Pioneering research has unveiled a powerful new tool in the fight against skin cancer, combining cutting-edge artificial intelligence (AI) with deep learning to enhance the precision of skin lesion classification. This innovative approach, which utilizes a weighted ensemble of transfer learning models and test time augmentation (TTA), promises to significantly improve the accuracy of skin cancer diagnosis.

Physical examinations are important diagnostic tools that can reveal critical insights into a patient's health, but complex conditions may be overlooked if a clinician lacks specialized training in that area. While previous research has investigated using large language models (LLMs) as tools to aid in providing diagnoses, their use in physical exams remains untapped.

Almost all leading large language models or "chatbots" show signs of mild cognitive impairment in tests widely used to spot early signs of dementia, finds a study in the Christmas issue of The BMJ.

The results also show that "older" versions of chatbots, like older patients, tend to perform worse on the tests. The authors say these findings "challenge the assumption that artificial intelligence will soon replace human doctors."

Patients will be better able to benefit from innovations in medical artificial intelligence (AI) if a new set of internationally-agreed recommendations are followed.

A new set of recommendations published in The Lancet Digital Health and NEJM AI aims to help improve the way datasets are used to build Artificial intelligence (AI) health technologies and reduce the risk of potential AI bias.

The slow adoption of blockchain technology is partly driven by overhyped promises that often obscure the complex technological, organisational, and environmental challenges, according to research from the University of Surrey.

Blockchain is a secure digital ledger that records and verifies transactions across many computers in a way that's hard to alter.

A joint research team from the University of Canberra and Kuwait College of Science and Technology has achieved groundbreaking detection of Parkinson's disease with near-perfect accuracy, simply by analyzing brain responses to emotional situations like watching video clips or images. The findings offer an objective way to diagnose the debilitating movement disorder, instead of relying on clinical expertise and patient self-assessments, potentially enhancing treatment options and overall well-being for those affected by Parkinson's disease.

Using just one inhalation lung CT scan, a deep learning model can accurately diagnose and stage chronic obstructive pulmonary disease (COPD), according to a study published today in Radiology: Cardiothoracic Imaging, a journal of the Radiological Society of North America (RSNA).

COPD is a group of progressive lung diseases that impair a person's ability to breathe.