AI for science & health

AI applied to biology, chemistry, physics, medicine, climate and other sciences

9 articles

All Agents Language models Reinforcement learning Vision Generative media Speech & audio Robotics & embodied AI Safety & security Evaluation & benchmarks Efficiency & systems Code & software AI for science & health Theory

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Ocean emulator runs on unstructured mesh, beating baselines for currents

The authors present HClimRep-Ocean, a machine-learning emulator that operates directly on the unstructured computational mesh of the FESOM2 ocean model. Trained on a 209-year control simulation, the model forecasts ocean currents more accurately than all reference methods at 30-day lead times, though temperature and salinity predictions are less skilled than a simple damped-anomaly persistence forecast. The authors also demonstrate competitive performance on the OceanBench benchmark against a reanalysis product.

3 Oct·3 min

AI for science & health

Joint deterministic-generative model extends reliable extreme-precipitation nowcasting to six hours

The authors present MW-Nowcast, a six-hour ensemble radar nowcasting model that jointly learns a deterministic predictor for organized precipitation structure and a generative flow-matching model for local residuals. The model doubles the available warning time for the most intense rainfall, delivering 6 h forecasts with skill previously confined to 3 h for the leading generative baseline.

3 Oct·3 min

AI for science & health

Real tumor board discussions expose gaps in medical language models

Li and colleagues built OpenTumorBoard from recordings of real multidisciplinary cancer discussions, preserving the sequence of questions, specialist responses and consensus decisions. Testing 14 general-purpose and medical language models showed that even the strongest systems remained substantially below full clinical equivalence, although supervised finetuning and reinforcement learning improved results on held-out cases.

30 Sept·2 min

AI for science & health

Compressed routing instances provide fast, strong starts for large solvers

Zhao et al. shift attention from designing a better routing solver to giving existing solvers a better starting point. Across four routing problem classes with 1,000 to 100,000 nodes, their initialization method reportedly matched or exceeded state-of-the-art solution quality while averaging roughly 70 times lower runtime.

30 Sept·2 min

AI for science & health

Incomplete inputs impose unavoidable limits on blind radiomap prediction

Li et al. develop a population-level theory for predicting radio coverage maps from incomplete descriptions of environments and base stations, without field measurements. They show that deterministic models optimally target the conditional mean radiomap, and report that their RadioDecomp framework improves two different propagation-prior baselines across random, unseen-configuration, and unseen-environment tests.

29 Sept·2 min

AI for science & health

Accident classifiers transfer across industries after task-specific adaptation

Yapi and colleagues tested whether classifiers trained on occupational accident narratives from one sector could work in others without retraining. Task-adapted models transferred better than frozen pretrained representations, with the three strongest approaches averaging 85.6% to 85.8% balanced accuracy across the target corpora.

22 Sept·2 min

AI for science & health

Dual-layer knowledge graph connects fragmented pharmaceutical process-development documents

Amirmoshiri, Sahneh and Jangjou built an agentic platform that transforms heterogeneous Chemistry, Manufacturing and Controls documents into a queryable, provenance-linked knowledge graph. On 505 questions drawn from 38 reports for one Sanofi small-molecule program, the lexical retrieval layer performed strongly on direct questions but weakened on comparative and corpus-wide queries.

12 Sept·2 min

AI for science & health

Paper-derived rubrics improve AI generation of scientific research plans

The authors turn 20,000 scientific papers into training environments for research-plan generation, deriving prompts from each paper’s goals and background while extracting evaluation criteria from its methods and experiments. Across three Qwen3 model sizes, their rubric-centered training schedule produced higher benchmark scores than supervised fine-tuning, either training stage alone, or the same stages in reverse order.

2 Sept·2 min

AI for science & health

Learned sepsis score tracks hourly severity without hourly outcome labels

The authors trained a 0–10 sepsis severity index from 43 routinely charted variables collected over 72 hours, using treatment-level mortality to rank trajectories rather than assigning mortality labels to every hour. In held-out testing, the score provided hourly prognostic information, correlated with changes in lactate and other physiological measures, and showed partial transfer between hospital systems.

30 Aug·2 min