#ai-in-healthcare

#ai-in-healthcare

AI has the potential to revolutionize neuroscience, but ethical considerations regarding data usage and privacy are crucial.

Without oversight, AI in healthcare may worsen existing inequities.

The article focuses on securing ML algorithms in healthcare through federated learning and robust protection against intellectual property theft.

Sophisticated large-language models are expanding discussions on the ethical considerations of AI in patient care, especially end-of-life decisions.

Quibim raised $50 million to enhance AI diagnostic accuracy in medical imaging, planning U.S. expansion and development of digital twins of the human body.

The next DA in Alameda County must commit to the Care First initiative to improve public safety and community care.

Enhanced AI algorithm improves diagnosis of REM sleep behavior disorder, affecting over 80 million people globally by accurately analyzing clinical sleep test videos.

AI is increasingly being used to predict viral evolution, which has significant implications for vaccine and antiviral development.

Healthcare marketers must adapt to changing trends like AI, personalization, and brand trust to better engage patients in 2025.

The study addresses the impact of ethnicity on the prediction of lung lesions using chest radiographs.

It emphasizes the importance of fairness in AI healthcare models across different racial subgroups.

AI can significantly impact healthcare by bridging the gap between innovations and clinical practice.

Sword Health is reducing its physical therapist workforce to enhance patient management through AI.

Ataraxis has developed an AI test to precisely differentiate between high-risk and low-risk breast cancer, potentially reducing unnecessary chemotherapy treatments.

Transforming scientific discoveries into effective treatments requires innovative collaboration across disciplines and sectors to address complex healthcare challenges.