Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.

Chest X-rays are the most commonly performed medical imaging exam, yet they are often misinterpreted by physicians. Here, we present an FDA-cleared, artificial intelligence (AI) system which uses a deep learning algorithm to assist physicians in the comprehensive detection and localization of abnormalities on chest X-rays. We trained and tested the AI system on a large dataset, assessed generalizability on publicly available data, and evaluated radiologist and non-radiologist physician accuracy when unaided and aided by the AI system.

Reevaluation of missed lung cancer with artificial intelligence.

Lung cancer is often missed on chest radiographs, despite chest radiography typically being the first imaging modality in the diagnosis pathway. We present a 46 year-old male with chest pain referred for chest X-ray, and initial interpretation reported no abnormality within the patient's lungs. The patient was discharged but returned 4 months later with persistent and worsening symptoms.

Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types.

Background: Missed fractures are the most common diagnostic errors in musculoskeletal imaging and can result in treatment delays and preventable morbidity. Deep learning, a subfield of artificial intelligence, can be used to accurately detect fractures by training algorithms to emulate the judgments of expert clinicians. Deep learning systems that detect fractures are often limited to specific anatomic regions and require regulatory approval to be used in practice.

Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs.

Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation.

Retrieval practice over the long term: should spacing be expanding or equal-interval?

If multiple opportunities are available to review to-be-learned material, should a review occur soon after initial study and recur at progressively expanding intervals, or should the reviews occur at equal intervals? Landauer and Bjork (1978) argued for the superiority of expanding intervals, whereas more recent research has often failed to find any advantage. However, these prior studies have generally compared expanding versus equal-interval training within a single session, and have assessed effects only upon a single final test. We argue that a more generally important goal would be to maintain high average performance over a considerable period of training.

Maximizing students' retention via spaced review: practical guidance from computational models of memory.

During each school semester, students face an onslaught of material to be learned. Students work hard to achieve initial mastery of the material, but when they move on, the newly learned facts, concepts, and skills degrade in memory. Although both students and educators appreciate that review can help stabilize learning, time constraints result in a trade-off between acquiring new knowledge and preserving old knowledge.

Improving students' long-term knowledge retention through personalized review.

Human memory is imperfect; thus, periodic review is required for the long-term preservation of knowledge and skills. However, students at every educational level are challenged by an ever-growing amount of material to review and an ongoing imperative to master new material. We developed a method for efficient, systematic, personalized review that combines statistical techniques for inferring individual differences with a psychological theory of memory.