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Leptospirosis, a disease that dogs can get from drinking water contaminated with Leptospira bacteria, can cause kidney failure, liver disease and severe bleeding into the lungs. Early detection of the disease is crucial and may mean the difference between life and death.

Veterinarians and researchers at the University of California, Davis, School of Veterinary Medicine have discovered a technique to predict leptospirosis in dogs through the use of artificial intelligence. After many months of testing various models, the team has developed one that outperformed traditional testing methods and provided accurate early detection of the disease. The groundbreaking discovery was published in Journal of Veterinary Diagnostic Investigation.

Traditional testing for Leptospira lacks sensitivity early in the disease process, said lead author Krystle Reagan, a board-certified internal medicine specialist and assistant professor focusing on infectious diseases. Detection also can take more than two weeks because of the need to demonstrate a rise in the level of antibodies in a blood sample. Our AI model eliminates those two roadblocks to a swift and accurate diagnosis.

The research involved historical data of patients at the UC Davis Veterinary Medical Teaching Hospital that had been tested for leptospirosis. Routinely collected blood work from these 413 dogs was used to train an AI prediction model. Over the next year, the hospital treated an additional 53 dogs with suspected leptospirosis. The model correctly identified all nine dogs that were positive for leptospirosis (100% sensitivity). The model also correctly identified approximately 90% of the 44 dogs that were ultimately leptospirosis negative.

The goal for the model is for it to become an online resource for veterinarians to enter patient data and receive a timely prediction.

AI-based, clinical decision making is going to be the future for many aspects of veterinary medicine, said School of Veterinary Medicine Dean Mark Stetter. I am thrilled to see UC Davis veterinarians and scientists leading that charge. We are committed to putting resources behind AI ventures and look forward to partnering with researchers, philanthropists, and industry to advance this science.

Leptospirosis is a life-threatening zoonotic disease, meaning it can transfer from animals to humans. As the disease is also difficult to diagnose in people, Reagan hopes the technology behind this groundbreaking detection model has translational ability into human medicine.

My hope is this technology will be able to recognize cases of leptospirosis in near real time, giving clinicians and owners important information about the disease process and prognosis, said Reagan. As we move forward, we hope to apply AI methods to improve our ability to quickly diagnose other types of infections.

Reagan is a founding member of the schools Artificial Intelligence in Veterinary Medicine Interest Group comprising veterinarians promoting the use of AI in the profession. This research was done in collaboration with members of UC Davis Center for Data Science and Artificial Intelligence Research, led by professor of mathematics Thomas Strohmer. He and his students were involved in the algorithm building. The center strives to bring together world-renowned experts from many fields of study with top data science and AI researchers to advance data science foundations, methods, and applications.

Reagans group is actively pursuing AI for prediction of outcome for other types of infections, including a prediction model for antimicrobial resistant infections, which is a growing problem in veterinary and human medicine. Previously, the group developed an AI algorithm to predict Addisons disease with an accuracy rate greater than 99%.

Other authors include Shaofeng Deng, Junda Sheng, Jamie Sebastian, Zhe Wang, Sara N. Huebner, Louise A. Wenke, Sarah R. Michalak and Jane E. Sykes. Funding support comes from the National Science Foundation.

Continued here:
Using Artificial Intelligence to Predict Life-Threatening Bacterial Disease in Dogs - University of California, Davis

New York, May 23, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence In Drug Discovery Market Size, Share & Trends Analysis Report By Application, By Therapeutic Area, By Region And Segment Forecasts, 2022 - 2030" - https://www.reportlinker.com/p06277978/?utm_source=GNW

Artificial Intelligence In Drug Discovery Market Growth & Trends

The global artificial intelligence in drug discovery market size is expected to reach USD 9.1 billion by 2030. It is expected to expand at a CAGR of 29.4% from 2022 to 2030. The pandemic has made the adoption of AI more widespread in the pharma industry. AI and its related platforms aim to enhance medical imaging and diagnostics, management of chronic diseases, and drug designing. The overall human hours spent would be far more in comparison to the AI system scanning the same data, which reduces overall cost and is a more feasible approach.

The drug optimization and repurposing application segment held the largest revenue share in 2021.AI platforms help in the identification of target proteins for drugs to determine adverse events and possible side effects the drug can have.

Drug molecules can be repurposed to make them more effective and with minimum side effects. Portfolio drugs for a company can be altered and studied using AI platforms at a much faster pace so as to hasten new drug development.

The oncology therapeutic area segment accounted for the largest revenue share in 2021.The majority of the pharmaceutical companies are pairing up with AI start-ups to optimize their cancer research, which is still an unchartered territory and there is a lot to be discovered.

AI systems can identify cancer much earlier than a regular scan would indicate to even a thoroughly trained radiologist.This, in turn, can increase life expectancy and can also help identify markers to be studied for cancer research and drug development.

Patients can be prescribed treatments suited to their genetic composition.

North America dominated the market in 2021.Many tech companies are now investing their money and efforts toward the use of AI in pharmaceutical companies.

Companies like IBM, Microsoft, and other tech giants have formed collaborations with research institutes for faster drug development and clinical trials for multiple indications. Developing countries are also finding cost-effective measures to implement AI technology for their drug development and disease understanding.

Artificial Intelligence In Drug Discovery Market Report Highlights The drug optimization and repurposing application segment dominated the market and accounted for a revenue share of over 50.0% in 2021 By therapeutic area, the oncology segment held the largest revenue share of over 20.0% in 2021. The infectious diseases segment is expected to register the fastest growth rate during the forecast period Asia Pacific is expected to expand at the fastest CAGR of 32.2% from 2022 to 2030. This can be attributed to the increasing adoption of AI among the developing countries in this region as a means to understand diseases and aid drug discovery North America led the market and accounted for a revenue share of over 55.0% in 2021. The U.S. is significantly contributing to the regional market growth as the country has been the forerunner in the artificial intelligence technologyRead the full report: https://www.reportlinker.com/p06277978/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Artificial Intelligence In Drug Discovery Market Size, Share & Trends Analysis Report By Application, By Therapeutic Area, By Region And Segment...

In the third of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in the United Arab Emirates.

What is the UAEs strategy for Artificial Intelligence?

In 2017, the UAE appointed a Minister of State for Artificial Intelligence, H.E. Omar Bin Sultan Al Olama, and issued a national AI strategy seeking to become one of the world leaders in AI by 2031. The strategy sets out 8 objectives including building the UAEs reputation as an AI destination, developing an ecosystem for AI development and deployment, and providing strong governance and effective regulation of AI.

To implement the national AI strategy, the UAE established an Office for Artificial Intelligence, which is responsible for enhancing government performance by investing in AI technologies and tools for application across various sectors.

The UAE Council for Artificial Intelligence and Blockchain was also established to provide advice to the government on the adoption and use of AI, to design policies that promote an AI-friendly ecosystem, to advance research and to promote privatepublic collaboration in that space.

To develop local capabilities in AI and encourage the adoption of AI across government, the UAE has developed a national AI training program, the Artificial Intelligence Program, in collaboration with University of Oxford. This is targeted at government employees but also open generally to UAE residents.

Which are the leading UAE research institutions for Artificial Intelligence?

The worlds first dedicated AI university, the Mohamed Bin Zayed University for Artificial Intelligence or MBZUAI, was established in 2020. It is the first graduate-level, research-based university offering specialised graduate programmes and supporting applied research in AI.

What laws and regulations is the UAE developing for Artificial Intelligence?

There is no specific legislation governing AI or addressing the ethical and legal issues arising from the use of AI (such as liability, privacy, discrimination and data bias). However, recent sector-specific regulations, such as the Federal Data Protection Law, the DIFC Data Protection Law and the Health Data law, all deal with privacy implications of decisions made through machine learning tools. Dubai has implemented non-binding guidelines to provide some regulation and guidance on the development and ethical use of AI. Dubais Ethical AI Toolkit aims to support the development and use of AI in ways that is responsible, boosts innovation and delivers human benefit.

Which are the leading UAE companies for Artificial Intelligence?

The UAE has major industrial and manufacturing sectors, especially in the oil, gas and petrochemicals sector, which are currently the powerhouse of the national economy. By adopting the right AI technologies, these, and other related sectors, are looking to improve their productivity, quality, efficiency, and cost effectiveness.

There are several companies operating in this space in the UAE. One of the leading companies is Group 42 or G42 which is an artificial intelligence and cloud computing company founded in the UAE in 2018. The company is focused on research, development and deployment of AI technologies and partnering across a wide range of sectors including healthcare, finance, oil and gas, aviation and hospitality. The UAEs national oil company, ADNOC, and Dubais water and electricity provider, DEWA, and the flagship carrier, Emirates Airline, are already using AI to optimise their operations.

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Artificial Intelligence in the UAE - Lexology

As world leaders meet in Davos this week for the World Economic Forum, President Alice Gast writes on using AI in the race to meet the SDGs.

President Gast writes: "It is imperative that we put good processes and practices in place to ensure AI is developed in a positive and ethical way to see it adopted and used to its fullest by citizens and governments.

"We must now work together to ensure that artificial intelligence can accelerate progress of the Sustainable Development Goals and help us get back on track to reaching them by 2030."

President Gast highlighted some of the projects at Imperial that are harnessing the potential of AI to reach the SDGs.

Researchers from Google Health, DeepMind, the NHS, Northwestern University and colleagues at Imperial have designed andtrained an AI modelto spot breast cancer from X-ray images.

The computer algorithm, which was trained using mammography images from almost 29,000 women, was shown to be as effective as human radiologists in spotting cancer. At a time whenhealth services around the world are stretchedas they deal with long backlogs of patients following the pandemic, this sort of technology can help ease bottlenecks and improve treatment.

For malaria, a handheld lab-on-a-chip molecular diagnostics systems developed with AI could revolutionize how the disease is detected in remote parts of Africa. The project, which is led by the Digital Diagnostics for Africa Network, brings together collaborators such as MinoHealth AI Labs in Ghana and Imperials Global Development Hub. This technology could help pave the way for universal health coverage and push us towards achieving SDG3.

With an expanding global population,we face challenges around food demand and production not only how to reduce malnourishment but the impact on the planet too, such as deforestation, emissions and biodiversity loss. To meet these needs, the use of artificial intelligence in agriculture is growing rapidly and is enabling farmers to enhance crop production, direct machinery to carry out tasks autonomously, and identify pest infestations before they occur.

Smart sensing technology is also helping farmers use fertilizer more effectively and reduce environmental damage.An exciting research project, funded by the EPSRC, Innovate UK and Cytiva, will help growers optimize timing and amount of fertilizer to use on their crops, taking into account factors like the weather and soil condition. This will reduce the expense and damaging effects of over-fertilizing soil.

Imperial College Business Schoolwill be in Davos alongside the World Economic Forum from 22 - 26 May 2022. They will be holding a series of events on the theme ofTransforming Organisations, Transforming Markets an opportunity to explore some of the most pressing challenges and exciting opportunities facing business today.

One of the salons will focus on 'Delivering AI ethics' and another will focus on 'Decoding digital assets'.

Imperial academics will also be taking part in fringe panels on 'Integrating SDGs in the transition to Web3 technologies' and 'Rethinking Capitalism in the 4th industrial revolution'.

Excerpt from:
Davos 2022: Artificial intelligence is vital in the race to meet the SDGs - Imperial College London

PatentNext Summary:ArtificialIntelligence (AI) Patent Application filings continue theirexplosive growth trend at the U.S. Patent Office (USPTO). At theend of 2020, the USPTO published a report finding an exponentialincrease in the number of patent application filings from 2002 to2018. This trend has continued. In addition, current data showsthat AI-related application filings pertaining to graphics andimaging are taking the lead over AI modeling and simulationapplications.

In the last quarter of 2020, the United States Patent andTrademark Office (USPTO) reported that patent filings forArtificial Intelligence (AI) related inventions more than doubledfrom 2002 to 2018.SeeOffice of the Chief Economist, Inventing AI:Tracking The Diffusion Of Artificial Intelligence With Patents, IPDATA HIGHLIGHTS No. 5 (Oct. 2020).

Since the publication of the USPTO's report almost two yearsago, AI patent application filings have continued their explosivegrowth trend.

The below chart shows filings by Technology ("Tech")Center over time from 2000 to 2022.

Note that the right-most side of the graph slopes down becauseof the 18-month "Publication Delay," during whichinformation for newer patent application filings is not yetpublicly available. See37 CFR 1.211.

The above chart organizes patent application filings byTech Center. As shown, most AI-related patentapplications fall into one of two Tech Centers. First,Tech Center 2100(purple color in theabove graph) includes examiners that handle "ComputerArchitecture and Software" inventions. It is not surprisingthat many AI-related patent applications end up here because TechCenter 2100 includes the specific AI-relatedArt Unit 2120, which handles technology involving "AI& Simulation/Modeling."

Second,Tech Center 2600(red color in the abovegraph) handles "Communications" technology. Tech Center2600 includes several art units devoted to graphic and visualprocessing, such as Art Unit 2615 ("Computer GraphicProcessing") and Art Unit 2660 ("Digital Cameras; ImageAnalysis; Applications; pattern Recognition; Color and Compression;Enhancement and Transformation"). Such Art Units handleAI-related patent applications that involve image processing. Thiscan include the use of a Convolutional Neural Network("CNN") to detect, classify, and/or predict objects intwo-dimensional (2D) and three-dimensional (3D) space.

Together these two Tech Centers receive a majority of theAI-related patent application filings.

For example, during the year 2018, Tech Center 2100 saw1,733 filings, and Tech Center 2600 saw 1,416 filings.

Later, in the year 2020, these two centers still saw the mostfilings but were reversed in respective rankings as to the numberof filings, where Tech Center 2100 saw 2,152 filings, and TechCenter 2600 saw 2,542 filings.

This suggests that graphical or image-related AI patentapplication filings have overtaken non-graphical or non-imageAI-related filings by the year 2020.

As good news for AI inventors, these two Tech Centers experiencehigh percentages of allowance. The below chart shows the patentapplication allowance rate by Tech Center.

As shown above, AI-related patent applications handled by TechCenter 2100 (purple bar in the above graph) has a relatively highallowance rate (84%). AI-related patent applications handled byTech Center 2600 (red bar in the above graph) has an even higherallowance rate (91%).

This finding can be contrasted toTech Center 3600(yellow bar in the abovegraph), which handles patent applications for a mix ofbusiness-related technologies, i.e.: "Transportation,Construction, Electronic Commerce, Agriculture, National Securityand License and Review."

Certain art units of Tech Center 3600, such as Art Unit 3620("Business Methods"), are infamous for issuingpatent-eligibility rejections pursuant to 35 USC 101. Suchrejections can be difficult to overcome and explains the much lowerallowance rate of 67% for Tech Center 3600.

Accordingly, staying out of Tech Center 3600 remains a viablestrategy for patentees.

To the extent the reader is interested in accomplishing this,please see PatentNext's articles on best practices forpatenting AI inventions. SeeHow to Patent an Artificial Intelligence (AI)Invention: Guidance from the U.S. Patent Office(USPTO)andHow to Patent Software Inventions: Show an"Improvement."

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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Artificial Intelligence (AI) Patent Filings Continue Explosive Growth Trend At The USPTO - Patent - United States - Mondaq