Deepfake X-rays created by AI are now convincing enough to fool both doctors and AI models. In tests, radiologists had limited success identifying fake images, especially when they didn’t know they were being shown. This opens the door to risks like fraudulent medical claims and tampered diagnoses. Experts say stronger safeguards and detection tools are critical as the technology advances.
As AI systems began acing traditional tests, researchers realized those benchmarks were no longer tough enough. In response, nearly 1,000 experts created Humanity’s Last Exam, a massive 2,500-question challenge covering highly specialized topics across many fields. The exam was engineered so that any question solvable by current AI models was removed. Early results show even the most advanced systems still struggle — revealing a surprisingly large gap between AI performance and true expert-level knowledge.
Researchers tested whether generative AI could handle complex medical datasets as well as human experts. In some cases, the AI matched or outperformed teams that had spent months building prediction models. By generating usable analytical code from precise prompts, the systems dramatically reduced the time needed to process health data. The findings hint at a future where AI helps scientists move faster from data to discovery.
A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling.
Scientists have discovered that the human brain understands spoken language in a way that closely resembles how advanced AI language models work. By tracking brain activity as people listened to a long podcast, researchers found that meaning unfolds step by step—much like the layered processing inside systems such as GPT-style models.
New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers redesigned AI systems to better resemble biological brains, some models produced brain-like activity without any training at all. This challenges today’s data-hungry approach to AI development. The work suggests smarter design could dramatically speed up learning while slashing costs and energy use.
AI tools designed to diagnose cancer from tissue samples are quietly learning more than just disease patterns. New research shows these systems can infer patient demographics from pathology slides, leading to biased results for certain groups. The bias stems from how the models are trained and the data they see, not just from missing samples. Researchers also demonstrated a way to significantly reduce these disparities.
BISC is an ultra-thin neural implant that creates a high-bandwidth wireless link between the brain and computers. Its tiny single-chip design packs tens of thousands of electrodes and supports advanced AI models for decoding movement, perception, and intent. Initial clinical work shows it can be inserted through a small opening in the skull and remain stable while capturing detailed neural activity. The technology could reshape treatments for epilepsy, paralysis, and blindness.
Princeton researchers found that the brain excels at learning because it reuses modular “cognitive blocks” across many tasks. Monkeys switching between visual categorization challenges revealed that the prefrontal cortex assembles these blocks like Legos to create new behaviors. This flexibility explains why humans learn quickly while AI models often forget old skills. The insights may help build better AI and new clinical treatments for impaired cognitive adaptability.
A new AI tool called DOLPHIN exposes hidden genetic markers inside single cells, enabling earlier detection and more precise treatment choices. It also sets the stage for building virtual models of cells to simulate disease and drug responses.