AI and the World's Hard Problems ─ Medicine & drug discovery
Science and scholarship advanced by AI, by discipline
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An interpretable hierarchical deep learning framework for lung and colon cancer detection
Classifying lung and colon cancer from histopathology images is difficult because the classes look similar and tissue morphology varies, and conventional deep learning models offer little transparency. The authors split the five-class task into coarse-to-fine stages: first normal versus cancer, then a specialized classifier for normal-tissue categories or cancer subtypes, with the best architecture chosen for each stage. The framework reached 98.50% accuracy on LC25000 and 82.01% on the independent LungHist700 dataset, and Grad-CAM, Grad-CAM++, LIME and Occlusion Sensitivity were used to highlight the image regions behind its predictions.
Artificial intelligence-based prediction of hepatocellular carcinoma in individuals without HBV or HCV infection in the MASLD era
Current liver cancer (HCC) surveillance guidelines mainly target people with cirrhosis or viral hepatitis. The researchers built an AI model to predict HCC risk in people without viral hepatitis, using data from about 7.96 million South Korean adults who underwent health screening. Among twelve models compared, MHA_DNN performed best and kept consistent accuracy in an independent validation cohort. Fatty liver index and triglycerides were the most influential predictors, suggesting support for metabolism-based personalized surveillance.
A multi task deep learning framework for automated health condition prediction from retinal fundus images using hybrid attention and explainable AI
Existing deep learning models for retinal fundus images handle only one task, offer limited interpretability, and struggle with variation across datasets. The authors propose a multi-task network that classifies diabetic retinopathy severity, glaucoma, AMD stage, and cardiovascular risk from a single image in one forward pass, with Grad-CAM explanations. It reached 94.7% accuracy and AUC 0.978 across four datasets, outperforming all single-task models statistically, and AUC of at least 0.951 in zero-shot cross-dataset evaluation.
An automata-machine learning integrated framework for intelligent mental health diagnostics
Depression among students often goes undetected until it becomes serious, so early prediction is needed. The authors combined finite automata-based behavioural modelling with machine learning, generating features from students' emotional states and merging redundant features. Using an ensemble of random forest and KStar, accuracy rose from an 82.02% baseline with random forest to 96.88% with the automata-based features. The authors state that the approach keeps the features interpretable and traceable.
A multi-stage validation framework for trustworthy large-scale clinical information extraction using large language models
Validating large language models for clinical information extraction usually requires costly manual annotation. The authors proposed a multi-stage framework combining rule-based filtering, semantic grounding, judge-LLM evaluation, expert review, and predictive validity analysis. Applied to substance use disorder diagnoses across 919,783 clinical notes, it removed 14.59% of LLM-positive extractions as unsupported or implausible. The primary LLM achieved an F1 score of 0.80 against judge evaluations, and its extracted diagnoses predicted later specialty care engagement better than structured-data baselines.
AI detects coronary heart disease from face and tongue photos in small test
A new study from China trained an AI system on photographs of the face and tongue to detect coronary heart disease (CHD). In a small test, the system correctly identified every patient with CHD.
Identification of adventitious lung sounds in children with respiratory illnesses in Bangladesh using artificial intelligence and digital auscultation
Integrated management of childhood illness guidelines have high sensitivity but low specificity for pneumonia. Health workers in Bangladesh recorded lung sounds from children under five with suspected pneumonia using a digital stethoscope, and an AI algorithm was compared with a pediatrician panel. The AI detected abnormal sounds with 61.8% sensitivity and 60.7% specificity overall, and 63.5% and 66.8% in pneumonia cases under IMCI. The authors describe this as moderate performance, with improvement needed in classifying uninterpretable recordings.
Autonomous labeling of surgical resection margins using a foundation model
Assessing surgical resection margins in pathology is affected by manual inking errors, tissue distortion, and cautery damage. The authors developed a virtual inking network that uses a frozen foundation model and a small classifier to map the margin on whole-slide images without physical ink. On 20 slides from unseen tissue blocks, it reached about 73.3% region-level accuracy and produced overlays that qualitatively matched expert annotations. The results are proof-of-concept and need validation in larger, clinical cohorts.
Artificial intelligence-enabled clinical phenotyping of human brown fat
Methods for quantifying brown adipose tissue (BAT) activity in humans at scale have been lacking. Using data from 101,243 patients at a large cancer centre, the authors semi-automatically quantified BAT activity in about 8,700 patients with machine learning. Higher BAT activity, especially in the axillary depot, was associated with lower odds of cardiometabolic disease and healthier white fat distribution.
An all-in-one foundation model for multimodal medical image restoration and enhancement
AMIFound is a foundation model that restores and enhances medical images degraded by noise, motion artefacts, low contrast or low-dose acquisition across seven modalities, including CT, MRI, PET, endoscopy, ultrasound, fundus and X-ray. Trained on 3.76 million paired images, it outperformed 38 task-specific and general baselines on image quality metrics, clinician assessment and robustness tests. Enhanced images also improved 44 disease detection tasks, tumour stage prediction and survival risk stratification in non-small-cell lung cancer.
Hidden signals in sleep can predict future health
AI tools can extract physiological patterns from a single night of sleep that conventional sleep measures miss, and these patterns are linked to disease and mortality. Two recent papers, in Nature Communications and Nature Medicine, suggest sleep data hold far more health information than clinicians and researchers currently extract. Growing evidence also indicates that sleep changes can appear before a disease is diagnosed.
Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia
Primary ciliary dyskinesia (PCD) is a genetic condition with dysmotile cilia, and diagnosis remains challenging. The authors developed CiliaTracks, a machine learning framework that analyses particle transport on cultured airway cells captured by high-speed microscopy. Classical machine learning models and a convolutional neural network classified recordings from 14 PCD individuals and 10 healthy donors with 95-97% accuracy.
Brain tumor segmentation using particle swarm optimized histogram equalization and a VGG19 based U-Net
Brain tumor segmentation in MRI is slow, error-prone, and affected by operator bias, and deep learning models often lack interpretability. The authors propose SPOHE-VGGNet, which combines a modified U-Net with VGG-19 and particle swarm optimized histogram equalization to enhance MRI contrast. It reached Dice scores of 0.9522 (meningioma), 0.9428 (glioma) and 0.9518 (pituitary tumor) on the Figshare dataset and 0.9463 for whole-tumor segmentation on BraTS 2021, though the authors state that external, volumetric and clinician-centered validation is still required before clinical use.
Unlocking Earth AI’s planetary geospatial foundation models for global public health
Public health decisions are often slowed by reporting lags, siloed data, and sparse data. Google's Population Dynamics Foundation Model (PDFM) compresses privacy-preserving search trends, human mobility, built-environment density, and environmental factors into location embeddings. These embeddings can be plugged into existing epidemiological models without task-specific fine-tuning, and five partner case studies were presented.
A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography
Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) look alike on echocardiography, which makes them hard to tell apart. The study developed a deep-learning framework that classifies 2D echo images into five clinically relevant views, extracts features from each view and fuses them. In fivefold cross-validation on 212 HCM, 119 CA and 200 control patients, it reached a precision of 0.83, sensitivity of 0.81, specificity of 0.89 and micro-F1 of 0.82, and the authors describe it as a reliable diagnostic tool.
New formulation helps RNA vaccines withstand high temperatures
RNA vaccines usually need ultracold storage, so MIT researchers used an AI algorithm to tweak the formulation around the lipid nanoparticles that deliver mRNA vaccines. The algorithm predicted optimal formulations, cutting the number of experiments needed and speeding development. The resulting vaccines stayed stable for up to a year at room temperature or two months at nearly 100°F, and in mice a Covid-19 vaccine produced an immune response as strong as that of an RNA vaccine similar to Moderna's.
Estimating suicide risk from text
Identifying people at high risk of suicide is a priority for counselors during mental health crises. Researchers at MIT's McGovern Institute developed a language-processing tool that searches text for words and phrases linked to 49 suicide risk factors to estimate an individual's risk. In text conversations with crisis counselors, the tool accurately predicted suicide risk, and with more validation it could support risk assessment in clinical and crisis-support settings.