The FDA’s device center has posted its annual fiscal year guidance agenda, and there are several carry-over items from fiscal 2021. The most conspicuous element of the FY 2022 agenda may be that a draft guidance for change control for artificial intelligence (AI) algorithms rates an entry on the B list rather than the A list, suggesting that the draft is not likely to emerge any time in the next 12 months.
The question of bias in artificial intelligence (AI) algorithms is generally thought to be overcome by ensuring that the data set used to train the algorithm is representative of the population at large. However, Naomi Aaronson, executive director of clinical evaluation at the Blue Cross Blue Shield Association, said its not that simple because demographic data can combine in unpredictable ways and thus “the only real understanding of whether it works is in the clinical validation” of the algorithm in various settings.
The COVID-19 pandemic has pushed India’s pharmaceutical and medical device industries towards the more widespread use of blockchain as part of a significant digital transformation effort underpinned by growing use of the Internet of Things (IoT), machine learning (ML) and the use of artificial intelligence (AI).
Scientists at Cleveland-based Case Western Reserve University have used artificial intelligence (AI) to identify biomarkers contained in naturally occurring collagen that could predict whether breast cancer will return after treatment. Identified from standard tissue biopsy slides of early-stage breast cancer, collagen-based assays could also be less expensive than gene expression-based assays typically conducted at highly specialized labs in California.
The FDA is applying a good deal of resources toward a framework for regulation of artificial intelligence (AI) and machine learning (ML), but there are several sources of drag on those efforts. According to the FDA's Jana Delfino, one of these is that there is little agreement between regulators on a number of definitions, including the meaning of terms such as "validation," a problem she said must be solved if the field is to advance in a meaningful manner.
Engine Biosciences Pte. Ltd., a Singapore and Silicon Valley-based company using machine learning, combinatorial genetics and other technologies to hasten the discovery of gene interactions and biological networks underlying disease, has raised $43 million in series A financing. Polaris Partners led the round, which the company said would help it expand its portfolio of precision oncology therapeutics, prepare for its first clinical programs, and scale its technology platform.
The pace at which companies are integrating the sophisticated tools of artificial intelligence (AI) and machine learning (ML) into their drug discovery and development programs continues to accelerate.
The allocation of capital to the build-out of next-generation gene therapies continues apace. Dyno Therapeutics Inc., a leader in applying artificial intelligence to advanced capsid engineering, raised $100 million in a series A round to fund its expansion and that of its Capsidmap platform.
HONG KONG – Shionogi & Co. Ltd. has inked a multitarget drug discovery collaboration for Inveniai LLC’s artificial intelligence and machine learning platform Alphameld.
Soulbrain Holdings Co. Ltd. ramped up its unusual diversification program with the acquisition of Pixcell Medical Technologies Ltd. as part of the semiconductor company's shift into health care and in vitro diagnostics. The acquisition of Pixcell follows Soulbrain’s acquisition of Ark Diagnostics Inc. in 2018 as the company looks to the bio-health care industry as its "engine for new growth."
