Since its introduction to the public in November 2022, ChatGPT, an artificial intelligence system, has substantially grown in use, creating written stories, graphics, art and more with just a short prompt from the user. But when it comes to scientific, peer-reviewed research, could the tool be useful?

Generative artificial intelligence platforms, from ChatGPT to Midjourney, grabbed headlines in 2023. But GenAI can do more than create collaged images and help write emails - it can also design new drugs to treat disease.

Today, scientists use advanced technology to design new synthetic drug compounds with the right properties and characteristics, also known as "de novo drug design." However, current methods can be labor-, time-, and cost-intensive.

Artificial Intelligence (AI) can predict whether adult patients with brain cancer will survive more than eight months after receiving radiotherapy treatment.

The use of the AI to successfully predict patient outcomes would allow clinicians to be better informed for planning the next stage of treatment and refer patients to potentially life-saving treatment quicker.

Ever wonder if the latest and greatest artificial intelligence (AI) tool you read about in the morning paper is going to save your life? A new study published in JAMA led by John W. Ayers, Ph.D., of the Qualcomm Institute within the University of California San Diego, finds that question can be difficult to answer since AI products in healthcare do not universally undergo any externally evaluated approval process assessing how it might benefit patient outcomes before coming to market.

The application of AI in precision oncology has so far been largely confined to the development of new drugs and had only limited impact on the personalisation of therapies. New AI-based approaches are increasingly being applied to the planning and implementation of personalised drug and cell therapies.

Chemists of the University of Amsterdam (UvA) have developed an autonomous chemical synthesis robot with an integrated AI-driven machine learning unit. Dubbed 'RoboChem', the benchtop device can outperform a human chemist in terms of speed and accuracy while also displaying a high level of ingenuity.

In a groundbreaking study published on January 18, 2024, in Cancer Discovery, scientists at University of California San Diego School of Medicine leveraged a machine learning algorithm to tackle one of the biggest challenges facing cancer researchers: predicting when cancer will resist chemotherapy.

All cells, including cancer cells, rely on complex molecular machinery to replicate DNA as part of normal cell division.