Executives increasingly turn to generative AI to help evaluate strategic decisions, from ranking business models to weighing competing options — but new research published in Strategic Management Journal cautions that no single AI-generated evaluation should be trusted at face value. The study finds that individual generative AI evaluations of strategic decisions are inconsistent and often biased, simply by the order in which two options are presented — but aggregating many evaluations across different prompts, roles, and models produces judgments that closely resemble those of human experts.

The research team of Anil R. Doshi of University College London, J. Jason Bell of the University of Oxford, and Emil Mirzayev and Bart Vanneste, both of University College London, tested large language models by having them rank 60 business models through pairwise comparisons, repeated across multiple models, ten assumed roles — including journalist, strategy professor, and industry expert — and two prompting strategies. No human expert panel evaluates the same option set that many ways, giving the researchers a rare way to measure where AI’s judgment holds up, and where it doesn’t.

Study compares three deep learning approaches for finding stroke lesions on contrast-free MRI, a step toward faster, more automated screening.

When someone may be having a stroke, every minute counts, and doctors often turn first to MRI scans that do not require an injected contrast dye; these are faster to obtain and more widely available in everyday clinical settings. The catch is that these contrast-free scans, known as FLAIR sequences, can be harder for computer programs to read automatically than more detailed scans, because the damaged brain tissue often shows up faintly, in irregular shapes, and looks somewhat different from one MRI machine to the next. A new study, published ahead of print in the Journal of Intelligent Systems in Current Computer Engineering, tested three different artificial intelligence approaches to see which handles this challenge best.

AI agents could reliably support diagnoses and clinical decision-making in the future – provided that sensitive health data are protected and clinicians can assess how reliable individual AI-generated results are. Researchers at the Else Kröner Fresenius Center (EKFZ) for Digital Health at TU Dresden and Dresden University Hospital have developed an on-premise medical AI system that addresses both challenges. Their findings have been published in the journal Nature Medicine.

USC Dornsife researchers studying the history of Utah’s Great Salt Lake identified two brief, deep-lake phases when the water was much fresher than now. Their analysis of core samples spanning the past 240,000 years shows that the most recent freshwater phase, which formed Lake Bonneville, lasted longer and was fresher than the earlier Little Valley lake phase. The researchers found that both lakes shrank and became salty as the regional climate warmed and dried, offering possible insights into Great Salt Lake’s future in the face of climate change.