Google's HeAR bioacoustic model detects various diseases such as tuberculosis, COVID-19, and chronic obstructive pulmonary disease by analyzing sounds, trained on 313 million audio clips, demonstrating exceptional diagnostic capabilities. HeAR outperforms existing models in multiple health acoustic events, cough, and lung function inference tasks, particularly excelling in detecting COVID-19 and tuberculosis. Salcit Technologies has applied HeAR in its product Swaasa® for lung assessment.

Through deep learning, researchers at MIT have successfully discovered a new type of antibiotic that effectively combats drug-resistant bacteria, which cause more than 10,000 deaths annually. The application of artificial intelligence in the medical field is gaining increasing attention, and MIT's research provides an efficient and resource-effective framework for drug development. Researchers have shared their findings with the startup Phare Bio, planning further data analysis to search for compounds that can kill other bacteria. This study offers mechanical insights into the application of deep learning in antibiotic development and provides a new approach to addressing the problem of antibiotic resistance.

With a surge in global breast cancer cases, early detection is vital. A study by the University of Nottingham compared the accuracy of AI and doctors in interpreting breast X-rays. The commercial AI Lunit performed comparably to human physicians, offering new insights for clinical practice. Breast cancer rates are high, but early intervention can improve survival rates. The results of AI screening for breast X-rays provide strong support for AI in the medical field.