LONDON – Consumer smartphone apps that use image processing algorithms to assess and monitor potentially cancerous skin lesions have not been properly tested in clinical trials and cannot be relied on to produce accurate results, according to a systematic review of published studies.
Multiple tactics employed by the biopharma industry to improve the recruitment and retention of participants in clinical trials seem to be paying off. More than three of four (77%) studies now fully enroll on or ahead of schedule, according to researchers at the Tufts Center for the Study of Drug Development (CSDD), reporting in the January/February Tufts CSDD Impact Report on global recruitment performance benchmarks.
PARIS – EY SAS has published the results of the first edition of a barometer dedicated to the role of artificial intelligence (AI) in French public hospitals. The health care sector, which is undergoing wholesale change in France, is suffering tight economic constraints and faces ever-increasing expectations from patients. “The development of [AI] in France is a priority. It's a matter of gauging it,” Loïc Chabanier, an EY partner responsible for health care, told BioWorld.
When Zebiai Therapeutics Inc.’s CEO, Rick Wagner, went about naming his new machine learning company, he wanted it to connote something dramatic that displayed the company’s potential to reach into the seemingly boundless future technology had unlocked.
What does the landscape look like in terms of funding for digital health? Geoffrey Starr, a partner at Cooley LLP, dove into this question during the Digital Health Summit, part of CES 2020. He acknowledged that 2019 saw a slight dip in funding compared with the record-breaking previous year. With that said, it was the second largest year ever for digital health care financings, with more than one-third of all health care venture financings involving digital health technologies.
Artificial intelligence (AI) is better than humans at pattern recognition within images and other densely complex datasets. That fact has long been expected to translate into meaningful change in the way we interpret health care data, but beyond a few early exceptions that is not yet the case. Now, the research is starting to amass that demonstrates the real potential for machine learning to significantly improve diagnostics and treatment.
The next wave of drug discovery is being enabled by artificial intelligence (AI). This fact has not been lost on investors, who are keeping a close watch on emerging biopharma companies that are using AI and machine learning to enable the discovery of next-generation medicines.
For biopharma, 2019 can be described as a terrific year – with a few asterisks. The financial markets were flourishing, with venture capital dollars, in particular, flowing to the sector, while dealmaking reached historic proportions. Meanwhile, scientific breakthroughs led the way as cell and gene therapies gained ground, the first signs of success emerged with new technologies like CRISPR and the long-awaited promise of genomics found its way to the front lines of health care.
Machine learning and artificial intelligence (AI) are already being actively used in drug discovery to evaluate potential binding of small-molecule drugs to proteins, but there's potential for the technologies to be used on the development side as well, especially in hard-to-treat diseases such as Alzheimer's disease.