemotherapy resistance significantly hinder long-term clinical survival and therapeutic outcomes... read more 

AI-assisted decision support for sickle cell disease severity stratification using routine blood tests: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: Stratifying sickle cell disease (SCD) severity remains challenging, particularly in resource-limited settings. Artificial intelligence (AI) models using routine complete blood count (CBC) parameters have been proposed as accessible tools ... read more 

Beyond visual inspection: can a multimodal machine learning model improve the preoperative differentiation of endometrial polyps from non-polypoid endometrial lesions?

BMC women's health
OBJECTIVE: To develop a multimodal machine learning model that integrates clinical data and ultrasound features to improve the non‑invasive preoperative differentiation between endometrial polyps (EMPs) and non‑polypoid endometrial lesions (including... read more 

Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.

BMC medical informatics and decision making
BACKGROUND: Pediatric rare diseases are highly heterogeneous and are frequently associated with missed or delayed diagnosis, creating substantial burden for patients, families, and clinicians. Although artificial intelligence (AI), including large la... read more 

Development and temporal validation of a machine-learning based risk prediction model for depression in older adults with chewing difficulty: evidence from the KNHANES.

BMC oral health
BACKGROUND: Depression in older adults is a multifactorial condition influenced by demographic, behavioral, and health-related factors. Oral functional problems, such as chewing difficulty, have received relatively limited attention despite potential... read more 

Language-dependent performance variation in large language models for dental trauma management: a comparative evaluation of ChatGPT-5.2, Gemini 3.0, and Claude 4.5 Sonnet.

BMC oral health
BACKGROUND: Large language models (LLMs) are increasingly evaluated for medical question answering and clinical information tasks, yet the impact of query language on their performance in specialized domains such as dental traumatology remains insuff... read more 

Evaluation of parents' attitudes and perceptions regarding the use of artificial intelligence in dental practice: a cross-sectional survey.

BMC oral health
AIM: This study aimed to evaluate the attitudes and perceptions of parents of pediatric patients in our sample towards the growing use of artificial intelligence in dental practices. MATERIALS AND METHODS: The descriptive cross-sectional survey study... read more 

Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.

BioData mining
The advent of high-throughput sequencing technologies has generated increasingly large and complex genomic datasets, necessitating analytical approaches capable of capturing high-dimensional and potentially nonlinear genetic interactions. This situat... read more