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Category: robotics/AI

Clinical Utility of Deep Learning–based Multiple Arterial Phase MRI in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) diagnosis relies heavily on well‑timed arterial phase MRI, yet single arterial phase scans often miss the optimal late arterial phase, especially with hepatobiliary contrast agents that are prone to motion artifacts and narrow timing windows. These limitations can compromise image quality and reduce detection of key features such as arterial phase hyperenhancement.

In a study recently published in Radiology: Imaging Cancer, researchers led by Kai Liu, BS, Zhongshan Hospital at Fudan University in Shanghai, compared conventional single phase imaging with an ultrafast, deep learning-based multiphase MRI technique, which can rapidly acquire six high-resolution arterial phases in a single breath hold.

In a cohort of 236 participants, the deep learning–based multiphase MRI technique markedly improved late arterial capture, boosted overall image quality and enhanced detection of lesions and HCC for both extracellular and hepatobiliary agents. The method achieved a late arterial capture rate of 98% (vs. 81% to 85% with single phase imaging) and showed strong performance in identifying small tumors.

“These findings support the potential of deep learning-based multiphase arterial MRI to streamline HCC diagnosis,” the authors conclude.

Read the full article, “Clinical Utility of Deep Learning–based Multiple Arterial Phase MRI in Hepatocellular Carcinoma.”

The AI Paradox: Cure or Poison?

Technology promised simplicity. It delivered complexity.

AI promised resolution. It is delivering acceleration.

The paradox is not a bug. It is the feature. And the question is what we choose to do about it.

This week I published a new essay, It is the argument I have been circling for a decade, finally in one place.

The short version: as AI’s capabilities grow, so do the risks. They are not separate variables. They climb the same curve. A more powerful model can cure more diseases and design more weapons. A smarter agent can book your travel and drain your bank account. Capability is leverage. Leverage is indifferent to ethics.

Every time we raise the ceiling of what AI can do, we raise the floor of what can go wrong.

We still have the how. We are drowning in the what. What we have neglected, almost completely, is the why.

Continue reading “The AI Paradox: Cure or Poison?” | >

Natural-language AI helps chemists design molecules step by step

Designing molecules is one of chemistry’s most complex challenges. From life-saving drugs to advanced materials, each compound requires a precise sequence of reactions. Planning these steps demands both technical knowledge and strategic insight, making it a task that often relies on years of experience.

Two problems plague much of modern chemistry. The first is retrosynthesis: Chemists start from a target molecule and work backward to identify simpler building blocks and viable reaction pathways. Retrosynthesis involves countless decisions, from choosing starting materials to determining when to form rings or protect sensitive functional groups. While computers can explore vast “chemical spaces,” they often struggle to capture the strategic reasoning used by human experts.

The second problem is reaction mechanisms. These describe how chemical reactions unfold step by step through the movements of electrons. Mechanistic insight helps scientists predict new reactions, improve efficiency, and reduce costly trial and error. Existing computational methods can generate many possible pathways, but often lack the chemical intuition needed to identify the most plausible ones.

Bridging structure and function: artificial intelligence-based modelling of kidney proteins

Advances in artificial intelligence-driven algorithms and experimental technologies have revolutionized the field of protein modelling. This Review describes how these developments have provided unprecedented insights into the structure of key proteins within the kidney, improved understanding of the relationships between protein structure and stability, and enabled mechanistic interpretation of variants that underlie a variety of kidney pathologies.

Nvidia becomes first company to cross $5 trillion in market value

Nvidia has achieved a historic milestone. The chipmaker is now the world’s most valuable listed company. Its market capitalization has surpassed five trillion dollars. This surge places Nvidia ahead of tech giants like Alphabet and Apple. The company’s success is driven by its crucial role in supplying GPUs for artificial intelligence models. Nvidia’s stock performance reflects its strong market position.

Machine learning identifies catalyst ‘sweet spot’ for greener urea from waste gases

Urea is an extremely important chemical, especially for fertilizers. But, making urea is energy intensive and relies heavily on fossil fuels. However, new findings from Griffith University and the Queensland University of Technology have highlighted new ways to produce urea electrochemically, using electricity and waste gases such as carbon monoxide (CO) and nitrogen oxides (NO) instead.

The paper, “Machine Learning-Assisted Design Framework of Carbon Edge-Dominated Dual-Atom Catalysts for Urea Electrosynthesis,” has been published in ASC Nano.

“The challenge is that when CO and NO react on a catalyst, they usually don’t form urea,” said co-lead author Professor Qin Li from Griffith University.

Label-free optical imaging enables automated measurement of human white matter microstructure

White matter pathways allow distant parts of the brain to communicate, supporting memory, emotion, and language. One such pathway, the uncinate fasciculus, connects the front of the temporal lobe with regions of the frontal cortex involved in decision-making and social behavior. Despite its importance, little is known about the microscopic structure of this tract in the human brain.

Traditional techniques such as electron microscopy can reveal fine details, but they often fail when applied to postmortem human tissue, which is frequently degraded.

In a study published in Biophotonics Discovery, researchers report a new way to examine white matter structure in postmortem human brains.

AI Models Mirror Human Logic on Real-World Scenarios

“What we show is that the models actually capture that human uncertainty pretty well,” said Michael Lepori. [ https://www.labroots.com/trending/technology/30475/ai-models…cenarios-2](https://www.labroots.com/trending/technology/30475/ai-models…cenarios-2)

Can AI models distinguish fact from fiction? This is what a new study scheduled to be presented at the International Conference on Learning Representations this weekend hopes to address as a team of scientists investigated how AI models could tell the difference between facts and “fake news”. This study has the potential to help scientists, engineers, and the public better understand how AI models can evolve to meet human needs, which comes at a time when AI is becoming more integrated into our everyday lives.

For the study, the researchers analyzed how AI language models (LMs) were able to differentiate between different topics and information and judge what’s true and what’s fake. The motivation behind this study was to address a knowledge gap regarding whether large language models (LLMs) have a human-like understanding of the world or if they simply make decisions based on what’s given to them.

The goal of the study was to ascertain if the LMs could determine whether an event is real or fake, along with ascertaining when the LM makes this determination during its thought process. For example, the researchers would give the LM simple scenarios like “clean a car”, clean a road”, and “clean a cloud”, and ask the LM to figure out which was real or fake. In the end, the researchers found that large LMs were capable of differentiating between real and fake events or data.

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