A multi-modal artificial intelligence model may have the ability to accurately stratify patients with prostate cancer into risk subgroups.

Learning Genomics by Fundación ECO & Health in Code

Learning Genomics by Fundación ECO & Health in Code

Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.

Electronic nudges delivered to health care clinicians based on a machine learning algorithm that predicts mortality risk quadrupled rates of conversations with patients about their end-of-life care preferences, according to the long-term results of a randomized clinical trial published by Penn Medicine investigators in JAMA Oncology today. The study also found that the machine learning-triggered reminders significantly decreased use of aggressive chemotherapy and other systemic therapies at end of life, which research shows is associated with poor quality of life and side effects that can lead to unnecessary hospitalizations in their final days.

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Too often, searching for the right cancer treatment is a guess-and-check process, as doctors move through a list of costly therapies that may or may not work until they find one that sticks. But a newly discovered biomarker that can be calculated by an artificial intelligence algorithm from a routine CT scan could one day make that process much more efficient.

A new study shows that an ensemble of machine-learning models can predict six-month mortality for patients with ovarian cancer using patient-reported outcome data.

Learning Genomics by Fundación ECO & Health in Code

Learning Genomics by Fundación ECO & Health in Code