Medicine & Health

Small language models for clinical decision support often answer at far greater length than the question requires after fine-tuning, and existing length control relies on preference data, reward models or proprietary teachers. PECA adds a differentiable conciseness term to the QLoRA supervised fine-tuning objective: once generation passes a token budget, it raises the end-of-sequence probability, with no preference data, no auxiliary model and a single commodity GPU. On the 1.7-billion-parameter Qwen3-1.7B-Instruct, PECA reduces mean response length from 256.0 to 69.7 tokens, a 72.8% cut, while raising MedMCQA multiple-choice diagnostic accuracy from 40.8% to 44.0%.

Small language models for clinical decision support often answer at far greater length than the question requires after fine-tuning, and existing length control relies on preference data, reward models or proprietary teachers. PECA adds a differentiable conciseness term to the QLoRA supervised fine-tuning objective: once generation passes a token budget, it raises the end-of-sequence probability, with no preference data, no auxiliary model and a single commodity GPU. On the 1.7-billion-parameter Qwen3-1.7B-Instruct, PECA reduces mean response length from 256.0 to 69.7 tokens, a 72.8% cut, while raising MedMCQA multiple-choice diagnostic accuracy from 40.8% to 44.0%.

Raysemble splits single-view bone reconstruction into shape and placement: it predicts each bone's shape on a normalized grid, then uses predicted depth and camera geometry to fix its center and scale in millimeters. On simulated frontal radiographs from 43 CT scans of 23 held-out patients, it averages 65.24% Dice, 9.09 mm HD95 and 3.32 mm ASSD before rigid alignment, leading 15 configurations; with identical inputs it beats VolumeNeRF by 5.86 Dice points.

Raysemble splits single-view bone reconstruction into shape and placement: it predicts each bone's shape on a normalized grid, then uses predicted depth and camera geometry to fix its center and scale in millimeters. On simulated frontal radiographs from 43 CT scans of 23 held-out patients, it averages 65.24% Dice, 9.09 mm HD95 and 3.32 mm ASSD before rigid alignment, leading 15 configurations; with identical inputs it beats VolumeNeRF by 5.86 Dice points.

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.

Clinical images arrive in batches by institution, modality and annotated structure, so a segmentation model must absorb a new imaging distribution and new lesion classes without forgetting what it already learned. FGCARL puts bidirectional feature-statistics alignment and Fisher-weighted parameter constraints into one incremental framework that needs no replay of historical patient data. Moving from UCSF-BMSR to BraTS-METS 2023, where domain and classes shift together, FGCARL with Swin UNETR reaches 68.28% mean Dice across the three tumor regions of the new domain, against 60.94% for the strongest comparator, CLAMTS.

Clinical images arrive in batches by institution, modality and annotated structure, so a segmentation model must absorb a new imaging distribution and new lesion classes without forgetting what it already learned. FGCARL puts bidirectional feature-statistics alignment and Fisher-weighted parameter constraints into one incremental framework that needs no replay of historical patient data. Moving from UCSF-BMSR to BraTS-METS 2023, where domain and classes shift together, FGCARL with Swin UNETR reaches 68.28% mean Dice across the three tumor regions of the new domain, against 60.94% for the strongest comparator, CLAMTS.

For home health consultation and triage in traditional Chinese medicine gynecology, MAGR-TCM turns 741 clinical case records into a knowledge graph of 10,231 entities and 32,051 relationships, then has four specialized agents reason step by step along the disease-pattern-principle-formula-medicinal chain. On 30 held-out test cases its RAGAS faithfulness is 0.821, against 0.766 for GraphRAG, 0.651 for LightRAG and 0.590 for Naive RAG; on a safety set of 10 emergency and 20 routine cases, the red-flag mechanism intercepted and passed every case, with sensitivity and specificity both 1.000. These results come from retrospective, expert-annotated records and have not yet been validated prospectively with real home users.

For home health consultation and triage in traditional Chinese medicine gynecology, MAGR-TCM turns 741 clinical case records into a knowledge graph of 10,231 entities and 32,051 relationships, then has four specialized agents reason step by step along the disease-pattern-principle-formula-medicinal chain. On 30 held-out test cases its RAGAS faithfulness is 0.821, against 0.766 for GraphRAG, 0.651 for LightRAG and 0.590 for Naive RAG; on a safety set of 10 emergency and 20 routine cases, the red-flag mechanism intercepted and passed every case, with sensitivity and specificity both 1.000. These results come from retrospective, expert-annotated records and have not yet been validated prospectively with real home users.

Paired modalities are usually used to pull two encoders into one shared embedding space. DuoJEPA instead keeps a separate LeJEPA self-supervised space per modality and appends to each encoder one residual-free transformer layer that translates its embedding into the other encoder's space, trained by a contrastive loss computed entirely within that target space. On CheXpert Plus chest radiographs and reports it reaches a zero-shot AUC of 86.6 and a cross-modal retrieval score of 78.5, against 60.0 and 64.2 for a reimplementation of LeVLJEPA with the same encoders and data, while a paired-cosine test detects no direct alignment between the two own spaces.

Paired modalities are usually used to pull two encoders into one shared embedding space. DuoJEPA instead keeps a separate LeJEPA self-supervised space per modality and appends to each encoder one residual-free transformer layer that translates its embedding into the other encoder's space, trained by a contrastive loss computed entirely within that target space. On CheXpert Plus chest radiographs and reports it reaches a zero-shot AUC of 86.6 and a cross-modal retrieval score of 78.5, against 60.0 and 64.2 for a reimplementation of LeVLJEPA with the same encoders and data, while a paired-cosine test detects no direct alignment between the two own spaces.

Patient-derived xenografts (PDX) implant human tumors into immunocompromised mice to test drugs, but screening every tumor-drug combination is infeasible in time and cost. GrAFT-PDX is a Bayesian model that jointly models tumor growth trajectories and mouse death risk, using a low-rank tensor decomposition to share information across PDX lines, treatments and time, so it can impute untested experiments and predict death times. On the large PDXe screen it assigns higher log probability to death times than Royston-Parmar and other survival baselines, matches strong baselines on tumor response, and preserves known gene-drug associations without genomic input.

Patient-derived xenografts (PDX) implant human tumors into immunocompromised mice to test drugs, but screening every tumor-drug combination is infeasible in time and cost. GrAFT-PDX is a Bayesian model that jointly models tumor growth trajectories and mouse death risk, using a low-rank tensor decomposition to share information across PDX lines, treatments and time, so it can impute untested experiments and predict death times. On the large PDXe screen it assigns higher log probability to death times than Royston-Parmar and other survival baselines, matches strong baselines on tumor response, and preserves known gene-drug associations without genomic input.

SIM replaces clinical raw k-space with training samples generated from multi-coil MRI physics, then scales data and model capacity together in a generic Transformer. Across 14 public and in-house datasets spanning seven centers, five vendors, 0.3 to 7 T and more than ten protocols, its zero-shot reconstruction beats the second-best method by 1.06 to 3.95 dB PSNR; in a 150-scan blinded reader study, SIM reached 100% diagnostic usability at an acceleration factor the source does not state.

SIM replaces clinical raw k-space with training samples generated from multi-coil MRI physics, then scales data and model capacity together in a generic Transformer. Across 14 public and in-house datasets spanning seven centers, five vendors, 0.3 to 7 T and more than ten protocols, its zero-shot reconstruction beats the second-best method by 1.06 to 3.95 dB PSNR; in a 150-scan blinded reader study, SIM reached 100% diagnostic usability at an acceleration factor the source does not state.

Curiosity-0826-4B extends a generative medical event foundation model trained on Epic Cosmos records from adults to pediatric patients and adds explicit representations of death and right-censoring. The model grows from 1B to 4.3B parameters and from an 8K to a 16K context window, generally matching or exceeding Curiosity-0226-1B on tasks both support, with mean absolute error on laboratory value prediction 18–46% lower. Pediatric AUROCs for incident asthma and asthma exacerbation are comparable to an adult reference cohort, while sickle cell crisis is lower in children.

Curiosity-0826-4B extends a generative medical event foundation model trained on Epic Cosmos records from adults to pediatric patients and adds explicit representations of death and right-censoring. The model grows from 1B to 4.3B parameters and from an 8K to a 16K context window, generally matching or exceeding Curiosity-0226-1B on tasks both support, with mean absolute error on laboratory value prediction 18–46% lower. Pediatric AUROCs for incident asthma and asthma exacerbation are comparable to an adult reference cohort, while sickle cell crisis is lower in children.

Automated surgical skill scoring has depended on a fixed rubric, so a new set of similarly worded assessment items meant retraining. Predictive Rubric Alignment (PRA) instead predicts a rater-weighted combination of the embeddings of each level's written description, then projects that prediction onto a path joining adjacent descriptions to read out a score. On AIxSuture suturing videos, the PRA-item variant with learned item queries reached 0.848 accuracy and 0.829 macro-F1, 0.113 macro-F1 above Video Swin-T, and PRA-cond trained on only two items still gave positive score correlations on unseen JIGSAWS items.

Automated surgical skill scoring has depended on a fixed rubric, so a new set of similarly worded assessment items meant retraining. Predictive Rubric Alignment (PRA) instead predicts a rater-weighted combination of the embeddings of each level's written description, then projects that prediction onto a path joining adjacent descriptions to read out a score. On AIxSuture suturing videos, the PRA-item variant with learned item queries reached 0.848 accuracy and 0.829 macro-F1, 0.113 macro-F1 above Video Swin-T, and PRA-cond trained on only two items still gave positive score correlations on unseen JIGSAWS items.