AIMC Topic: Artificial Intelligence

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AI revolutions in biology: The joys and perils of AlphaFold.

EMBO reports
AlphaFold is the most ground-breaking application of AI in science so far; it will revolutionize structural biology, but caution is warranted.

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.

Sensors (Basel, Switzerland)
Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identific...

Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review.

BMJ open
INTRODUCTION: The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may...

AI delivers Michaelis constants as fuel for genome-scale metabolic models.

PLoS biology
Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic...

What influences attitudes about artificial intelligence adoption: Evidence from U.S. local officials.

PloS one
Rapid advances in machine learning and related techniques have increased optimism about self-driving cars, autonomous surgery, and other uses of artificial intelligence (AI). But adoption of these technologies is not simply a matter of breakthroughs ...

The augmented radiologist: artificial intelligence in the practice of radiology.

Pediatric radiology
In medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can "see" more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics - thus enabling better c...

Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study.

Journal of medical Internet research
BACKGROUND: Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are...

Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

Nature reviews. Clinical oncology
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challen...

Should we replace radiologists with deep learning? Pigeons, error and trust in medical AI.

Bioethics
The sudden rise in the ability of machine learning methodology, such as deep neural networks, to identify and predict with great accuracy instances of malignant cell growth from radiological images has led prominent developers of this technology, suc...

Meta-learning, social cognition and consciousness in brains and machines.

Neural networks : the official journal of the International Neural Network Society
The intersection between neuroscience and artificial intelligence (AI) research has created synergistic effects in both fields. While neuroscientific discoveries have inspired the development of AI architectures, new ideas and algorithms from AI rese...