KR — 전자신문

ITIS and NTL Healthcare Team Up to Pursue AI-Based Women's Cancer Diagnostics Market

AINovember 3, 20261 min read

ITIS and NTL Healthcare signed a deal to build AI tools for women's cancer testing together. They also plan to sell these tools in other countries.

What happened

ITIS, a medical AI company, signed a memorandum of understanding (MOU, a basic agreement to work together) with NTL Healthcare. The two companies will develop an AI-based women's cancer test and expand it to overseas markets. ITIS combines its medical AI platform with NTL Healthcare's skill in cervical cancer testing.

Key facts

NTL Healthcare has worked on cervical cancer screening for about 30 years.
Its AI cervical cancer tool, CerviCARE AI, is sold in more than 20 countries.
NTL Healthcare plans to expand to more than 50 countries.
ITIS's AI platform is called GENAI ME.

What is the background?

A medical AI platform is software that uses artificial intelligence to organize and study health data. Cervical cancer is cancer of the lower part of the womb, and it can be found early through screening tests. AI can help doctors read these tests more quickly and consistently.

Why it matters

This deal shows how AI could be used to find women's cancers earlier. If the tools work well in real hospitals, they could help many patients around the world. The companies say they will test the solutions and look for real business results.

What to watch

Whether the two companies release a joint AI women's cancer test.
How the tests perform in real clinical trials.
Whether NTL Healthcare reaches its goal of more than 50 countries.
Which overseas partners and markets ITIS enters.

Glossary

GENAI ME: ITIS's medical AI platform for combining and analyzing health data.
CerviCARE AI: An AI tool that helps detect cervical cancer, sold in over 20 countries.
MOU: A memorandum of understanding, a basic agreement to cooperate before a full contract.

Anthropic's compute tab could top $1T as Q3 revenue nears $17B

Anthropic's revised IPO filing reveals nearly $900 billion in compute and infrastructure commitments, which could exceed $1 trillion with estimated additional obligations. The filing warns that its revenue growth is unlikely to continue at recent rates, and Q3 revenue of nearly $17 billion rose 46 percent from Q2, while compute costs grew 77 percent.

AIITmediaNov 3

Anthropic Amends IPO Filing: Q3 Revenue Near $17 Billion, Infrastructure Commitments About $895.4 Billion

Anthropic filed an amended registration statement (Form S-1/A) with the U.S. SEC on November 2 for its planned IPO, adding third-quarter 2026 results and future spending commitments for AI compute and infrastructure. Revenue for July–September was $16.956 billion, up about 47% from the previous quarter, but the company posted an operating loss of $567 million. The total infrastructure commitments of about $895.4 billion include roughly $213.8 billion that can be canceled under pre-start conditions, so the full amount is not unconditional debt.

AITechCrunchNov 3

Trajectory raises $45M to make AI model training accessible to nontechnical experts

Trajectory, a startup founded about a year ago by three Stanford classmates, raised $45 million in a Series A round at a $300 million valuation, led by Sequoia. The company's platform lets businesses use feedback to continuously retrain open source AI models for specialized tasks, an approach it calls continual learning, and it is now publicly available. Customers include Harvey, Rogo, Decagon, Mercor, and Nvidia.

AIThe RegisterNov 3

AI could break legal systems by finding previously hidden loopholes

Stanford Law School visiting fellow Felix Ringe and collaborator Linus Folkerts argue that large language models have become capable enough to find exploitable inconsistencies in written law at scale, much as they can find vulnerabilities in code. They cite several potential gaps in German law they uncovered with LLM help, while noting they are sharing only exploits they believe could not cause serious harm. The researchers warn that cheap automated legal reasoning could jeopardize the functioning of legal systems, and suggest lawmakers may need to harden legislation, possibly by using advanced AI themselves.