OpenAI's decision to replace 4o with the more straightforward GPT-5 follows a steady drumbeat of news about the potentially harmful effects of extensive chatbot use. Reports of incidents in which ChatGPT sparked psychosis in users have been everywhere for the past few months, and in a blog post last week, OpenAI acknowledged 4o's failure to recognize when users were experiencing delusions. The company's internal evaluations indicate that GPT-5 blindly affirms users much less than 4o did. AI companionship is new, and there's still a great deal of uncertainty about how it affects people. Yet the experts we consulted warned that while emotionally intense relationships with large language models may or may not be harmful, ripping those models away with no warning almost certainly is.

AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. A growing area of interest within this field is Open-Endedness - the ability of AI systems to continuously and autonomously generate novel and diverse artifacts or solutions. This has become relevant for accelerating scientific discovery and enabling continual adaptation in AI agents. This position paper argues that the inherently dynamic and self-propagating nature of Open-Ended AI introduces significant, underexplored risks, including challenges in maintaining alignment, predictability, and control. This paper systematically examines these challenges, proposes mitigation strategies, and calls for action for different stakeholders to support the safe, responsible and successful development of Open-Ended AI.

Physicians often query a patient's electronic health record for information that helps them make treatment decisions, but the cumbersome nature of these records hampers the process. Research has shown that even when a doctor has been trained to use an electronic health record (EHR), finding an answer to just one question can take, on average, more than eight minutes. The more time physicians must spend navigating an oftentimes clunky EHR interface, the less time they have to interact with patients and provide treatment. Researchers have begun developing machine-learning models that can streamline the process by automatically finding information physicians need in an EHR. However, training effective models requires huge datasets of relevant medical questions, which are often hard to come by due to privacy restrictions.

Physicians typically question a affected person's digital well being report for info that helps them make therapy choices, however the cumbersome nature of those data hampers the method. Analysis has proven that even when a physician has been skilled to make use of an digital well being report (EHR), discovering a solution to only one query can take, on common, greater than eight minutes. The extra time physicians should spend navigating an oftentimes clunky EHR interface, the much less time they must work together with sufferers and supply therapy. Researchers have begun creating machine-learning fashions that may streamline the method by routinely discovering info physicians want in an EHR. Nonetheless, coaching efficient fashions requires large datasets of related medical questions, which are sometimes laborious to return by because of privateness restrictions.

An important goal in artificial intelligence and biology is to uncover general principles that underlie intelligence. While artificial intelligence algorithms need not relate to biology, they might provide a synthetic means to investigate biological intelligence in particular. Importantly, a more complete understanding of such biological intelligence would profoundly impact society.Thus, to explore biological hypotheses some AI researchers take direct inspiration from biology. However, nature's implementations of intelligence may present only one facet of its deeper principles, complicating the search for general hypotheses. This complication motivates the approach in this paper, called radical reimplementation, whereby biological insight can result from purposefully unnatural experiments. The main idea is that biological hypotheses about intelligence can be investigated by reimplementing their main principles intentionally to explicitly and maximally diverge from existing natural examples. If such a reimplementation successfully exhibits properties similar to those seen in biology it may better isolate the underlying hypothesis than an example implemented more directly in nature's spirit. Two examples of applying radical reimplementation are reviewed, yielding potential insights into biological intelligence despite including purposefully unnatural underlying mechanisms. In this way, radical reimplementation provides a principled methodology for intentionally artificial investigations to nonetheless achieve biological relevance.

When Covid came to Massachusetts, it forced Constance Lehman to change how Massachusetts General Hospital screens women for breast cancer. Many people were skipping regular checkups and scans due to worries about the virus. So the center Lehman codirects began using an artificial intelligence algorithm to predict who is at most risk of developing cancer. Since the outbreak began, Lehman says, around 20,000 women have skipped routine screening. Normally five of every 1,000 women screened shows signs of cancer.

A deep learning computer model developed by researchers at Massachusetts General Hospital (MGH) was able to identify subtle information in breast cancer images that could help better predict a woman's chances of developing the disease. Currently, the main methods of judging individual risk include checking for a family history of cancer, evaluating any biopsied tissue and noting whether they've given birth to a child. Screening mammograms--recommended annually by the American Cancer Society for women between the ages of 45 and 54--are typically used by oncologists to measure the density of the breast. "Why should we limit ourselves to only breast density when there is such rich digital data embedded in every woman's mammogram?" said Constance Lehman, M.D., Ph.D., MGH's division chief of breast imaging and senior author of a paper presented at the annual meeting of the Radiological Society of North America. "Every woman's mammogram is unique to her just like her thumbprint," Lehman said.

Using artificial intelligence (AI), European researchers have developed an algorithm that they say successfully detects molecular changes in tumor cells and tissues from microscopic slides in many different cancers. "What is quite remarkable is that our algorithm can automatically link the histological appearance of almost any tumor with a very broad set of molecular characteristics and with patient survival," said Moritz Gerstung, PhD, group leader at EMBL European Bioformatics Institute. Institute researchers collaborated on the study with scientists from the Wellcome Sanger Institute and Addenbrooke's Hospital in Cambridge, UK. The pan-cancer analysis is believed to be the largest to date to train computer vision to "see" and combine digital pathology with the genetic changes that occur in cells as malignancies take hold. Ordinarily, histopathologists examine the appearance of cancer tissue under a microscope first, then geneticists perform molecular sequencing separately to analyze changes in the genetic code.

Model developed at MIT's Computer Science and Artificial Intelligence Laboratory could reduce false positives and unnecessary surgeries. Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they're still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries. One common cause of false positives are so-called "high-risk" lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time.

Artificial intelligence (AI) can be used to identify outbreaks such as the coronavirus, which, to date, has resulted in nearly 1,800 reported deaths and more than reported 71,000 infections. In a February 13 webinar, Casey Ross, national technology correspondent for STAT, pointed to efforts by John Brownstein, PhD, chief innovation officer at Boston Children's Hospital, to use machine learning to review social media posts, reports by physicians, news outlets, and information released by official public health entities to assess the condition's outbreak beyond China's borders. Brownstein's work is proof that AI is showing its value in tracking the outbreak of the disease, says Ross. Closer to home, healthcare systems around the country use AI to inform operational tasks such as scheduling. Some healthcare organizations use AI to pinpoint patients who need additional care, says Ross. For example, it's used in sepsis detection and prediction, the assessment of readmission risk, and the identification of patients who are deteriorating.