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AI Capabilities Incorporated in ‘Search’ Feature of Microsoft 365

Microsoft introduced personalized search across Office 365 at "Ignite" last year

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Microsoft
Microsoft's beta Android launcher has digital health feature. Pixabay

Microsoft on Monday announced to expand its personalised “Search” feature for both inside and outside “Microsoft 365” by incorporating Artificial Intelligence (AI) technology to make organisational search more effective and the results more accurate.

“Microsoft 365” is an integrated bundle of Windows 10, Office 365 and Enterprise Mobility and Security (EMS) solutions by the tech giant, sold on a subscription basis.

The “Microsoft Search” capability is a feature that intelligently helps users find, discover, command and navigate across an organisation’s network of data.

“Our vision is a cohesive and coherent search capability, prominent in every experience, providing the way to search across all your organisation’s data — both inside and outside of ‘Microsoft 365’,” the company said as it kicked off the annual “Ignite 2018” conference here.

As part of the announcement, “Microsoft Search” would now be available on the company’s web search engine “Bing”, “Office” suite and “SharePoint” mobile app — a web-based collaborative platform used for integration with “Office” and “Outlook” mobile app.

Microsoft
A sign for Microsoft is seen on a building in Cambridge. VOA

The tech giant said in the first half of 2019, the “Office” suite-wide “Search” feature would expand in Windows to allow users perform local and organisational searches concerned with people, flie locations and more.

“In 2019, we will build native connectors for popular third-party applications that will surface search results in line with Microsoft data into all the search experiences including Office, Windows, Edge and Bing.com, allowing admins to select the connectors they wish to use for their organisations,” said the company.

With this facility, Microsoft plans to give organisations the ability to customise search sources, show search results with custom refiners across verticals and alter the display of information appearances in result pages.

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“Using machine reading comprehension technology is just one of the many ways we’ll be continuously improving ‘Microsoft Search’ in the future,” the company said.

Microsoft introduced personalized search across Office 365 at “Ignite” last year. (IANS)

Next Story

With Ovarian Cancer Deaths Set to Spike by 67%, AI to Rescue: Study

However, the scans cannot give clinicians detailed insight into patients’ likely overall outcomes or on the likely effect of a therapeutic intervention

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Cancer
Cancer Ribbon. Pixabay

With the incidence of ovarian cancer likely to increase by 55 per cent in another 15 years or so, researchers have created an artificial intelligence (AI) software to help best treat ovarian cancer that will pave the way for personalised medicine and expedite relief, a new study says.

The mathematical software tool — TEXLab — can also predict what treatment might be most effective for patients with the World Ovarian Cancer Coalition predicting that deaths will likely increase by 67 per cent by 2035 due to this particular cancer.

The technology can be used to identify patients who are unlikely to respond to standard treatments and offer alternatives as ovarian cancer is the sixth most common cancer in women in the UK that usually strikes after menopause or those with a family history of the disease.

Early detection of the disease could improve survival rates, the study noted.

“Long-term survival rate for patients with advanced ovarian cancer is poor despite advancements in treatments. There is an urgent need for new ways,” said lead author Eric Aboagye, Professor at Imperial College London.

For the study, researchers used the software to identify the aggressiveness of tumours in CT scans and tissue samples from 364 women with ovarian cancer.

The patients were then given a score known as Radiomic Prognostic Vector (RPV) which indicates how severe the disease is, ranging from mild to severe.

Cancer patient
Cancer patient.

The findings, published in Nature Communications, showed that the software was up to four times more accurate for predicting deaths from ovarian cancer than standard methods.

In addition, five per cent of patients with high RPV scores had a survival rate of less than two years, results showed.

High RPV was also associated with chemotherapy resistance and poor surgical outcomes, suggesting that RPV can be used as a potential bio-marker to predict how patients would respond to treatments.

“Our technology is able to give clinicians more detailed and accurate information on how the patients are likely to respond to different treatments, which could enable them to make better and more targeted treatment decisions,” said Aboagye.

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Doctors as of now diagnose ovarian cancer in a number of ways, including a blood test followed by a CT scan that uses X-rays and a computer to create detailed pictures of the ovarian tumour.

This helps clinicians know how far the disease has spread and determines the type of treatment patients receive, such as surgery and chemotherapy.

However, the scans cannot give clinicians detailed insight into patients’ likely overall outcomes or on the likely effect of a therapeutic intervention. (IANS)