Research at The Ohio State University Wexner Medical Center finds stress during pregnancy may change how the placenta sends messages to the fetus. These changes may help explain how pregnancy stress can affect early brain development. Researchers hope to eventually identify a therapy to stop the transmission of stress.

Cardiovascular medicine is inherently multimodal, but the data available at the clinical decision point are rarely as complete or as synchronized as benchmark evaluations assume. Recent reviews identify incomplete datasets, heterogeneous data characteristics, and missing modalities as persistent barriers to multimodal clinical AI. A correspondence published in Medicine Discovery argues that the central problem is not how to fuse more data, but how to reason when clinically relevant data are incomplete, temporally misaligned, or no longer representative of the patient's current state. The authors distinguish five linked but non-equivalent problems — conventional multimodal fusion, robustness to missing modalities, temporally aware modeling, uncertainty-aware selective prediction, and clinically informed interpretation of why a modality is unavailable — and treat modality availability as an observation state rather than a data defect. They define deferral operationally, separate it from calibration, uncertainty estimation, and distribution-shift detection, propose hybrid deferral rules that combine non-compensatory clinical hard stops with graded model-derived signals, and set out five candidate conditions for an intended-use-specific minimum evaluation suite. The framework is offered as a complement to existing clinical AI guidance (TRIPOD+AI, PROBAST+AI, FUTURE-AI, DECIDE-AI, CONSORT-AI, SPIRIT-AI) rather than a replacement, and as a conceptual proposal requiring prospective evaluation and multi-stakeholder consensus.