Ludwig Cancer Research scientists have developed a full, start-to-finish computational pipeline that integrates multiple molecular and genetic analyses of tumors and the specific molecular targets of T cells and harnesses artificial intelligence algorithms to use its output to design personalized cancer vaccines for patients.

The design, validation and comparative assessment of this computational suite, NeoDisc, are detailed in the current issue of Nature Biotechnology in a publication led by Florian Huber and Michal Bassani-Sternberg of the Lausanne Branch of the Ludwig Institute for Cancer Research.

If ChatGPT were cut loose in the Emergency Department, it might suggest unneeded x-rays and antibiotics for some patients and admit others who didn't require hospital treatment, a new study from UC San Francisco has found.

The researchers said that, while the model could be prompted in ways that make its responses more accurate, it's still no match for the clinical judgment of a human doctor.

A recent position paper in the Asia-Pacific Journal of Ophthalmology explores the transformative potential of artificial intelligence (AI) in ophthalmology. Led by Lama Al-Aswad, Professor of Ophthalmology and Irene Heinz Given and John La Porte Given Research Professor of Ophthalmology II, of the Scheie Eye Institute, the work represents a collaboration among researchers from Penn Engineering, Penn Medicine, the University of Michigan Kellogg Eye Center, St. John Eye Hospital in Jerusalem, and Gyeongsang National University College of Medicine in Korea.

Generative AI should be able to write usable doctor's letters and thus potentially speed up medical documentation, according to a study by the University Medical Center Freiburg. Around 93% of the AI-generated reports could have been used with only minimal adaptations, the researchers found.

As artificial intelligence advances, its uses and capabilities in real-world applications continue to reach new heights that may even surpass human expertise. In the field of radiology, where a correct diagnosis is crucial to ensure proper patient care, large language models, such as ChatGPT, could improve accuracy or at least offer a good second opinion.

It has been estimated that nearly 300 million people, or about 4% of the global population, are afflicted by some form of depression. But detecting it can be difficult, particularly when those affected don’t (or won't) report negative feelings to friends, family or clinicians.

Now Stevens professor Sang Won Bae is working on several AI-powered smartphone applications and systems that could non-invasively warn us, and others, that we may be becoming depressed.

Generative artificial intelligence (genAI) uses hundreds of millions, sometimes billions, of data points to train itself to produce realistic and innovative outputs that can mimic human-created content. Its applications include personalized recommendations for online shoppers, creating audio and visual content and accelerating engineering design.