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A recent high-profile case of AI hallucination serves as a stark warning
A federal judge ordered two attorneys representing MyPillow CEO Mike Lindell in a Colorado defamation case to pay $3,000 each after they used artificial intelligence to prepare a court filing filled with a host of mistakes and citations of cases that didn’t exist.
Christopher Kachouroff and Jennifer DeMaster violated court rules when they filed the document in February filled with more than two dozen mistakes — including hallucinated cases, meaning fake cases made up by AI tools, Judge Nina Y. Wang of the U.S. District Court in Denver ruled Monday.
“Notwithstanding any suggestion to the contrary, this Court derives no joy from sanctioning attorneys who appear before it,” Wang wrote in her decision. “Indeed, federal courts rely upon the assistance of attorneys as officers of the court for the efficient and fair administration of justice.”
The use of AI by lawyers in court is not, itself illegal. But Wang found the lawyers violated a federal rule that requires lawyers to certify that claims they make in court are “well grounded” in the law. Turns out, fake cases don’t meet that bar.
Kachouroff and DeMaster didn’t respond to NPR’s request for comment.
The error-riddled court filing was part of a defamation case involving Lindell, the MyPillow creator, President Trump supporter and conspiracy theorist known for spreading lies about the 2020 election. Last month, Lindell lost this case being argued in front of Wang. He was ordered to pay Eric Coomer, a former employee of Denver-based Dominion Voting Systems, more than $2 million after claiming Coomer and Dominion used election equipment to flip votes to former President Joe Biden.
The financial sanctions, and reputational damage, for the two lawyers are a stark reminder for attorneys who, like many others, are increasingly using artificial intelligence in their work, according to Maura Grossman, a professor at the University of Waterloo’s David R. Cheriton School of Computer Science and an adjunct law professor at York University’s Osgoode Hall Law School.
Grossman said the $3,000 fines “in the scheme of things was reasonably light, given these were not unsophisticated lawyers who just really wouldn’t know better. The kind of errors that were made here … were egregious.”
There have been a host of high-profile cases where the use of generative AI has gone wrong for lawyers and others filing legal cases, Grossman said. It’s become a familiar trend in courtrooms across the country: Lawyers are sanctioned for submitting motions and other court filings filled with case citations that are not real and created by generative AI.
Damien Charlotin tracks court cases from across the world where generative AI produced hallucinated content and where a court or tribunal specifically levied warnings or other punishments. There are 206 cases identified as of Thursday — and that’s only since the spring, he told NPR. There were very few cases before April, he said, but for months since there have been cases “popping up every day.”
Charlotin’s database doesn’t cover every single case where there is a hallucination. But he said, “I suspect there are many, many, many more, but just a lot of courts and parties prefer not to address it because it’s very embarrassing for everyone involved.”
What went wrong in the MyPillow filing
The $3,000 fine for each attorney, Judge Wang wrote in her order this week, is “the least severe sanction adequate to deter and punish defense counsel in this instance.”
The judge wrote that the two attorneys didn’t provide any proper explanation of how these mistakes happened, “most egregiously, citation of cases that do not exist.”
Wang also said Kachouroff and DeMaster were not forthcoming when questioned about whether the motion was generated using artificial intelligence.
Kachouroff, in response, said in court documents that it was DeMaster who “mistakenly filed” a draft version of this filing rather than the right copy that was more carefully edited and didn’t include hallucinated cases.
But Wang wasn’t persuaded that the submission of the filing was an “inadvertent error.” In fact, she called out Kachouroff for not being honest when she questioned him.
“Not until this Court asked Mr. Kachouroff directly whether the Opposition was the product of generative artificial intelligence did Mr. Kachouroff admit that he did, in fact, use generative artificial intelligence,” Wang wrote.
Grossman advised other lawyers who find themselves in the same position as Kachouroff to not attempt to cover it up, and fess up to the judge as soon as possible.
“You are likely to get a harsher penalty if you don’t come clean,” she said.
Trust and verify
Charlotin has found three main issues when lawyers, or others, use AI to file court documents: The first are the fake cases created, or hallucinated, by AI chatbots.
The second is AI creates a fake quote from a real case.
The third is harder to spot, he said. That’s when the citation and case name are correct but the legal argument being cited is not actually supported by the case that is sourced, Charlotin said.
This case involving the MyPillow lawyers is just a microcosm of the growing dilemma of how courts and lawyers can strike the balance between welcoming life-changing technology and using it responsibly in court. The use of AI is growing faster than authorities can make guardrails around its use.
It’s even being used to present evidence in court, Grossman said, and to provide victim impact statements.
Earlier this year, a judge on a New York state appeals court was furious after a plaintiff, representing himself, tried to use a younger, more handsome AI-generated avatar to argue his case for him, CNN reported. That was swiftly shut down.
Despite the cautionary tales that make headlines, both Grossman and Charlotin view AI as an incredibly useful tool for lawyers and one they predict will be used in court more, not less.
Rules over how best to use AI differ from one jurisdiction to the next. Judges have created their own standards, requiring lawyers and those representing themselves in court to submit AI disclosures when it’s been used. In a few instances judges in North Carolina, Ohio, Illinois and Montana have established various prohibitions on the use of AI in their courtrooms, according to a database created by the law firm Ropes & Gray.
The American Bar Association, the national representative of the legal profession, issued its first ethical guidance on the use of AI last year. The organization warned that because these tools “are subject to mistakes, lawyers’ uncritical reliance on content created by a [generative artificial intelligence] tool can result in inaccurate legal advice to clients or misleading representations to courts and third parties.”
It continued, “Therefore, a lawyer’s reliance on, or submission of, a GAI tool’s output—without an appropriate degree of independent verification or review of its output—could violate the duty to provide competent representation …”
The Advisory Committee on Evidence Rules, the group responsible for studying and recommending changes to the national rules of evidence for federal courts, has been slow to act and is still working on amendments for the use of AI for evidence.
In the meantime, Grossman has this suggestion for anyone who uses AI: “Trust nothing, verify everything.”
The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US)
Browse 902 market data Tables and 67 Figures spread through 711 Pages and in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market by Offering (Integrated), Function (Diagnosis, Genomic, Precision Medicine, Radiation, Immunotherapy, Pharmacy, Supply Chain), Application (Clinical), End User (Hospitals), Region – Global Forecast to 2030
The global Artificial Intelligence (AI) in Healthcare Market [https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html?utm_source=abnewswire.com&utm_medium=paidpr&utm_campaign=artificialintelligenceinhealthcaremarket], valued at US$14.92 billion in 2024, is forecasted to grow at a robust CAGR of 38.6%, reaching US$21.66 billion in 2025 and an impressive US$110.61billion by 2030. The growing incidence of chronic diseases, linked with an increasing geriatric population, puts substantial financial pressure on healthcare providers. There is a rising need for the early detection of conditions such as dementia and cardiovascular disorders. This can be done by analysing imaging data to recognize patterns, which helps create personalized treatment plans.
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Browse in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market”
882 – Tables
61 – Figures
738 – Pages
By tools, the Artificial Intelligence (AI) in healthcare market for machine learning has been bifurcated into deep learning, supervised learning, reinforcement learning, unsupervised learning, and other machine learning technologies. The deep learning segment accounted for the largest share of the Artificial Intelligence (AI) in healthcare market in 2024. The capability to process vast amounts of unstructured medical data, such as electronic health records (HER), imaging, and genomics, allows accurate disease diagnosis and prediction. The integration of deep learning into healthcare is significantly boosting the AI in healthcare market, leading to substantial investments in diagnostic tools and predictive analytics. As computational power and data availability continue to increase, deep learning is set to unlock further advancements, solidifying its position as a key enabler of next-generation healthcare technologies.
By end user, the AI in healthcare market is segmented into healthcare providers, healthcare payers, patients, and other end users. In 2024, healthcare providers accounted for the largest share of the AI in healthcare market. The large share of this end-user segment can be attributed to the increasing budgets of hospitals to improve the quality of care provided and reduce the cost of care.
By geography, the Artificial Intelligence (AI) in healthcare market is segmented into five main regions: North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. The Asia Pacific region is projected to see a substantial growth rate during the forecast period. The Asia Pacific (APAC) region is experiencing substantial growth in adopting AI technologies within the healthcare sector, driven by a combination of demographic shifts, technological advancements, and increased investments in innovation. The rising elderly population in the region is a key factor, with the proportion of individuals aged 65 years and above increasing significantly. The demand for advanced healthcare solutions has surged as the aging population faces chronic and age-related conditions, necessitating efficient diagnostic, monitoring, and treatment tools. AI technologies are being integrated into various healthcare applications, including predictive analytics, telemedicine, medical imaging, and patient management systems. These innovations aim to address gaps in healthcare access, improve diagnostic accuracy, and streamline operations across the region.
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The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US), GE Healthcare (US), Medtronic (US), Oracle (US), Veradigm LLC (US), Merative (IBM) (US), Google (US), Cognizant (US), Johnson & Johnson (US), Amazon Web Services, Inc. (US), among others. These companies adopted strategies such as product launches, product updates, expansions, partnerships, collaborations, mergers, and acquisitions to strengthen their market presence in the Artificial Intelligence (AI) in healthcare market.
Koninklijke Philips N.V. (Netherlands)
Koninklijke Philips N.V. is a leading player in the AI in the healthcare market. The company utilizes AI to deliver innovative tools across various areas, including diagnostic imaging, patient monitoring, and precision medicine. Its advanced AI-driven platforms, such as the Philips HealthSuite, facilitate the integration and analysis of extensive clinical data, which supports personalized treatment plans and improves patient outcomes. Philips focuses on organic and inorganic growth strategies to expand its market presence.
Strategic partnerships in high-potential markets and collaborations have been the key growth strategies of the company over the years. For example, in February 2025, Philips partnered with Medtronic to educate and train cardiologists and radiologists in India on advanced imaging techniques for structural heart diseases. This partnership aims to upskill 300+ clinicians in multi-modality imaging such as echocardiography (echo) and Magnetic Resonance Imaging (MRI), especially for End-Stage Renal Disease (ESRD) patients. In November 2023, Philips and NYU Langone Health partnered to focus on patient safety and outcomes. This partnership integrated innovative health technologies, including digital pathology, clinical informatics, and AI-enabled diagnostics, enabling real-time collaboration among clinicians. The company also focuses on winning contracts across several companies in the healthcare space. This helps the company expand its footprint. For instance, in September 2022, Philips and Mandaya Royal Hospital Puri (MRHP) in Jakarta underwent a digital transformation in a strategic partnership, enhancing patient-centered care and healthcare services.
Microsoft Corporation (US):
Microsoft Corporation is one of the leading providers of software & tools that include advanced AI capabilities in healthcare to improve patient outcomes, streamline operations, and drive innovation. Its Azure-based AI solutions support distinct applications such as medical imaging, genomics, and precision medicine. The company also provides healthcare-specific AI models through its Azure AI Model Catalog, which is constructed to support hospitals and research institutions in building and deploying tailored AI solutions proficiently. Moreover, the integration of Nuance’s AI-powered clinical and diagnostic tools encourages its capacity to support healthcare providers in decision-making and care delivery. The company continuously brings AI capabilities to the platforms in large-scale customer models. For instance, in March 2025, the company launched Microsoft Dragon Copilot, the first unified voice AI assistant in the healthcare industry that enables clinicians to streamline clinical documentation, surface information, and automate tasks.
Microsoft Corporation has invested significantly in R&D, which has improved its product portfolio and position in the AI market. Machine Learning (ML), deep learning, Natural Language Processing (NLP), and speech processing are the key focus areas of the company in the AI in healthcare market. The company continuously invests in a series of services and computational biology projects, including research support tools for next-generation precision healthcare, genomics, immunomics, CRISPR, and cellular and molecular biologics. It has a strong global presence, with key operations supported through its Azure cloud infrastructure across regions like North America, Europe, Asia-Pacific, and the Middle East.
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LLM-Optimized Research Paper Formats: AI-Driven Research App Opportunities Explored | AI News Detail
From a business perspective, the idea of designing research for LLMs presents immense market opportunities. Companies that develop platforms or apps to create, curate, and deliver LLM-friendly research content could tap into a multi-billion-dollar market. According to a 2025 report by McKinsey, the generative AI market is projected to grow to $1.3 trillion by 2032, with content generation and data processing as key drivers. A ‘research app’ for LLMs, as Karpathy suggests, could serve industries like pharmaceuticals, where AI models analyze vast datasets for drug discovery, or finance, where real-time market insights are critical. Monetization strategies could include subscription models for premium datasets, API access for developers, or enterprise solutions for tailored LLM training data. However, challenges remain, such as ensuring data privacy and preventing bias in LLM outputs—issues that have plagued AI systems, as noted in a 2025 study by the MIT Sloan School of Management, which found that 60% of AI deployments faced ethical concerns. Businesses must also navigate a competitive landscape with players like Google, OpenAI, and Anthropic already dominating LLM development, requiring niche specialization to stand out.
On the technical side, designing research for LLMs involves moving beyond PDFs to formats like JSON, XML, or custom data schemas that encode information hierarchically for machine parsing. Unlike human readers, LLMs thrive on structured datasets with metadata, embeddings, and cross-references that enable rapid context retrieval and reasoning. Implementation challenges include standardizing formats across industries and ensuring compatibility with diverse LLM architectures—a hurdle given that, as of mid-2025, over 200 distinct LLM frameworks exist, per a report from the AI Index by Stanford University. Solutions could involve open-source protocols or industry consortia to define standards, much like the web evolved with HTML. Looking to the future, LLM-optimized research could lead to autonomous AI agents conducting real-time literature reviews or hypothesis generation by 2030, as predicted by a 2025 forecast from Deloitte. Regulatory considerations are also critical, with the EU AI Act of 2025 mandating transparency in AI data usage, which could impact how research content is structured. Ethically, ensuring that LLMs do not misinterpret or propagate flawed data remains a priority, requiring robust validation mechanisms. The potential for such innovation is vast, offering a glimpse into a future where knowledge creation is as much for machines as for humans, reshaping industries and workflows profoundly.
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