Artificial Intelligence in Medical Research

The objective of this study is to develop a prediction model for individualized risks and benefits of a trial of labor after cesarean using modern mathematical techniques.

The results demonstrate that AI shows promise in reducing mortality from Barrett’s esophagus while operating within resource thresholds Australians consider acceptable.

This study assessed whether computer-aided detection use promotes learning or deskilling.

Image-enhanced endoscopy, including dye-based techniques such as indigo carmine chromoendoscopy and digital techniques such as narrow-band imaging, has therefore been a longstanding focus of efforts to improve lesion visibility.

The aim of this study was to evaluate a computer-aided detection system in a varied colonoscopy population.

Endoscopic recognition of early Barrett’s neoplasia may be difficult; assistance for detection of Barrett’s neoplasia has therefore always been one of the most appealing AI applications.

This study evaluated diagnostic performance of an AI-based model in detecting cholangiocarcinoma lesions by location.

The authors developed a deep learning-based artificial intelligence (AI) classifier to distinguish between low-grade and high-grade neoplasia and evaluated its diagnostic performance.

This original article aims to evaluate an AI model detecting the pylorus and ileocolonic junction in Crohn’s disease small bowel capsule endoscopies.

Artificial intelligence is becoming part of everyday conversation in dentistry.

Artificial intelligence and robotics are rapidly integrating into orthodontic practice.

This narrative review critically evaluates current AI applications across key clinical domains, including lesion detection and classification in diagnostic imaging, prediction of surgical difficulty and inferior alveolar nerve injury in impacted third molar surgery, cone-beam computed tomography–based bone assessment and implant planning, automated landmark detection and surgical simulation in orthognathic surgery, classification of temporomandibular joint disorders, and prognostic modeling in oral oncology.

In this narrative review, the authors attempt to unwind the interlacing relationship between medicine and technology in endoscopy.

This paper provides an overview of the current application and prospects of artificial intelligence in the traditional Chinese medicine pharmaceutical field.

The aim of this original paper is to develop an optimal model to predict valproic acid (VPA) concentrations by machine learning, ensuring that the VPA plasma concentration is in the effective treatment range, and thus effectively control the patient’s epilepsy.

The aim was to test whether already established programs for AI-assisted sonography of breast lesions and thyroid nodules are also suitable for identifying and measuring superficial lymph nodes.

In this study, a data mining algorithm was used to calculate epidemiological data and in-hospital prevalence statistics of pulmonary embolism (PE) by analyzing structured CT reports.

This review explores the various applications of AI across the spectrum of care, from diagnosis to clinical workflows, treatment, and monitoring of movement disorders.