For a breast screening application, it will always be confirmed with manual review before biopsy.
For screening, it depends on the false positives rate. A radiologist with have to check every positive prediction. Although, I believe in Europe, they have approved AI to be used as a second reader.
It's known as automation bias and a problem in pilots as well as doctors. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651899/
There are healthcare startups around fraud detection, reducing no-shows, telemedicine, drug discovery, and patient triage. Just radiology alone is prime for ML due to existing digital infrastructure and clinical use…
Go can be simulated and self-trained. Self driving cars are robots interacting in the real world with multiple, stochastic agents. Real time hardware systems that are reliable and commercially viable will also be…
Not processing power but a better world model of physics and reasoning. Humans can understand other drivers intentions and when laws can be bent based on the context to avoid dangerous situations.
Humans can do a lot of things computers cannot do. Computers can do a lot of things humans cannot do.
More like deferring to a later batch than kicked out entirely.
AI projects are akin to research. High uncertainty in estimating progress and timelines. Nightmare for project managers accustomed to traditional software development workflows.
Any type of preditive analysis in high dimensional data (medical imaging, surveillance, remote sensing, machine translation, speech recognition/synthesis, music information retrieval). Other important work being done on…
Models to detect strokes in medical images to be deployed in a hospital.
For a breast screening application, it will always be confirmed with manual review before biopsy.
For screening, it depends on the false positives rate. A radiologist with have to check every positive prediction. Although, I believe in Europe, they have approved AI to be used as a second reader.
It's known as automation bias and a problem in pilots as well as doctors. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651899/
There are healthcare startups around fraud detection, reducing no-shows, telemedicine, drug discovery, and patient triage. Just radiology alone is prime for ML due to existing digital infrastructure and clinical use…
Go can be simulated and self-trained. Self driving cars are robots interacting in the real world with multiple, stochastic agents. Real time hardware systems that are reliable and commercially viable will also be…
Not processing power but a better world model of physics and reasoning. Humans can understand other drivers intentions and when laws can be bent based on the context to avoid dangerous situations.
Humans can do a lot of things computers cannot do. Computers can do a lot of things humans cannot do.
More like deferring to a later batch than kicked out entirely.
AI projects are akin to research. High uncertainty in estimating progress and timelines. Nightmare for project managers accustomed to traditional software development workflows.
Any type of preditive analysis in high dimensional data (medical imaging, surveillance, remote sensing, machine translation, speech recognition/synthesis, music information retrieval). Other important work being done on…
Models to detect strokes in medical images to be deployed in a hospital.