Internal rotation of the shoulder—the inward rotation of the arm—is essential for many everyday tasks. Reaching behind the back to tuck in a shirt, fastening a bra strap, retrieving a wallet from a back pocket, all depend on a specific form of internal rotation in which the hand moves upward behind the back toward the spine. Clinicians commonly assess this capacity in patients with adhesive capsulitis, rotator cuff tears, and after shoulder arthroplasty using three clinical tests: internal rotation at the side (IRs), with the elbow at the side and the forearm swinging inward; internal rotation at 90°, abduction (IRa) with the arm abducted and rotated downward; and internal rotation to the posterior (IRp), in which the hand reaches upward behind the back. These tests are often used together in clinical practice, yet little research has examined whether they measure the same underlying motion or different ones. This distinction matters when clinicians encounter patients whose IRp is severely limited while IRs appears near normal, or when IRp deficits prove resistant to rehabilitation.

Despite advances in computational biology and regulatory moves to reduce animal use, a coherent framework for AI-driven polypharmacology as a bridge between animal models and human drug development is still missing. The shift from "cage" to "code" brings opportunities but also unresolved issues in biological interpretation, regulation, and education. This review critically examines this transition within the 3R framework, focusing on: (i) regulatory and ethical drivers for non-animal methods, (ii) gaps left by reduced animal use, and (iii) AI's potential to reconstruct biological complexity via multi-omics and predictive modelling. The review suggests that AI-driven polypharmacology offers a scalable, ethical, and precision-oriented path forward.

A practitioner article published in the SMS Explorer, “AI Will Help Formulate Strategy, But Should Never Finish It,” by Michael Olenick, challenges a growing assumption in the business world: that generative AI can take strategy off executives’ plates. As AI tools grow more capable, the debate has shifted from whether AI can contribute to strategy to whether it should lead it. Olenick, drawing on more than two decades of AI-aided strategy research, argues it should not—and explains precisely where the line must be drawn.

The article examines the role generative AI can—and cannot—play in the strategy process, drawing on Olenick’s development of VSTRAT, an AI-aided strategy system he first built in 2002 and later advanced as a research fellow at INSEAD. The piece references published findings that large language models can generate and evaluate strategies at levels comparable to entrepreneurs and investors, while insisting that the ability to produce strategic outputs is not the same as the authority to commit to them.