Recent developments, specifically artificial intelligence and the ubiquity of smart devices, enable us to monitor cough unobtrusively and continuously for periods of time. Objective cough quantification can be combined with patients’ perceptions to better determine diagnosis, treatment response and prognosis.
AI not only improves data collection and analysis, it impacts which products are engaged in clinical trials, determines necessary medical criteria, helps design the trials and can even choose the best participating facilities. The result is, organizations that leverage AI will be more successful and will go to market faster than those that don’t.
Augmented reality (AR), with the help of artificial intelligence (AI), is providing healthcare professionals with the means to offer patients an unprecedented level of care and personalized treatments, and assisting MedTech and life sciences companies in product design and development. Yet, the potential of AR with AI in health care is still far from fully explored.
Sonio Detect, a manufacturer-agnostic software product that uses artificial intelligence (AI) to enhance the quality of fetal exams, and automatically detect views and quality criteria of ultrasound images has been granted FDA clearance.
The use of artificial intelligence in medical device design is already transforming health care. In this article we look at areas of greatest promise as well as the challenges that must be addressed to realize the promise of AI in device design and engineering.
When considering use cases and data classification methods for machine learning, image quality, power consumption and latency must be considered.
Large sets of data are collected throughout the surgical continuum, but are chief medical officers and perioperative leaders able to use that data to drive clinical, operational, and financial improvements? Embracing data-driven surgery can help HCOs make use of their data to improve care, reduce costs and better manage staffing and workflow.
This week Huma Therapeutics received FDA Class II 510(k) clearance for its Software as a Medical Device (SaMD) platform, potentially speeding approval of a variety of AI and machine-learning (ML)-powered digital health devices. We spoke with Kaushik Gune, U.S. Head of Healthcare at Huma, about the current state of digital health technologies, the value of partnerships to enhance the use of real world data and how digital health is likely to advance in the coming years.
Not all use cases are good candidates for machine learning. In this column we look at cases where AI/ML may be appropriate and when building a traditional algorithm to solve a problem is a better choice.
AAMI and the British Standards Institute (BSI) have jointly published new guidance documents on performing risk management for machine learning or artificial intelligence incorporating medical devices.
