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James Zou, PhD, assistant professor of biomedical data science at Stanford University, discusses machine learning and the different ways oncologists are utilizing it for the management, treatment, and diagnosis of cancer.

Machine learning is being applied in both early- and late-stage disease, and aids clinicians in providing the best treatment plans and options for their patients with cancer. In this video, Zou further discusses some of the specific methods the algorithm is trained to look at.

Transcription:

0:09 | Machine learning and artificial intelligence are seeing a lot of applications in oncology. For example, in diagnosis, often the clinicians are working with different kinds of imaging data could be mammography images or CT scans. Machine learning AI algorithms can be very helpful in helping clinicians to analyze those kinds of images for them to identify or to segment relevant regions.

0:39 | There are different stages where machine learning is being applied. They will go all the way from early stages in diagnosis to later stages in terms of treatment planning and treatment recommendations. [On the] diagnosis side, we are seeing a lot of these computer vision algorithms, which is a type of AI or machine learning models that are trained to really understand and analyze different images. For example, now there are algorithms that are looking at histopathology images and slides, and then try to diagnose and predict patient outcomes based on those histology images.

1:18 | There are also algorithms that are trained to look at mammography images and try to detect tumors, legions from these mammography images as other diagnosis sites and other treatment planning sites. People also develop machine learning models that look at, for example, mutation profiles of patients, right from their somatic mutations, and then try to predict based on these mutation profiles if immunotherapy or some other treatments are likely to be a good treatment for this particular patient.

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Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer ... - Nature.com

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Ali Hasan R., ThroughPut Inc., outlines three ways cement manufacturers can use artificial intelligence and machine learning to boost output and efficiency.

Cement manufacturers today face many complex challenges. Amid technological disruption and an intensely competitive landscape, their world has become more regulated and less predictable. As part of an industry highly connected to the environment, cement manufacturers are under rising pressure to lower their carbon impact and meet increasingly complicated environmental regulations.

It is clear that manufacturers in this space need a solution something to help them make progress on their goals and sift through the noise to find the most effective actions to take. Are tools such as artificial intelligence and machine learning the answer?

Looking at manufacturing in general, the science shows that AI-driven tools can help solve problems, cut both costs and waste, and squeeze out efficiencies in difficult times. According to a2022 reportby McKinsey & Company, 42% of organisations that adopted AI in 2021 decreased their manufacturing costs, and 61% saw an increase in revenues.

What about cement manufacturers specifically? AI and Machine Learning (ML) technology can help them get through the specific challenges the industry is facing.

Consider supply chain disruptions. The cement industry involves the large-scale transportation of materials; as such delays and disruptions such as those seen since the pandemic and its associated crises began can wreak havoc on production schedules. ML and AI bring higher levels of data capture and automation to the process, making supply chains more transparent and adaptable. Manufacturers can see ahead, spot problems in the pipeline, and reduce costly downtime.

Improving supply chains is just one example of how AI and ML tools will be able to streamline production, environmental regulations are another. The cement industry is amajor producer of carbon dioxide emissions, but new technologies can show cement manufacturing companies where they can perform carbon capture and where, precisely, they can reduce their carbon footprint and comply with regulations.

However, manufacturers might hesitate as they approach adopting complex technologies. The perceived costs of implementing new AI and ML technologies, at a time when the costs of manufacturing are already perilously high, can turn companies away from technology that could help them. Risk aversion has historically made sense in such a capital-intensive industry, and using new tools at scale could require significant investments in equipment, infrastructure, and training before their benefits appear.

The key for cement manufacturers will be to do their due diligence and only select technologies that make the most sense for their operations.

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Making The Most Out Of Machine Learning - World Cement

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