TEXLab, a mathematical AI software created by scientists at Imperial College London and the University of Melbourne can predict survival rates of patients with ovarian cancer more accurately than any current method, a trial published in Nature Communications has found.
With so many talks and panels occurring across our four stages and Live Lounge, we understand that it can be pretty hard to pick out the most unmissable discussions at the festival this year. Given the conundrum, we thought we’d help out! We’ve selected a couple of talks and panels occurring across the two days which we think will be incredibly interesting and enormously informative for a whole range of people.
A Boston biotechnology company has built a neural network which examines the overall elements of the human face and compiles a list of the ten genetic syndromes that person potentially has, possibly helping medical professionals narrow down the diagnosis for that individual.
A new AI program can predict the symptoms of a patient’s cancer and their severity of the course of treatment.
Machine learning can help healthcare workers predict whether patients may require emergency hospital admission, new study finds
AMP 2018: Decoding the Cancer Genome: Breakthrough AI Technology Quickly Identifies Actionable Mutations
Explosive advances in next-generation sequencing (NGS) have greatly improved the ability to identify actionable cancer mutations, both for solid and hematological malignancies, and sparked a new era of oncology care. But accurate analysis and proper interpretation of the complex genomic data produced by NGS remain key hurdles.
With artificial intelligence, machines can now examine thousands of medical images for signs of disease. Will this technology replace doctors – or work side by side with them?
Verge Genomics, the drug discovery startup we recently featured in our Top 5 Startups Disrupting Healthcare, has raised $32 million in Series A financing.
Personalised medicine has been a goal of researchers and doctors for a long time. Now, researchers have developed what they call a personalised Therapeutic Intervention Fingerprint (pTIF), for patients with neurological disease.
Researchers have created an artificial intelligence system for predicting, not simply tracking, potential side effects from drug combinations.
Phenotyping trailblazers are proving an impressive success in the clinic as the continue to go from strength to strength.
Researchers at Caltech have developed an artificial neural network made out of DNA that can solve a classic machine learning problem: Correctly identifying handwritten numbers.