Artificial Intelligence Medical Compendium

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Translating artificial intelligence (AI) from virtual implant planning to dental implant surgical execution is obstructed by technical, clinical, and socioethical barriers. This critical review appraises these impediments, focusing on tomographic dat... read more 

Oral potentially malignant disorders (OPMDs) are heterogeneous conditions with a significantly increased risk of developing oral squamous cell carcinoma. Traditional risk assessment methods are often subjective, largely unidimensional, and insufficie... read more 

RATIONALE AND OBJECTIVES: Patients with type 2 diabetes mellitus (T2DM) are at high risk of acute ischemic stroke (AIS), yet accurate risk stratification remains challenging. This study aimed to develop and validate a multimodal model based on caroti... read more 

Gliomas represent one of the most formidable challenges in oncology due to their diffuse infiltrative growth in the brain, marked molecular heterogeneity, immune evasion, and the restrictive nature of the blood-brain barrier (BBB), which collectively... read more 

OBJECTIVE: This study aimed to develop a predictive model for left atrial appendage thrombus (LAAT) in patients with non-valvular atrial fibrillation by integrating clinical and echocardiographic features using machine learning algorithms. The SHaple... read more 

Accurate identification of the most native-like conformation remains a critical bottleneck in protein complex structure prediction, particularly when multiple globally plausible candidate models are generated by deep learning methods such as AlphaFol... read more 

The past decade has seen machine learning play an increasingly important role in medicine. However, many of the state-of-the-art models being developed and deployed suffer from a lack of transparency, limiting integration into the clinical decision-m... read more