Combining AI-driven precision, an integrated knowledge hub, and actionable insights, a new AI discovery service by Ovid® is revolutionizing how healthcare and academic institutions manage research challenges. With tools designed to streamline workflows, enhance retrieval accuracy, and synthesize information, it ensures that institutions stay at the forefront of medical and academic advancements.

Understanding the challenges in research management

Healthcare and academic institutions operate as constantly evolving ecosystems powered by ongoing research and innovation. However, outdated tools and fragmented systems often hinder progress, leading to inefficiencies in critical workflows. Before implementing solutions, it is essential to fully comprehend the key challenges institutions face:

1. Siloed resources restrict innovation

Institutions often house thousands of vital resources — articles, guidelines, clinical tools — but these remain scattered across disconnected systems. Navigating this complex landscape is not only time-consuming but also limits the full potential of groundbreaking research.

2. Time constraints hamper impactful decision-making

Healthcare professionals and academic researchers alike face incredible pressure to deliver fast, precise outcomes. With traditional search systems requiring manual efforts to filter relevant data, precious time is wasted sifting through irrelevant or outdated resources.

3. Inefficient processes lead to missed opportunities

Fragmented research workflows create operational bottlenecks, delaying critical discoveries and increasing the risk of oversight in clinical and academic settings. Building more unified and efficient systems is essential to maximizing outcomes.

The Ovid Discovery AI solution

Ovid Discovery AI addresses these issues through a powerful combination of cutting-edge technology and user-centric design. By aligning directly with institutional needs, it transforms workflows into seamless processes, accelerates research, and empowers decision-makers with actionable insights.

1. Find what you need, fast

AI-powered search and contextual matching gets to the true meaning behind search queries, delivering relevant results and concise summaries, reducing irrelevant noise. Your AI Results Analysis Assistant also extracts study details and publication quality metrics so you can quickly assess evidence strength and make informed decisions with confidence.

AI biomedical semantic search & facets

Artificial intelligence transforms queries and content into semantic vectors — allowing the platform to return highly relevant results, even when different terminology is used. Users can refine their search with biomedical facets mapped to MeSH categories like diseases, drugs, and more.

AI contextual matching

Whether users search “impact of alcohol on depression” or “mental health effects of drinking,” Ovid Discovery AI surfaces the most meaningful research — not irrelevant noise.

AI generated summaries

Each result is accompanied by a reliable, AI-generated summary that synthesizes the most important takeaways and cites supporting sources — saving users time and guiding their next steps.

AI-powered search suggestions

Based on query context and user intent, the platform dynamically generates related search suggestions, guiding users toward deeper discovery.

AI Results Analysis Assistant

Support with assessing impact, methods, and outcomes at a glance. Suggested queries and main concepts guide deeper exploration, delivering the most relevant evidence with exceptional speed and accuracy.

2. Centralize all your resources

At the heart of Ovid Discovery AI is its centralized repository, which transforms fragmented institutional assets into an accessible, unified knowledge hub. Rather than navigating disparate systems, users can instantly access all licensed library resources alongside organizational best practices and proprietary documents.

This customizable repository removes traditional barriers to information access, enabling students, researchers, and clinicians to focus on leveraging knowledge rather than hunting for resources.

Plus, for organizations who have an Ovid® Synthesis subscription, users can Seamlessly send search results into new or existing projects in Ovid Synthesis directly from the Discovery interface. With a single click, users can begin analyzing and synthesizing the literature they just found — no downloads or separate systems required.

Then, finalized project summaries from Ovid Synthesis can be exported as PDFs and uploaded back into Ovid Discovery — making institutional evidence searchable, citable, and accessible to the broader organization or the public. Finally, administrators can view project activity, progress, and output in a standardized format, enabling more effective oversight across departments and initiatives.

3. Gain actionable library insights

Medical libraries can gather intelligence on resource usage and user behavior, enabling informed decision-making on content acquisition and library resource allocation, maximizing ROI. With the 360º Insights Dashboard and personalized reporting available, institutions can track resource usage, optimize content acquisition strategies, and identify emerging needs through data analytics embedded within the platform.

Unwavering support

A new standard for research excellence

Whether you are driving clinical excellence or conducting groundbreaking academic studies, Ovid Discovery AI is the ultimate tool for transforming your processes. From addressing outdated infrastructure to introducing streamlined workflows powered by AI, it sets a new benchmark for innovation and reliability.

The advanced search technologies, seamless centralized repository, integration across research platforms, and data-driven insights make it the definitive platform for institutions aiming to optimize outputs while minimizing inefficiencies. With this level of precision and efficiency, Ovid Discovery AI ensures users can access, analyze, and apply high-quality evidence to achieve results that matter.

At Wolters Kluwer Health, Customer Support is committed to your success. You’ll have a dedicated consultant and implementation team to ensure a quick and customizable setup process, which takes about two weeks. Additionally, Ovid Support is there for you throughout the entire service lifecycle, available 24/7/365.

In recent months, a surge in unidentified flying object (UFO) sightings has captured the attention of not just conspiracy theorists but also some of the world’s most powerful technology companies. According to a report from MSN, tech giants including Google, Amazon, and Microsoft are channeling billions of dollars into research initiatives aimed at investigating these phenomena, blending cutting-edge AI with aerospace expertise to decode what could be extraterrestrial signals or advanced human technology.

This investment wave comes amid a dramatic uptick in reported UFO incidents, with over 2,000 sightings documented in the first half of 2025 alone, as detailed in data from the National UFO Reporting Center cited by The Gateway Pundit. Industry insiders suggest that these firms are not merely chasing curiosities; they’re positioning themselves to capitalize on potential breakthroughs in propulsion systems, energy sources, and surveillance tech derived from UFO analyses.

Rising Interest from Silicon Valley

The push into UFO probing represents a strategic pivot for tech behemoths traditionally focused on cloud computing and machine learning. Sources familiar with the matter indicate that Google’s parent company, Alphabet, has allocated upwards of $1.5 billion through its X lab division for drone fleets equipped with spectral imaging to track anomalous aerial activities, building on patterns observed in sightings that NewsNation reports have prompted bipartisan calls for Pentagon transparency.

Meanwhile, Amazon’s involvement ties into its vast satellite network via Project Amelia, which insiders say is repurposing Starlink-like constellations to monitor global hotspots for UFO activity. This aligns with a broader trend where private capital is filling gaps left by government programs, as evidenced by investments in startups like Enigma Labs, which has received funding from Silicon Valley proxies, according to posts on X highlighting ties to former intelligence officials.

Collaborations with Defense Contractors

Partnerships with aerospace giants such as Lockheed Martin and Boeing are accelerating these efforts, with joint ventures exploring reverse-engineered technologies from alleged UFO retrievals. A filing noted on X for an ‘alien tech’ ETF by Tuttle Capital underscores how defense contractors are eyeing commercial applications, potentially revolutionizing fields like quantum computing and hypersonic travel.

Critics, however, warn of overhyped narratives. A former NASA official, quoted in Fox News, dismisses many claims as military secrecy, arguing that interstellar explanations overlook practical barriers like 70,000-year travel times. Yet, the financial commitment persists, with Microsoft integrating UFO data into its Azure platform for AI-driven pattern recognition, aiming to differentiate genuine anomalies from drones or atmospheric illusions.

Economic Implications and Future Outlook

The economic stakes are immense, with analysts projecting that successful UFO-derived innovations could add trillions to global markets by 2030. RAND Corporation’s commentary on long-term space investments, available at RAND, emphasizes the need for sustained funding in exoplanet research, which indirectly supports these probes by providing chemical signature baselines for alien tech detection.

As sightings continue to climb—nearly 3,000 reported in 2025 per UFO Matrix—tech leaders are betting that unraveling these mysteries will yield proprietary advantages. Whether this leads to disclosure or disappointment, the influx of billions signals a new era where Silicon Valley’s ambitions extend beyond Earth, potentially reshaping industries from defense to energy. Insiders expect more revelations as government reports, like Canada’s Sky Canada Project detailed in CTV News, push for standardized investigations.

NEWS RELEASE

UNIVERSITY OF WATERLOO

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Professional sports teams pour millions of dollars into data analytics, using advanced tracking systems to study every sprint, pass, and decision on the field. The results of that analysis, however, are industry secrets, making many sports difficult for researchers to study. 

Now, two University of Waterloo researchers, Dr. David Radke and Kyle Tilbury, are using AI to level the playing field. 

By tapping into Google Research Football’s reinforcement learning environment, the researchers developed a system that can simulate and record unlimited soccer matches. To get things started, they generated and saved data from 3,000 simulated soccer games, resulting in a rich and complex dataset of passes, goals, and player movements for researchers to study. 

“While researchers have access to a lot of data about episodic sports like baseball, continuous invasion-game sports like soccer and hockey are much more difficult to analyze,” said Radke, a recent Waterloo PhD graduate in computer science and currently a senior research scientist for the NHL’s Chicago Blackhawks. 

“While the AI-generated players might not exactly play like Lionel Messi, the simulated datasets they generate are still useful for developing sports analysis tools.”

Datasets like the ones generated by the team are particularly useful for researchers, enthusiastic fans, and smaller research teams that may not have extensive access to proprietary sports data.

“Enabling researchers to have this data will open up all kinds of opportunities,” said Tilbury, a Waterloo PhD student in computer science who equally co-authored the research. “It’s a democratization of access to this kind of sports analytics data. 

While datasets like the one generated by the team are particularly interesting for sports enthusiasts, they have larger implications for AI research as well. 

“At its core, invasion-game sports analytics is about understanding complex multiagent systems,” Radke said. “The better we are at modelling the complexity of human behaviour in a sporting situation, the more useful that is for AI research. In turn, more advanced multiagent systems will help us better understand invasion-game sports.” 

Radke and the team believe the future of sports analytics relies on progress in the space of tracking data. They therefore hope researchers interested in sports without access to tracking data will utilize their datasets and repository to gain experience working with this type of data.

The study, “Simulating tracking data to advance sports analytics research,” appeared in the proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems.

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