WPI researchers, who are developing a tool to analyze skin images for signs of skin cancer, have finished third in an international artificial intelligence (AI) competition aimed at improving early diagnosis of melanoma, a potentially lethal type of skin cancer.

October 20–22, Abu Dhabi

Insilico Medicine will make an appearance at the Abu Dhabi Future Health Summit, held at the Abu Dhabi National Exhibition Centre (ADNEC) from October 20 to 22, and will welcome visitors at Booth 3A00.

October 23–24, Dubai | Regenerative Medicine Congress 2026

Dr. Alex Aliper, Co-founder and President of Insilico Medicine, has been invited to attend the Regenerative Medicine Congress 2026 in Dubai. He will deliver a speech titled "Generative and Automation AI for Drug Discovery and Longevity" on the afternoon of October 23 at 4:20 PM.

Respiratory disease in children is a significant public health concern. Conventional assessments require in-person clinic visits and specialized equipment. Smartphones, on the other hand, could be practical for home-based screening. However, existing models trained on electronic-stethoscope data face challenges when applied to smartphone audio. In a new study, researchers developed a domain-adaptive deep learning framework that adapts information from electronic-stethoscope recordings to smartphone audio, supporting smartphone-based abnormal lung sound detection with potential for remote screening. 

Aston University researchers have contributed to an AI adoption and evaluation guidance to help legal organisations assess, implement and increase the use of artificial intelligence with more confidence and clarity.

To build faster and more energy-efficient computers, researchers need to understand nanoscale magnetic materials. But looking inside these tiny magnets is surprisingly difficult: modern microscopes only measure the magnetic field outside the material, not the magnetic pattern inside. Because many different internal patterns can produce the exact same field, working backwards has long been a challenge. Physicists at the University of Vienna, together with colleagues in Dresden, Singapore, and Ulsan, have now solved this problem by building the laws of physics directly into modern machine learning methods. Their method reveals the realistic magnetic structure and even determines the precise distance between the sensor and the sample. The study is published in Physical Review Letters, and the software is freely available as open source.