Article Open access Published: 05 June 2026 S. Kripa1 & V. Jeyalakshmi1  Scientific Reports (2026) Cite this article We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and

Medical AI often optimizes end-to-end performance while offering post-hoc explanations, widening an epistemic gap between behavior and justified clinical claims. Interpretable models and XAI help, but are insufficient. We advocate governance by testability: preregistered empirical trials of causal alignment (avoiding shortcuts) and invariance across scanners, sites, and subgroups, plus monitoring and rollback. We propose Institutional

A smartphone-based artificial intelligence application can accurately and efficiently screen for rare ocular surface malignancies, potentially streamlining the referral process and increasing case detection rates. A smartphone-based, artificial intelligence (AI) tool, can screen for rare ocular surface malignancies with similar accuracy to specialist-graded slit-lamp exams, according to research published in JAMA Ophthalmology. The study suggests

The company leverages a machine learning model it created to verify proof-of-delivery photos submitted by drivers, ensuring each package was delivered ...

MIT, in collaboration with Georgia State University and a growing network of educational institutions, has announced expanded work under PATH (Pathways for AI Training and Hiring) — a multiyear initiative designed to scale effective, affordable, industry-aligned AI training for entry-level and current workers, with a particular focus on transforming community colleges into engines powering an

PARTNER CONTENT Partner retains sole responsibility for the content of this article Combining cloud data working with AI support could give researchers an edge. Produced by Digital tools can help automate the repetitive tasks involved in carrying out experiments, leaving scientists more time for creative work. Credit: Westend61/ Getty Images Formulation science is a complex

The biotech industry is in the midst of an artificial intelligence (AI) investment surge, with companies pouring billions into the promise of faster, smarter drug discovery. As leaders in R&D, we have a responsibility to ask a hard question: are we investing in real improvements in effectiveness, or are we chasing a narrative? The honest

The rapid adoption of AI in writing, design, and analysis, to name just a few areas, came with mixed results: it made workflows faster and easier in some ways, and more complicated in others. The constant need to switch between tools and contexts comes at a cost, and is a frequent source of frustration for