AIMC Journal:
JAMA network open

Showing 161 to 170 of 245 articles

Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers.

JAMA network open
IMPORTANCE: Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and inst...

Assessment of Facial Morphologic Features in Patients With Congenital Adrenal Hyperplasia Using Deep Learning.

JAMA network open
IMPORTANCE: Congenital adrenal hyperplasia (CAH) is the most common primary adrenal insufficiency in children, involving excess androgens secondary to disrupted steroidogenesis as early as the seventh gestational week of life. Although structural bra...

Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine.

JAMA network open
IMPORTANCE: Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotio...

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies.

JAMA network open
IMPORTANCE: Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored.

Development of a Dynamic Diagnosis Grading System for Infertility Using Machine Learning.

JAMA network open
IMPORTANCE: Many indicators need to be considered when judging the condition of patients with infertility, which makes diagnosis and treatment complicated.

Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality.

JAMA network open
IMPORTANCE: Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use.