Medical

Medical imaging segmentation (organs, tumors, anatomical structures)

Atten-Nonlocal Unet or MedSAM foundation model

Atten-Nonlocal Unet achieves 84-91% Dice scores across Synapse/ACDC/AVT with attention mechanisms for long-range dependencies. For broader generalization, MedSAM provides general-purpose segmentation across modalities with minimal task-specific fine-tuning.

Clinical question answering and decision support

GPT-4o or Med-PaLM 2

GPT-4o achieves 90.4% USMLE accuracy (92.7% diagnostic, 88.8% management) with multimodal capabilities. Med-PaLM 2 reaches 92.6% expert evaluation vs 92.9% for clinicians. Both outperform medical student baseline (59.3%) and handle complex clinical vignettes.

Medical image classification (cancer detection, disease identification)

DINO Vision Transformer with Grad-CAM

Self-supervised DINO outperforms CNNs across breast cancer, skin lesions, brain tumors, and retinal imaging. Grad-CAM provides spatially precise, class-discriminative explanations essential for clinical adoption. Works even with limited labeled data via transfer learning.

Clinical NLP and information extraction from unstructured notes

LLM-Augmented BiLSTM-BERT framework

Structured LLM augmentation improves strict NER F1 from 81.2% to 81.8% on i2b2-2012, relation extraction from 82.8% to 84.1% on N2C2-2018. Handles lengthy clinical documents exceeding standard transformer limits while preserving drug-dosage and condition-symptom relationships.

Drug discovery and protein design

BoltzGen for binder design, AlphaFold for structure prediction

BoltzGen enables generative design of novel protein binders for arbitrary targets (validated across 26 diverse cases). AlphaFold provides foundational structure prediction for 200M+ proteins with custom annotations. Represents step change from prediction to ab initio functional protein design.

Radiology workflow automation (imaging analysis, reporting)

FDA-cleared AI-Rad Companion or Claude 3.5 Sonnet for clinical tasks

AI-Rad Companion (FDA-cleared March 2025) handles organ segmentation for radiotherapy planning. For broader clinical workflows, Claude 3.5 Sonnet achieves 70% success on Stanford MedAgentBench (retrieving patient data, ordering tests, prescribing medications via FHIR APIs).

Pathology and whole-slide image analysis

Graph Neural Networks (DeepTFtyper architecture)

GNNs model spatial tissue relationships and topology critical for histopathology. DeepTFtyper predicts molecular subtypes (SCLC-A/N/P/Y) from H&E slides alone with AUC >0.70, enabling molecular-informed treatment selection without separate molecular testing.

Privacy-preserving multi-institutional model development

Federated Learning with Bayesian uncertainty quantification

Enables collaborative training while keeping patient data at local institutions. Bayesian approaches provide predictive uncertainty across federated settings, improving inference quality vs standard aggregation. Essential for GDPR/HIPAA compliance and rare disease research with distributed data.

Rare disease diagnosis with limited training data

RareScale LLM framework

Achieves 88.8% candidate generation performance and 17%+ improvement in Top-5 accuracy across 575 rare diseases vs baseline black-box LLMs. Specialized prompt engineering and evaluation strategies work even with inherently limited training examples.

Patient deterioration and survival prediction

DeepHit model for survival analysis

Achieves concordance index 0.94 and one-year AUC 0.89, substantially outperforming Cox proportional hazards. Effectively integrates temporal information and dynamic patient characteristics for longitudinal outcome prediction with missing data.