AI Research
This Blue Valley teen uses AI to research cancer. Trump’s budget cuts could halt his work
By Jodi Fortino
Matthew Chen, a senior at Blue Valley North High School, knows the importance of cancer research firsthand — he’s been working with the University of Kansas Cancer Center for two years to look into the disease.
He also joined cancer survivors and medical experts with the American Cancer Society Cancer Action Network last month in Washington, D.C., to advocate for cancer research funding following the threat of federal budget cuts.
The Trump administration has proposed slashing billions of dollars from the National Institutes of Health budget for 2026 and cutting nearly 40% of the National Cancer Institute’s funding.
At a time when more than 2 million new cancer cases will be diagnosed this year and more than 600,000 people will die from the disease, Chen said lawmakers should be putting more into the agency’s budget and not cutting back.
“These numbers are extremely high, and now, more than ever, we need increased efforts on cancer research for better treatments in cancer prevention,” Chen said. “Because cancer can affect anybody.”
Chen, 16, got his start with the KU Cancer Center by volunteering at cancer screening events, eventually moving into cancer research.
At first, Chen spent time experimenting with artificial intelligence and coding in different programming languages. That would later become his focus to help predict patient outcomes, side effects and quality of life from cancer treatment.
After studying existing research, he began writing code, training computer models and spending hours collecting data on patient outcomes. One of his projects looked at where people live and how that impacts their ability to afford cancer treatment.
Another AI model he built tracks lymphocytes, a type of white blood cell, in patients across their treatment.
“It helps doctors to sort of tailor the treatment that patients receive based on what side effects, or the severity of the reaction that they’re predicted to have,” Chen said.
In Washington, D.C.
Chen attended a U.S. Senate Appropriations subcommittee hearing last month during which lawmakers reviewed Trump’s budget request to shrink NIH funding. He said he was relieved to hear bipartisan support from legislators, including from U.S. Sen. Jerry Moran of Kansas, to whom he spoke after the event.
“It was great to feel like I personally am making a difference, and to let Senator Moran know that his constituents care a lot about this issue,” Chen said.
National cuts to cancer research could directly impact Kansans, Chen said. The KU Cancer Center is the state’s only NCI-designated cancer center, and it provides residents with access to clinical trials and education on cancer prevention and detection, he said.
Megan Word, the government relations director for ACS CAN in Kansas and Nebraska, said the cancer center also gives patients access to newer treatments and more specialists who treat specific cancers.
The American Cancer Society also has a Hope Lodge in Kansas City, where Word said people who travel more than 50 miles for treatment can stay free of charge.
Word said Kansans are lucky to have those resources, and the group is working to ensure the whole state can access them.
“We can’t do that without research funding. We can’t do that without early detection, prevention screening programs,” Word said. “To look and try to estimate how we’re going to keep that support system in place if we’re looking at a reduction as large as the president has proposed, it’s really unbelievable.”
Funding cuts
Additional federal cuts could impact other cancer prevention efforts across the state.
Trump proposed cutting $4 billion from the Centers for Disease Control and Prevention, which funds the Kansas registry that tracks how often residents are diagnosed with cancer, what type they have and their survival outcomes.
Word said some of the state’s cancer prevention programs have already been impacted by earlier cuts and layoffs.
The Department of Health and Human Services closed the CDC’s Office on Smoking and Health, which Word said shutters funding that went toward states’ work on tobacco cessation and prevention education.
Word said more cuts to the CDC under Trump’s budget proposal could threaten the state’s program to detect breast and cervical cancer.
“The current actions that have frozen funding, slash staffing and talk of future funding cuts for lifesaving cancer research is unacceptable. These cuts will have life-threatening consequences,” Word said. “That means fewer people will have access to clinical trials. Researchers on the cusp of new discoveries will be forced to shut off the lights.”
For now, Chen said he’s grateful for the opportunity to get hands-on experience in the biomedical field and to learn firsthand the importance of that work.
Chen said he’s been so inspired by his work that he hopes cancer research will be part of whatever job he pursues in the future.
“If there are these severe cuts, that might not be an option for future generations, and I want to ensure that it is because it’s such a great opportunity, and it helps not only high schoolers, but it helps cancer patients as well,” Chen said.
As KCUR’s education reporter, I cover how the economy, housing and school funding shape kids’ education. I’ll meet teachers, students and their families where they are — late night board meetings, in the classroom or in their homes — to break down the big decisions and cover what matters most to you. You can reach me at jodifortino@kcur.org.
AI Research
Artificial Intelligence News for the Week of July 11; Updates from Capgemini, Cerebras, Cloudian & More
Solutions Review Executive Editor Tim King curated this list of notable artificial intelligence news for the week of July 11, 2025.
Keeping tabs on all the most relevant artificial intelligence news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy artificial intelligence news items.
For early access to all the expert insights published on Solutions Review, join Insight Jam, a community dedicated to enabling the human conversation on AI.
Artificial Intelligence News for the Week of July 11, 2025
Accenture and Microsoft Expand Cybersecurity Partnership with GenAI Solutions
Accenture and Microsoft have deepened their partnership to deliver generative AI-powered cybersecurity solutions. The collaboration focuses on modernizing security operations, automating data protection, and enhancing identity and access management. By combining Accenture’s cybersecurity expertise with Microsoft’s security technologies, the alliance aims to help organizations tackle advanced threats, optimize security tools, and reduce operational costs.
Read the full article: Accenture & Microsoft Cyber Collaboration
Capgemini to Acquire WNS, Creating a Global Agentic AI Powerhouse
Capgemini has announced its acquisition of WNS for $3.3 billion, aiming to become a global leader in agentic AI-powered intelligent operations. The deal will combine Capgemini’s and WNS’s strengths in digital business process services (BPS), blending vertical sector expertise with scale to address the rapidly growing demand for AI-driven transformation.
Read the full press release: Capgemini to acquire WNS
Cerebras Launches Qwen3-235B: The World’s Fastest Frontier AI Model with 131K Context
Cerebras has unveiled Qwen3-235B, a groundbreaking AI reasoning model now available on the Cerebras Inference Cloud. Boasting a massive 131,000-token context window, Qwen3-235B delivers code generation and reasoning at 30 times the speed and one-tenth the cost of leading closed-source alternatives.
Read the full press release: Cerebras Launches Qwen3-235B
CapStorm Launches CapStorm:AI for Secure, Self-Hosted Data Insights
CapStorm has unveiled CapStorm:AI, a self-hosted AI solution that allows organizations to interact with their Salesforce and SQL data using natural language. The platform delivers real-time dashboards and insights without coding, keeping all data within the organization’s environment for maximum security and control. CapStorm:AI works with leading SQL databases and cloud data warehouses, empowering users to unlock actionable intelligence from complex datasets.
Read the full press release: CapStorm Launches CapStorm:AI
Cloudian Unveils Unified AI Inferencing and Data Storage Platform
Cloudian has launched a breakthrough platform that integrates high-performance object storage with AI inferencing capabilities, dramatically simplifying enterprise AI infrastructure. The new solution combines Cloudian HyperStore’s industry-leading storage—delivering up to 35GB/s per node—with integrated support for the Milvus vector database, enabling real-time, low-latency AI inferencing on petabyte-scale datasets.
Read the full press release: Cloudian Delivers Integrated AI Inferencing and Data Storage Solution
Cognizant Debuts Agent Foundry to Scale Agentic AI Across Enterprises
Cognizant has launched Agent Foundry, a new framework designed to help enterprises deploy and orchestrate autonomous AI agents at scale. The offering combines modular design, reusable assets, and multi-platform interoperability, enabling organizations to embed agentic capabilities into their workflows for adaptive operations and real-time decision-making. Agent Foundry supports the full lifecycle of agent deployment, from discovery to enterprise-wide scaling.
Read the full press release: Cognizant Introduces Agent Foundry
AI-Driven Cloud Demand Powers Record Q2 Growth in Global IT and Business Services
The latest ISG Index™ reveals that surging demand for cloud services—driven by enterprise AI initiatives—propelled the global IT and business services market to a record $29.2 billion in Q2, up 17% year-over-year. Cloud-based “as-a-service” (XaaS) offerings soared 28%, fueled by infrastructure investments from major hyperscalers, while managed services saw steady growth.
Read the full press release: AI-Driven Cloud Demand Fuels Q2 Growth in Global IT and Business Services Market: ISG Index
ManageEngine Report: Shadow AI as a Strategic Advantage
A new report from ManageEngine reveals that while 97 percent of IT leaders see significant risks in “shadow AI” (unauthorized AI tool use), 91 percent of employees believe the risks are minimal or outweighed by rewards. The report highlights the rapid adoption of unapproved AI tools—60 percent of employees use them more than a year ago—and identifies data leakage as a primary concern.
Read the full report summary: ManageEngine Shadow AI Report
National Academy for AI Instruction Launches with Microsoft, OpenAI, Anthropic, and AFT
The American Federation of Teachers (AFT), with support from Microsoft, OpenAI, and Anthropic, is launching the National Academy for AI Instruction in Manhattan. This $23 million initiative will train educators to harness AI technology in the classroom, with OpenAI contributing $10 million, Microsoft $12.5 million, and Anthropic $500,000 in the first year.
Read the full press release: AFT to launch National Academy for AI Instruction
SambaNova Launches First Turnkey AI Inference Solution for Data Centers
SambaNova has introduced SambaManaged, a turnkey AI inference solution for data centers that can be deployed in just 90 days—far faster than the industry norm. The modular system, powered by SambaNova’s SN40L AI chips, enables existing data centers to offer high-performance AI inference services with minimal infrastructure changes. This innovation addresses the growing demand for rapid, scalable AI infrastructure and is already being adopted by major public companies.
Read the full press release: SambaNova Launches Turnkey AI Inference Solution
WEKA Debuts NeuralMesh Axon for Exascale AI Deployments
WEKA has introduced NeuralMesh Axon, a breakthrough storage system designed for exascale AI workloads. Leveraging a fusion architecture, NeuralMesh Axon delivers up to 20x faster AI performance and 90 percent GPU utilization, addressing the challenges of large-scale AI training and inference. The system integrates seamlessly with GPU servers and AI factories, enabling organizations to accelerate AI model development, reduce costs, and maximize infrastructure efficiency.
Read the full press release: WEKA Debuts NeuralMesh Axon
Expert Insights
Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Review’s enterprise tech community where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.
Take the Tech Leader Survey – Spring 2025 Now
In partnership with Skiilify Co-Founder and distinguished Northeastern University Professor Paula Caligiuri, PhD, we’ve just launched our latest enterprise tech leader Survey to uncover how thought leaders are thinking about disruption in this AI moment.
The Digital Analyst with John Santaferraro Featuring IBM’s Bruno Aziza: Deep Blue, Deep Learning & the Future of AI
Bruno reveals why only 16 percent of organizations have achieved enterprise-scale AI adoption, shares battle-tested strategies from companies like PepsiCo and NatWest, and explains why the future belongs to leaders who can orchestrate agents at scale rather than just build them.
NEW Episode of Insight AI Featuring Doug Shannon: AGI on the Horizon
They break down what this means for knowledge workers, consultants, and anyone who thought their job was safe from automation. The conversation gets real about the five stages of AI grief most people are experiencing, why Apple is flailing while Meta throws $100 million at talent, and how to find your uniquely human value before the machines come for your paycheck.
Understanding & Preparing for the 7 Levels of AI Agents by Douglas Laney
The following framework for agentic AI stems from a computer science base with theoretical psychology and theoretical philosophy perspectives. Each of the seven levels represents a step-change in technology, capability, and autonomy. The framework shows how organizations gain more potential to innovate and thrive while transforming through data-powered and AI-based digital economic systems.
GLEWs Views:AI Transparency Moves Beyond Moratorium by Gregory Lewandowski
Following the Senate’s removal of a proposed AI development moratorium from major legislation in July 2025, Anthropic announced a targeted transparency framework for frontier AI companies. Their framework targets only the largest AI developers while establishing specific disclosure obligations around safety practices. This represents a significant shift in how the AI industry approaches self-regulation in the absence of comprehensive federal legislation.
6 Must-Have Human-Centric Skills for the AI Age by Tim King
Yet despite this shift, most organizations are not prepared. A proprietary study of over 200 senior tech professionals (get the research by my team and I here)—including AI practitioners, cybersecurity leaders, and IT executives—reveals a stark disconnect: while nearly all respondents believe human-centered skills are vital for the AI age, the vast majority admit their organizations lack the structure, time, or training mechanisms to develop them.
Take the Tech Leader Survey – Spring 2025 Now
In partnership with Skiilify Co-Founder and distinguished Northeastern University Professor Paula Caligiuri, PhD, we’ve just launched our latest enterprise tech leader Survey to uncover how thought leaders are thinking about disruption in this AI moment.
Mini Jam Highlights: Has AI Completely Replaced Process Automation?
Our AI industry experts debate whether AI agents have completely replaced traditional process automation (RPA) or if the future lies in a hybrid approach combining both technologies. This panel discussion reveals the hidden costs of AI implementation, the importance of solving real business problems over chasing use cases, and how the shift from SaaS to “Agent as a Service” is reshaping enterprise technology strategies.
Mini Jam Highlights: Best Cybersecurity Use Cases for AI Agents
Our cybersecurity experts reveal the most effective AI agent use cases transforming enterprise security operations, from compliance automation to vulnerability management and threat detection. They cover real-world implementations including CIS control optimization, SOC analyst assessment systems, and proactive vulnerability identification, while addressing the critical balance between AI autonomy and human oversight in security operations. Essential viewing for security leaders evaluating AI agent deployment strategies.
Mini Jam Highlights: Building and Deploying AI Agent Systems at Scale
Our AI and data experts dive deep into the architecture and infrastructure powering enterprise AI agent systems at scale, from low-latency decision making to vector databases and real-time streaming. This comprehensive technical discussion reveals the challenges of building reliable, traceable, and scalable agentic AI systems, including the critical role of human feedback loops and the current limitations preventing full AI agent autonomy. Essential viewing for technical leaders architecting AI agent deployments.
Mini Jam Highlights On-Demand: How AI Agents Will Transform Business Culture Forever
Our AI industry experts explore how agentic AI will fundamentally reshape business culture, workforce dynamics, and professional roles in the coming years. They discuss the shift from traditional employment to collaborative business partnerships, the rise of new AI-focused roles, and how companies must adapt their culture as AI agents automate routine tasks.
For consideration in future artificial intelligence news roundups, send your announcements to the editor: tking@solutionsreview.com.
New Grok 4 Takes on ‘Humanity’s Last Exam’ as the AI Race Heats Up
Elon Musk has launched xAI’s Grok 4—calling it the “world’s smartest AI” and claiming it can ace Ph.D.-level exams and outpace rivals such as Google’s Gemini and OpenAI’s o3 on tough benchmarks
Elon Musk released the newest artificial intelligence model from his company xAI on Wednesday night. In an hour-long public reveal session, he called the model, Grok 4, “the smartest AI in the world” and claimed it was capable of getting perfect SAT scores and near-perfect GRE results in every subject, from the humanities to the sciences.
During the online launch, Musk and members of his team described testing Grok 4 on a metric called Humanity’s Last Exam (HLE)—a 2,500-question benchmark designed to evaluate an AI’s academic knowledge and reasoning skill. Created by nearly 1,000 human experts across more than 100 disciplines and released in January 2025, the test spans topics from the classics to quantum chemistry and mixes text with images. Grok 4 reportedly scored 25.4 percent on its own. But given access to tools (such as external aids for code execution or Web searches), it hit 38.6 percent. That jumped to 44.4 percent with a version called Grok 4 Heavy, which uses multiple AI agents to solve problems. The two next best-performing AI models are Google’s Gemini-Pro (which achieved 26.9 percent with the tools) and OpenAI’s o3 model (which got 24.9 percent, also with the tools). The results from xAI’s internal testing have yet to appear on the leaderboard for HLE, however, and it remains unclear whether this is because xAI has yet to submit the results or because those results are pending review. Manifold, a social prediction market platform where users bet play money (called “Mana”) on future events in politics, technology and other subjects, predicted a 1 percent chance, as of Friday morning, that Grok 4 would debut on HLE’s leaderboard with a 45 percent score or greater on the exam within a month of its release. (Meanwhile xAI has claimed a score of only 44.4.)
During the launch, the xAI team also ran live demonstrations showing Grok 4 crunching baseball odds, determining which xAI employee has the “weirdest” profile picture on X and generating a simulated visualization of a black hole. Musk suggested that the system may discover entirely new technologies by later this year—and possibly “new physics” by the end of next year. Games and movies are on the horizon, too, with Musk predicting that Grok 4 will be able to make playable titles and watchable films by 2026. Grok 4 also has new audio capabilities, including a voice that sang during the launch, and Musk said new image generation and coding tools are soon to be released. The regular version of Grok 4 costs $30 a month; SuperGrok Heavy—the deluxe package with multiple agents and research tools—runs at $300.
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Artificial Analysis, an independent benchmarking platform that ranks AI models, now lists Grok 4 as highest on its Artificial Analysis Intelligence Index, slightly ahead of Gemini 2.5 Pro and OpenAI’s o4-mini-high. And Grok 4 appears as the top-performing publicly available model on the leaderboards for the Abstraction and Reasoning Corpus, or ARC-AGI-1, and its second edition, ARC-AGI-2—benchmarks that measure progress toward “humanlike” general intelligence. Greg Kamradt, president of ARC Prize Foundation, a nonprofit organization that maintains the two leaderboards, says that when the xAI team contacted the foundation with Grok 4’s results, the organization then independently tested Grok 4 on a dataset to which the xAI team did not have access and confirmed the results. “Before we report performance for any lab, it’s not verified unless we verify it,” Kamradt says. “We approved the [testing results] slide that [the xAI team] showed in the launch.”
According to xAI, Grok 4 also outstrips other AI systems on a number of additional benchmarks that suggest its strength in STEM subjects (read a full breakdown of the benchmarks here). Alex Olteanu, a senior data science editor at AI education platform DataCamp, has tested it. “Grok has been strong on math and programming in my tests, and I’ve been impressed by the quality of its chain-of-thought reasoning, which shows an ingenious and logically sound approach to problem-solving,” Olteanu says. “Its context window, however, isn’t very competitive, and it may struggle with large code bases like those you encounter in production. It also fell short when I asked it to analyze a 170-page PDF, likely due to its limited context window and weak multimodal abilities.” (Multimodal abilities refer to a model’s capacity to analyze more than one kind of data at the same time, such as a combination of text, images, audio and video.)
On a more nuanced front, issues with Grok 4 have surfaced since its release. Several posters on X—owned by Musk himself—as well as tech-industry news outlets have reported that when Grok 4 was asked questions about the Israeli-Palestinian conflict, abortion and U.S. immigration law, it often searched for Musk’s stance on these issues by referencing his X posts and articles written about him. And the release of Grok 4 comes after several controversies with Grok 3, the previous model, which issued outputs that included antisemitic comments, praise for Hitler and claims of “white genocide”—incidents that xAI publicly acknowledged, attributing them to unauthorized manipulations and stating that the company was implementing corrective measures.
At one point during the launch, Musk commented on how making an AI smarter than humans is frightening, though he said he believes the ultimate result will be good—probably. “I somewhat reconciled myself to the fact that, even if it wasn’t going to be good, I’d at least like to be alive to see it happen,” he said.
AI Research
Artificial Intelligence (AI) in Radiology Market to Reach USD 4236 Million by 2031 | 9% CAGR Growth Driven by Cloud & On-Premise Solutions
Artificial Intelligence in Radiology Market is Segmented by Type (Cloud Based, On-Premise), by Application (Hospital, Biomedical Company, Academic Institution).
BANGALORE, India , July 11, 2025 /PRNewswire/ — The Global Market for Artificial Intelligence in Radiology was valued at USD 2334 Million in the year 2024 and is projected to reach a revised size of USD 4236 Million by 2031, growing at a CAGR of 9.0% during the forecast period.
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Major Factors Driving the Growth of AI in Radiology Market:
The Artificial Intelligence in Radiology market is rapidly evolving into a cornerstone of modern diagnostic medicine. With its ability to improve accuracy, reduce turnaround time, and support clinical decision-making, AI is transforming radiological practices globally. The market is driven by both technology vendors and healthcare providers looking to optimize imaging workflows and outcomes. Continued innovation, clinical validation, and regulatory alignment are further solidifying AI’s role in the radiology ecosystem. As imaging demands increase and digital health ecosystems mature, AI in radiology is poised for robust growth across both developed and emerging healthcare markets.
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TRENDS INFLUENCING THE GROWTH OF THE ARTIFICIAL INTELLIGENCE (AI) IN RADIOLOGY MARKET:
Cloud-based platforms are significantly accelerating the growth of the Artificial Intelligence (AI) in Radiology market by offering scalable, real-time, and cost-effective infrastructure for medical imaging analysis. These platforms allow radiologists to upload, process, and analyze large volumes of imaging data across locations without investing in expensive on-premise systems. Cloud computing supports collaborative diagnosis and second opinions, making it easier for specialists worldwide to access and interpret radiological findings. AI algorithms hosted on the cloud continuously learn from diverse datasets, improving diagnostic accuracy. Additionally, the cloud simplifies data integration from electronic health records (EHRs), enhancing context-based imaging interpretation. This flexibility and accessibility make cloud-based models ideal for hospitals and diagnostic centers aiming for high-efficiency imaging operations, thereby driving market expansion.
On-premise deployment continues to play a critical role in the growth of the AI in Radiology market, especially for institutions emphasizing strict data security, regulatory compliance, and control. Hospitals with high patient volumes and in-house IT infrastructure often prefer on-premise AI solutions to ensure that sensitive imaging data stays within their private network. These systems offer faster processing speeds due to localized computing, reducing latency in real-time diagnostic decisions. Furthermore, institutions with proprietary imaging protocols benefit from customizable on-premise AI models trained on institution-specific data, enhancing diagnostic relevance. Despite the popularity of cloud solutions, the need for secure, localized, and tailored AI applications sustains strong demand for on-premise setups in high-end academic hospitals and specialized radiology centers.
Biomedical companies are key drivers of growth in the AI in Radiology market by developing next-generation imaging tools that integrate AI to enhance diagnostic performance. These companies are focusing on innovating AI-powered image reconstruction, detection, and segmentation tools that assist radiologists in identifying subtle anomalies with greater precision. Their collaboration with software developers, radiology experts, and hospitals fuels R&D in algorithm refinement and clinical validation. Many biomedical firms are also embedding AI directly into diagnostic hardware, creating intelligent imaging systems capable of real-time interpretation. This vertical integration of hardware and AI enhances efficiency and diagnostic confidence. Their commitment to improving patient outcomes and reducing diagnostic errors ensures consistent market advancement across clinical applications.
One of the major drivers is the rising need for early diagnosis and personalized treatment plans. AI in radiology enables rapid detection of minute anomalies in imaging data, which may be missed by the human eye, especially in early disease stages. This helps clinicians begin treatment sooner, improving patient outcomes. AI systems can also link imaging findings with genomic and clinical data to support tailored therapies. The push for predictive medicine and minimally invasive procedures reinforces the adoption of AI in radiology, particularly in oncology and neurology. As the healthcare industry leans towards precision care, AI becomes indispensable in modern diagnostic workflows.
Radiology departments globally are under immense pressure due to the increasing volume of imaging studies and a shortage of skilled radiologists. AI serves as a supportive solution by automating repetitive tasks like image labeling, prioritizing critical cases, and pre-analyzing scans to reduce turnaround time. This alleviates the burden on radiologists and helps maintain diagnostic quality despite workforce constraints. AI also improves workflow efficiency by integrating with radiology information systems (RIS) and picture archiving and communication systems (PACS). With healthcare systems strained by aging populations and rising chronic diseases, AI tools offer scalable solutions to meet diagnostic demand without compromising accuracy.
Recent progress in deep learning, a subfield of AI, has significantly enhanced the performance of radiology applications. These algorithms can analyze complex imaging patterns with remarkable accuracy and continue to learn from new datasets. With access to large annotated datasets and computing power, deep learning models can now rival or even outperform human radiologists in specific diagnostic tasks like tumor detection or hemorrhage recognition. The continuous refinement of these models is enabling faster, more consistent, and reproducible imaging interpretation. As algorithm transparency and explainability improve, regulatory acceptance and clinical adoption are also growing, driving broader market penetration.
The seamless integration of AI tools into hospital IT infrastructure is driving adoption. Radiology AI applications are now compatible with EHRs, PACS, and RIS, enabling smooth data flow and contextual analysis. This allows AI systems to consider patient history, lab results, and prior imaging during interpretation, thereby increasing diagnostic precision. Automation of report generation and structured data extraction from scans enhances communication between departments and reduces administrative workloads. As healthcare institutions prioritize interoperability and digital transformation, AI tools that fit within existing ecosystems are being widely embraced, contributing to sustained market growth.
The rising incidence of chronic diseases such as cancer, cardiovascular disorders, and neurological conditions is increasing the demand for medical imaging. These diseases require continuous monitoring through modalities like MRI, CT, and ultrasound, which generate large volumes of data. AI helps extract meaningful insights quickly from this data, facilitating timely interventions and longitudinal tracking. For example, AI can compare current and historical scans to detect subtle changes, supporting disease progression analysis. The growing prevalence of these conditions is pushing both private and public healthcare sectors to adopt AI tools that can handle high-frequency imaging needs efficiently.
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AI IN RADIOLOGY MARKET SHARE:
Regionally, North America leads the market due to its advanced healthcare systems, early adoption of AI technologies, and strong presence of leading AI radiology vendors. The U.S. benefits from robust funding, regulatory clarity, and high imaging volumes that support AI deployment.
The Asia-Pacific region is emerging as a key growth hub due to increasing healthcare investments in China, India, and Japan. Additionally, governments in the Middle East and Africa are exploring AI-based solutions to overcome radiologist shortages, gradually contributing to market diversification.
Key Companies:
- GE
- IBM
- GOOGLE INC
- Philips
- Amazon
- Siemens AG
- NVidia Corporation
- Intel
- Bayer(Blackford Analysis)
- Fujifilm
- Aidoc
- Arterys
- Lunit
- ContextVision
- deepcOS
- Volpara Health Technologies Ltd
- CureMetrix
- Densitas
- QView Medical
- ICAD
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DISCOVER MORE INSIGHTS: EXPLORE SIMILAR REPORTS!
– AI Radiology Software Market
– The Radiology AI Based Diagnostic Tools Market was valued at USD 2800 Million in the year 2024 and is projected to reach a revised size of USD 11200 Million by 2031, growing at a CAGR of 21.9% during the forecast period.
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– AI-Enabled X-Ray Imaging Solutions Market was valued at USD 423 Million in the year 2024 and is projected to reach a revised size of USD 600 Million by 2031, growing at a CAGR of 5.2% during the forecast period.
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– The global Radiology Software market is projected to grow from USD 150 Million in 2024 to USD 223.9 Million by 2030, at a Compound Annual Growth Rate (CAGR) of 6.9% during the forecast period.
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