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Many of the streaming media players on the market are built on Android—this includes Fire TV and, of course, Google TV. So, why can’t an Android phone be used as a streaming stick? They can, and that’s exactly what I did with an old Samsung Galaxy phone.

Research Note: CARE-X is a research model and not a Microsoft product offering or medical device. It has not been cleared or approved by any regulatory authority and is not intended for clinical diagnosis, screening, or patient care. The results described below are retrospective research findings and do not establish the safety, effectiveness, or suitability of CARE-X for any clinical use. References to potential workflows describe areas for future research, not currently available capabilities or recommended uses. 

At a glance

• The challenge: Chest X-ray interpretation spans diverse tasks that require both expressive report generation and calibrated diagnostic predictions.
• CARE-X is a unified chest X-ray VLM for diverse clinical interpretation tasks. It combines generation and structured prediction to provide both free-text reasoning and deterministic outputs.
• CARE-X uses reinforcement learning (DAPO) to reward clinical correctness in a multi-task setting.
• In a separate research experiment from CARE-X, we paired Qwen3-VL-4B-Instruct with deterministic measurement tools to evaluate whether direct computation could improve performance on measurement-dependent conditions compared with visual approximation alone. 
• Validated on real-world Indian clinical data from Narayana Health, including rare ICU pathologies and CT-confirmed enlargement conditions.

What radiologists need: Task diversity, flexibility, and clinical fidelity

A clinically useful radiology AI system must support a wide range of tasks, adapt to different workflows, and produce outputs that are medically accurate.

Radiologists and other clinicians use chest X-rays for many different purposes. A clinically useful AI system must be able to support that range of tasks. It may be asked to generate detailed findings and concise impressions for a report, answer questions about the presence, absence, or location of a finding, identify medical devices and assess their placement, or pinpoint exactly where an abnormality appears in an image.

These tasks also require different kinds of outputs, from narrative reports to calibrated diagnostic scores. And above all, they require clinical accuracy. A report could ostensibly be perfectly written yet clinically wrong if it misses a finding, reverses a negation, or misidentifies a location. Certain findings could be trivial in one context and vital to identify in another.

CARE-X was developed as a research model to explore how a unified approach can address these diverse demands. The system combines generative and discriminative capabilities, clinically aligned optimization, and tool-based reasoning to support a broader range of radiology workflows while maintaining clinical fidelity.

Azure AI Foundry Labs

Get a glimpse of potential future directions for AI, with these experimental technologies from Microsoft Research.

Azure AI Foundry Opens in a new tab Gaps in current radiology vision-language models

Despite the impressive task breadth of recent models, critical gaps remain between what radiologists need and what current systems deliver:

1. No calibrated confidence for diagnostic decisions. Generative VLMs predict diagnoses as free text, but they typically do not provide calibrated confidence scores. In clinical settings, confidence matters. Clinicians cannot tune sensitivity–specificity trade-offs across clinical contexts—an important requirement for real-world deployment. Discriminative models provide these properties but lack the flexibility of open-ended generation.
2. Cross-entropy loss does not optimize clinical fidelity. Standard training methods treat all token-level errors similarly, regardless of their clinical consequences. A coordinate mistake may be penalized no more than a harmless wording change. A “yes” can be flipped to a “no” even though the clinical meaning is completely different. Missing a life-threatening finding may carry the same training penalty as omitting a minor observation. As a result, models are not explicitly optimized for what matters most in patient care.
3. No capability for measurement-dependent findings. Some radiological findings require more than visual recognition. Radiological signs such as cardiomegaly, mediastinal widening etc. depend on precise measurements. For example, a model may correctly recognize whether a chest radiograph was acquired using an AP or PA view. But determining cardiomegaly requires measuring the cardiac and thoracic widths and determining the cardiothoracic ratio. Those quantities should be measured and computed rather than visually approximated while considering variables such as type of view, exposure, rotation of the patient etc. 

Together, these gaps call for more than a fluent generative model. The system must combine broad task coverage, structured predictions, clinically aligned optimization, and quantitative tools where direct measurement is required.

CARE-X: One model, flexible outputs

CARE-X brings these diverse interpretation capabilities into one model, using generative or dual inference according to the needs of each task:

Task typeWhat CARE-X doesInference modeReport generation: FindingsProduces the detailed findings sectionGenerativeReport generation: ImpressionProduces the concise diagnostic impressionGenerativePresence and negation assessmentDetermines whether a pathology is present or absent and handles negationDual: generative + auxiliary headDisease location assessmentIdentifies where an abnormality appearsGenerativeFine-grained multilabel disease classificationCategorizes abnormalities across multiple labelsGenerativeMultilabel tubes and lines classificationIdentifies visible medical devicesGenerativeAbnormal placement detection of tubes and linesDetermines whether a device is positioned incorrectlyDual: generative + auxiliary headAbnormality phrase groundingLocalizes a described pathological findingDual: generative + auxiliary headAnatomical groundingLocalizes 29 anatomical regionsDual: generative + auxiliary headTable 1: CARE-X task coverage and inference modes

Dual inference means that a single forward pass produces both an autoregressive response and a structured auxiliary-head prediction with a confidence score. This provides free-text flexibility alongside threshold-adjustable outputs for tasks where operating-point control matters.

The CARE-X architecture and training approach

CARE-X is built on a SigLIP2-so400M vision encoder and a Phi-4-mini-instruct (3.8B) language model connected through a lightweight adapter. To support both free-text generation and structured clinical predictions, the model augments the shared language backbone with task-specific auxiliary heads for classification and visual grounding. These heads provide calibrated diagnostic predictions and spatial localization signals while sharing representations with the generative language model. Rather than being trained independently, they are co-trained with the language-modeling objective, allowing structured supervision to enrich shared representations and improve generative performance on the same tasks.

Training. CARE-X uses a three-stage supervised fine-tuning pipeline (vision pre-training, adapter/head training, and LoRA adaptation) followed by DAPO-based reinforcement learning. DAPO optimizes task-specific rewards for clinical reporting, diagnostic accuracy, and spatial grounding quality.

Figure 1. The CARE-X model. (Left) Supervised fine-tuning with task-specific heads — classification, grounding, and language modeling — sharing the same Phi-4-mini-instruct backbone. The classification head outputs calibrated P(Yes)/P(No) scores; the grounding head outputs bounding box coordinate with confidence; the language modeling head generates free-text responses. (Right) DAPO with task-specific rewards for multi-task reinforcement alignment across report generation, grounding, and VQA.  Auxiliary supervision: Structured prediction strengthens generation

A central finding of this work is that co-training discriminative auxiliary heads with a generative VLM enriches shared representations, leading to stronger generative performance on the same tasks while also providing calibrated structured predictions.

Grounding improvements

The auxiliary grounding head consistently improves localization over generative decoding. On anatomical grounding (Chest ImaGenome), mAP and mIoU increase by +28.2 pp and +6.2 pp, while the largest gains occur on phrase grounding (PadChest), with +24.6 pp mAP and +14.1 pp mIoU. The composite spatial loss enhances geometric precision in shared representations.

DAPO bridges the gap to dedicated detection heads

DAPO-trained generative output approaches or exceeds the SFT auxiliary detection head. On Anatomy grounding, CARE-X generative (0.868 mAP) surpasses the SFT detection head (0.865). This is practically significant—it demonstrates that reward-aligned learning can bring autoregressive spatial decoding to parity with structured prediction, offering clinicians a single generative inference mode without requiring auxiliary heads at test time.

Calibrated classification with tunable operating points

Beyond representation enrichment, the classification head offers a distinct deployment advantage: calibrated probability scores with tunable thresholds allow clinicians to shift between high-sensitivity screening and high-specificity confirmation from a single forward pass—a capability purely generative architectures cannot provide.

ModelInference SettingSensitivity ↑PPV ↑F1 ↑CARE-XGenerative0.9320.8950.913CARE-X (Th=0.5)Auxiliary Head0.9430.8850.913CARE-X (Th=0.6)Auxiliary Head0.8550.9270.890CheXOneGenerative0.8780.8540.866MedGemmaGenerative0.7980.8860.839Table 2: Abnormality classification performance on Chest ImaGenome. Adjustable thresholds enable operating-point selection. Strong report generation across four benchmarks

Within the paper’s comparison set, CARE-X achieves the strongest performance on most reported metrics across MIMIC-CXR, IU-Xray, CheXpert-Plus, and ReXGradient. CRIMSON, a held-out metric that evaluates abnormal findings and weights errors by clinical severity, suggests these gains reflect clinically meaningful improvements rather than reward-specific optimization.

Figure 2. CRIMSON scores (↑) for CARE-X against baseline report-generation models across four chest X-ray datasets — ReXGradient, MIMIC-CXR, IU-Xray, and CheXpert-Plus. CARE-X (highlighted) achieves the highest CRIMSON score on every dataset. CARE-X reaches 94% accuracy on ReXVQA

CARE-X ranks first on the ReXrank RexVQA leaderboard (opens in new tab) as of August 2026. On the ReXVQA benchmark (41,007 question–answer pairs across five clinically relevant categories), CARE-X reaches 94% overall accuracy, six percentage points above the next-best publicly reported model. 

Figure 3: ReXVQA accuracy across five findings-quality dimensions — negation, presence, location, differential diagnosis, geometric information, and overall. CARE-X consistently outperforms CheXOne-R1 and MedGemma on every axis, with the largest margins in differential diagnosis, location assessment and negation. Tool-augmented measurement: Interleaving perception and computation

Some radiological findings depend on quantitative measurements rather than visual patterns. In a separate research experiment from CARE-X, we built an inference-time pipeline that combines Qwen3-VL-4B-Instruct with deterministic measurement tools, allowing the model to alternate between image understanding and precise computation. Qwen3-VL-4B-Instruct retains visual access to the radiograph throughout inference, invoking tools to identify anatomical landmarks, compute measurements, and evaluate diagnostic thresholds as needed. This creates a multi-turn reasoning loop that interleaves perception and measurement, enabling the model to combine visual context with exact quantitative evidence before reaching a diagnosis.

Figure 4. Tool-augmented quantitative reasoning pipeline. The orchestrator mediates a multi-turn loop: the VLM reasons over the image (perception), emits structured tool calls, receives deterministic results, and synthesizes the final diagnosis.

Despite requiring no task-specific training, this approach substantially outperforms perception-only inference across all evaluated measurement-based conditions. The results suggest that for threshold-dependent diagnoses, direct computation of clinically defined measurements is more reliable than visual approximation alone.

More broadly, this measurement-augmented approach could augment clinical workflows by expanding the set of quantitative assessments routinely derived from chest radiographs. For example, aortic dilation is not typically quantified on CXR and is often detected only incidentally on CT scans obtained for other indications. As delayed detection can contribute to adverse cardiovascular outcomes, reliable CXR-based screening could enable earlier identification and follow-up of aortic dilation.

ConditionPerception F1Tool F1Δ F1Cardiomegaly74.5696.00+21.4Mediastinal Widening72.6397.47+24.8Aortic Knob Enlargement60.3199.76+39.5Ascending Aorta Enlargement39.33100.00+60.7Descending Aorta Enlargement†28.57100.00+71.4Average+43.6Table 3: Perception-only versus tool-augmented measurement. The average F1 improvement is 43.6 percentage points across five conditions. Validation on Indian clinical data: Rare ICU conditions and CT-confirmed enlargement

Research ethics and data use: The Narayana Health evaluations used de-identified, retrospective clinical data under applicable institutional ethics review and data-use approvals. Narayana Health approved publication of the study results described here. 

Study 1: Inpatient and ICU conditions

To assess real-world generalizability in a research setting, we evaluated CARE-X on 1,047 de-identified chest radiographs from Narayana Health, annotated for five rare, high-acuity conditions with prevalence ranging from 2.6% to 5.2%—reflecting realistic clinical distributions where missed diagnoses carry severe consequences. 

FractureMediastinal ShiftPneumoperitoneumPneumothoraxTubes & Lines Abnormal PlacementModelSens / SpecSens / SpecSens / SpecSens / SpecSens / SpecCheXOne0.41 / 0.900.80 / 0.780.67 / 0.980.85 / 0.720.03 / 0.97MedGemma0.05 / 1.001.00 / 0.530.00 / 1.000.52 / 0.730.18 / 0.87CARE-X0.62 / 0.640.83 / 0.860.89 / 0.940.83 / 0.750.66 / 0.77Table 4: ICU pathology classification on Indian hospital data. CARE-X achieves the most balanced performance.

CARE-X achieves the highest sensitivity in three out of five conditions while maintaining reasonable specificity, demonstrating generalization to low-prevalence clinical settings.

Study 2: CT-confirmed enlargement conditions

In a retrospective study to measure pure recall efficacy, we evaluated measurement-dependent conditions such as mediastinal widening findings including aortic enlargement, hilar mass, and pulmonary artery enlargement on a outpatient cohort of 122 positive cases with CT-confirmed ground truth, avoiding the subjectivity of radiologist consensus on borderline enlargement findings on CXR. In the overlay setting, the VLM receives the original radiograph alongside a second image with condition-relevant anatomical segmentation masks — offering spatial guidance without direct access to measurement tools.

The tool-augmented variant reached 94.26% recall, a +10.65 percentage-point gain over the best perception-only baseline. Where CT or echocardiography access is limited, reliable triage from a widely available modality like chest X-ray can cut both unnecessary referrals and missed diagnoses.

Figure 5: Recall on the CT-confirmed enlargement cohort across perception-only, overlay-assisted, and tool-augmented inference. (Study 2)

In a related study (accepted at EACTS conference 2026), for mild aortic dilation, the measurement-driven reasoning approach detected 40 of 43 CT-confirmed cases (93% sensitivity), compared to just 5 of 43 (12%) identified on the initial radiology reads, where aortic enlargement is usually not the primary indication for the chest X-ray. This corresponds to 35 additional mild cases that were surfaced but missed during the initial CXR interpretation. These results suggest that explicit quantitative measurements may help identify borderline enlargement that is difficult to assess through visual inspection alone. 

What this does and doesn’t show 

These numbers are all recall, i.e., how many true positives we catch. This was the focus of the initial study because, in triage, a missed diagnosis is typically the costlier failure mode, and CT-confirmed ground truth gave us a clean way to measure it without relying on radiologist consensus for the difficult cases. 

Recall, however, captures only one dimension of diagnostic performance. A model that flags everything achieves perfect recall and is useless in practice. An extended study is underway that includes CT-confirmed negative cohorts as well. Preliminary results are promising, and further studies are planned to explicitly evaluate the viability of quantitative aortic measurements on chest X-ray as a screening tool for aortic dilation. 

CARE-X: Toward clinically useful radiology AI

CARE-X demonstrates that discriminative and generative objectives can be effectively combined within a unified radiology AI model. By jointly training classification, grounding, and language capabilities, the model supports both flexible report generation and calibrated, threshold-adjustable predictions. The separate measurement study further highlights a practical division of labor between learned reasoning and deterministic computation: the VLM provides visual understanding and identifies relevant evidence, while measurement-dependent diagnoses are computed through transparent, tool-based calculations. Retrospective evaluation on clinically challenging Narayana Health cohorts provides encouraging evidence of the potential of this approach for real-world radiology applications. The clinical relevance of this research is underscored by the selection of the AI-based aortic dilatation screening application as a finalist for showcase at the IHF Innovation Hub, World Hospital Congress 2026, recognizing its potential to support earlier detection and clinical decision-making in cardiovascular care. 

Looking ahead, CARE-X can be extended beyond its current capabilities through structured report generation, richer differential diagnosis support, and tighter integration of tools within the model itself. The framework could also benefit from incorporating broader clinical context, including laboratory results and patient history, enabling more comprehensive clinical reasoning. 

CARE-X is a research model, not a Microsoft product offering or medical device. It has not been cleared or approved by any regulatory authority and is not intended or validated for clinical diagnosis, screening, patient care, or clinical decision-making. The results described are retrospective research findings and do not establish safety, effectiveness, or suitability for clinical use.  

Paper co-authors:

Mercy Ranjit, Anirban Porya (opens in new tab), Niharika Vadlamudi (opens in new tab), Nikhilesh E (opens in new tab), Sathvik Joel (opens in new tab), Prasanth V V (opens in new tab), Tanuja Ganu, Abhyuday Swamy (opens in new tab), Pranay Umredkar (opens in new tab), Pradeep Narayan (opens in new tab), Vivek Rajagopal (opens in new tab)

Collaborators: Medha AI (opens in new tab), Narayana Health (opens in new tab)

Opens in a new tab

The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research.

Research Note: CARE-X is a research model and not a Microsoft product offering or medical device. It has not been cleared or approved by any regulatory authority and is not intended for clinical diagnosis, screening, or patient care. The results described below are retrospective research findings and do not establish the safety, effectiveness, or suitability of CARE-X for any clinical use. References to potential workflows describe areas for future research, not currently available capabilities or recommended uses. 

At a glance

• The challenge: Chest X-ray interpretation spans diverse tasks that require both expressive report generation and calibrated diagnostic predictions.
• CARE-X is a unified chest X-ray VLM for diverse clinical interpretation tasks. It combines generation and structured prediction to provide both free-text reasoning and deterministic outputs.
• CARE-X uses reinforcement learning (DAPO) to reward clinical correctness in a multi-task setting.
• In a separate research experiment from CARE-X, we paired Qwen3-VL-4B-Instruct with deterministic measurement tools to evaluate whether direct computation could improve performance on measurement-dependent conditions compared with visual approximation alone. 
• Validated on real-world Indian clinical data from Narayana Health, including rare ICU pathologies and CT-confirmed enlargement conditions.

What radiologists need: Task diversity, flexibility, and clinical fidelity

A clinically useful radiology AI system must support a wide range of tasks, adapt to different workflows, and produce outputs that are medically accurate.

Radiologists and other clinicians use chest X-rays for many different purposes. A clinically useful AI system must be able to support that range of tasks. It may be asked to generate detailed findings and concise impressions for a report, answer questions about the presence, absence, or location of a finding, identify medical devices and assess their placement, or pinpoint exactly where an abnormality appears in an image.

These tasks also require different kinds of outputs, from narrative reports to calibrated diagnostic scores. And above all, they require clinical accuracy. A report could ostensibly be perfectly written yet clinically wrong if it misses a finding, reverses a negation, or misidentifies a location. Certain findings could be trivial in one context and vital to identify in another.

CARE-X was developed as a research model to explore how a unified approach can address these diverse demands. The system combines generative and discriminative capabilities, clinically aligned optimization, and tool-based reasoning to support a broader range of radiology workflows while maintaining clinical fidelity.

Azure AI Foundry Labs

Get a glimpse of potential future directions for AI, with these experimental technologies from Microsoft Research.

Azure AI Foundry Opens in a new tab Gaps in current radiology vision-language models

Despite the impressive task breadth of recent models, critical gaps remain between what radiologists need and what current systems deliver:

1. No calibrated confidence for diagnostic decisions. Generative VLMs predict diagnoses as free text, but they typically do not provide calibrated confidence scores. In clinical settings, confidence matters. Clinicians cannot tune sensitivity–specificity trade-offs across clinical contexts—an important requirement for real-world deployment. Discriminative models provide these properties but lack the flexibility of open-ended generation.
2. Cross-entropy loss does not optimize clinical fidelity. Standard training methods treat all token-level errors similarly, regardless of their clinical consequences. A coordinate mistake may be penalized no more than a harmless wording change. A “yes” can be flipped to a “no” even though the clinical meaning is completely different. Missing a life-threatening finding may carry the same training penalty as omitting a minor observation. As a result, models are not explicitly optimized for what matters most in patient care.
3. No capability for measurement-dependent findings. Some radiological findings require more than visual recognition. Radiological signs such as cardiomegaly, mediastinal widening etc. depend on precise measurements. For example, a model may correctly recognize whether a chest radiograph was acquired using an AP or PA view. But determining cardiomegaly requires measuring the cardiac and thoracic widths and determining the cardiothoracic ratio. Those quantities should be measured and computed rather than visually approximated while considering variables such as type of view, exposure, rotation of the patient etc. 

Together, these gaps call for more than a fluent generative model. The system must combine broad task coverage, structured predictions, clinically aligned optimization, and quantitative tools where direct measurement is required.

CARE-X: One model, flexible outputs

CARE-X brings these diverse interpretation capabilities into one model, using generative or dual inference according to the needs of each task:

Task typeWhat CARE-X doesInference modeReport generation: FindingsProduces the detailed findings sectionGenerativeReport generation: ImpressionProduces the concise diagnostic impressionGenerativePresence and negation assessmentDetermines whether a pathology is present or absent and handles negationDual: generative + auxiliary headDisease location assessmentIdentifies where an abnormality appearsGenerativeFine-grained multilabel disease classificationCategorizes abnormalities across multiple labelsGenerativeMultilabel tubes and lines classificationIdentifies visible medical devicesGenerativeAbnormal placement detection of tubes and linesDetermines whether a device is positioned incorrectlyDual: generative + auxiliary headAbnormality phrase groundingLocalizes a described pathological findingDual: generative + auxiliary headAnatomical groundingLocalizes 29 anatomical regionsDual: generative + auxiliary headTable 1: CARE-X task coverage and inference modes

Dual inference means that a single forward pass produces both an autoregressive response and a structured auxiliary-head prediction with a confidence score. This provides free-text flexibility alongside threshold-adjustable outputs for tasks where operating-point control matters.

The CARE-X architecture and training approach

CARE-X is built on a SigLIP2-so400M vision encoder and a Phi-4-mini-instruct (3.8B) language model connected through a lightweight adapter. To support both free-text generation and structured clinical predictions, the model augments the shared language backbone with task-specific auxiliary heads for classification and visual grounding. These heads provide calibrated diagnostic predictions and spatial localization signals while sharing representations with the generative language model. Rather than being trained independently, they are co-trained with the language-modeling objective, allowing structured supervision to enrich shared representations and improve generative performance on the same tasks.

Training. CARE-X uses a three-stage supervised fine-tuning pipeline (vision pre-training, adapter/head training, and LoRA adaptation) followed by DAPO-based reinforcement learning. DAPO optimizes task-specific rewards for clinical reporting, diagnostic accuracy, and spatial grounding quality.

Figure 1. The CARE-X model. (Left) Supervised fine-tuning with task-specific heads — classification, grounding, and language modeling — sharing the same Phi-4-mini-instruct backbone. The classification head outputs calibrated P(Yes)/P(No) scores; the grounding head outputs bounding box coordinate with confidence; the language modeling head generates free-text responses. (Right) DAPO with task-specific rewards for multi-task reinforcement alignment across report generation, grounding, and VQA.  Auxiliary supervision: Structured prediction strengthens generation

A central finding of this work is that co-training discriminative auxiliary heads with a generative VLM enriches shared representations, leading to stronger generative performance on the same tasks while also providing calibrated structured predictions.

Grounding improvements

The auxiliary grounding head consistently improves localization over generative decoding. On anatomical grounding (Chest ImaGenome), mAP and mIoU increase by +28.2 pp and +6.2 pp, while the largest gains occur on phrase grounding (PadChest), with +24.6 pp mAP and +14.1 pp mIoU. The composite spatial loss enhances geometric precision in shared representations.

DAPO bridges the gap to dedicated detection heads

DAPO-trained generative output approaches or exceeds the SFT auxiliary detection head. On Anatomy grounding, CARE-X generative (0.868 mAP) surpasses the SFT detection head (0.865). This is practically significant—it demonstrates that reward-aligned learning can bring autoregressive spatial decoding to parity with structured prediction, offering clinicians a single generative inference mode without requiring auxiliary heads at test time.

Calibrated classification with tunable operating points

Beyond representation enrichment, the classification head offers a distinct deployment advantage: calibrated probability scores with tunable thresholds allow clinicians to shift between high-sensitivity screening and high-specificity confirmation from a single forward pass—a capability purely generative architectures cannot provide.

ModelInference SettingSensitivity ↑PPV ↑F1 ↑CARE-XGenerative0.9320.8950.913CARE-X (Th=0.5)Auxiliary Head0.9430.8850.913CARE-X (Th=0.6)Auxiliary Head0.8550.9270.890CheXOneGenerative0.8780.8540.866MedGemmaGenerative0.7980.8860.839Table 2: Abnormality classification performance on Chest ImaGenome. Adjustable thresholds enable operating-point selection. Strong report generation across four benchmarks

Within the paper’s comparison set, CARE-X achieves the strongest performance on most reported metrics across MIMIC-CXR, IU-Xray, CheXpert-Plus, and ReXGradient. CRIMSON, a held-out metric that evaluates abnormal findings and weights errors by clinical severity, suggests these gains reflect clinically meaningful improvements rather than reward-specific optimization.

Figure 2. CRIMSON scores (↑) for CARE-X against baseline report-generation models across four chest X-ray datasets — ReXGradient, MIMIC-CXR, IU-Xray, and CheXpert-Plus. CARE-X (highlighted) achieves the highest CRIMSON score on every dataset. CARE-X reaches 94% accuracy on ReXVQA

CARE-X ranks first on the ReXrank RexVQAleaderboard (opens in new tab) as of August 2026. On the ReXVQA benchmark (41,007 question–answer pairs across five clinically relevant categories), CARE-X reaches 94% overall accuracy, six percentage points above the next-best publicly reported model. 

Figure 3: ReXVQA accuracy across five findings-quality dimensions — negation, presence, location, differential diagnosis, geometric information, and overall. CARE-X consistently outperforms CheXOne-R1 and MedGemma on every axis, with the largest margins in differential diagnosis, location assessment and negation. Tool-augmented measurement: Interleaving perception and computation

Some radiological findings depend on quantitative measurements rather than visual patterns. In a separate research experiment from CARE-X, we built an inference-time pipeline that combines Qwen3-VL-4B-Instruct with deterministic measurement tools, allowing the model to alternate between image understanding and precise computation. Qwen3-VL-4B-Instruct retains visual access to the radiograph throughout inference, invoking tools to identify anatomical landmarks, compute measurements, and evaluate diagnostic thresholds as needed. This creates a multi-turn reasoning loop that interleaves perception and measurement, enabling the model to combine visual context with exact quantitative evidence before reaching a diagnosis.

Figure 4. Tool-augmented quantitative reasoning pipeline. The orchestrator mediates a multi-turn loop: the VLM reasons over the image (perception), emits structured tool calls, receives deterministic results, and synthesizes the final diagnosis.

Despite requiring no task-specific training, this approach substantially outperforms perception-only inference across all evaluated measurement-based conditions. The results suggest that for threshold-dependent diagnoses, direct computation of clinically defined measurements is more reliable than visual approximation alone.

More broadly, this measurement-augmented approach could augment clinical workflows by expanding the set of quantitative assessments routinely derived from chest radiographs. For example, aortic dilation is not typically quantified on CXR and is often detected only incidentally on CT scans obtained for other indications. As delayed detection can contribute to adverse cardiovascular outcomes, reliable CXR-based screening could enable earlier identification and follow-up of aortic dilation.

ConditionPerception F1Tool F1Δ F1Cardiomegaly74.5696.00+21.4Mediastinal Widening72.6397.47+24.8Aortic Knob Enlargement60.3199.76+39.5Ascending Aorta Enlargement39.33100.00+60.7Descending Aorta Enlargement†28.57100.00+71.4Average+43.6Table 3: Perception-only versus tool-augmented measurement. The average F1 improvement is 43.6 percentage points across five conditions. Validation on Indian clinical data: Rare ICU conditions and CT-confirmed enlargement

Research ethics and data use: The Narayana Health evaluations used de-identified, retrospective clinical data under applicable institutional ethics review and data-use approvals. Narayana Health approved publication of the study results described here. 

Study 1: Inpatient and ICU conditions

To assess real-world generalizability in a research setting, we evaluated CARE-X on 1,047 de-identified chest radiographs from Narayana Health, annotated for five rare, high-acuity conditions with prevalence ranging from 2.6% to 5.2%—reflecting realistic clinical distributions where missed diagnoses carry severe consequences. 

FractureMediastinal ShiftPneumoperitoneumPneumothoraxTubes & Lines Abnormal PlacementModelSens / SpecSens / SpecSens / SpecSens / SpecSens / SpecCheXOne0.41 / 0.900.80 / 0.780.67 / 0.980.85 / 0.720.03 / 0.97MedGemma0.05 / 1.001.00 / 0.530.00 / 1.000.52 / 0.730.18 / 0.87CARE-X0.62 / 0.640.83 / 0.860.89 / 0.940.83 / 0.750.66 / 0.77Table 4: ICU pathology classification on Indian hospital data. CARE-X achieves the most balanced performance.

CARE-X achieves the highest sensitivity in three out of five conditions while maintaining reasonable specificity, demonstrating generalization to low-prevalence clinical settings.

Study 2: CT-confirmed enlargement conditions

In a retrospective study to measure pure recall efficacy, we evaluated measurement-dependent conditions such as mediastinal widening findings including aortic enlargement, hilar mass, and pulmonary artery enlargement on a outpatient cohort of 122 positive cases with CT-confirmed ground truth, avoiding the subjectivity of radiologist consensus on borderline enlargement findings on CXR. In the overlay setting, the VLM receives the original radiograph alongside a second image with condition-relevant anatomical segmentation masks — offering spatial guidance without direct access to measurement tools.

The tool-augmented variant reached 94.26% recall, a +10.65 percentage-point gain over the best perception-only baseline. Where CT or echocardiography access is limited, reliable triage from a widely available modality like chest X-ray can cut both unnecessary referrals and missed diagnoses.

Figure 5: Recall on the CT-confirmed enlargement cohort across perception-only, overlay-assisted, and tool-augmented inference. (Study 2)

In a related study (accepted at EACTS conference 2026), for mild aortic dilation, the measurement-driven reasoning approach detected 40 of 43 CT-confirmed cases (93% sensitivity), compared to just 5 of 43 (12%) identified on the initial radiology reads, where aortic enlargement is usually not the primary indication for the chest X-ray. This corresponds to 35 additional mild cases that were surfaced but missed during the initial CXR interpretation. These results suggest that explicit quantitative measurements may help identify borderline enlargement that is difficult to assess through visual inspection alone. 

What this does and doesn’t show 

These numbers are all recall, i.e., how many true positives we catch. This was the focus of the initial study because, in triage, a missed diagnosis is typically the costlier failure mode, and CT-confirmed ground truth gave us a clean way to measure it without relying on radiologist consensus for the difficult cases. 

Recall, however, captures only one dimension of diagnostic performance. A model that flags everything achieves perfect recall and is useless in practice. An extended study is underway that includes CT-confirmed negative cohorts as well. Preliminary results are promising, and further studies are planned to explicitly evaluate the viability of quantitative aortic measurements on chest X-ray as a screening tool for aortic dilation. 

CARE-X: Toward clinically useful radiology AI

CARE-X demonstrates that discriminative and generative objectives can be effectively combined within a unified radiology AI model. By jointly training classification, grounding, and language capabilities, the model supports both flexible report generation and calibrated, threshold-adjustable predictions. The separate measurement study further highlights a practical division of labor between learned reasoning and deterministic computation: the VLM provides visual understanding and identifies relevant evidence, while measurement-dependent diagnoses are computed through transparent, tool-based calculations. Retrospective evaluation on clinically challenging Narayana Health cohorts provides encouraging evidence of the potential of this approach for real-world radiology applications. The clinical relevance of this research is underscored by the selection of the AI-based aortic dilatation screening application as a finalist for showcase at the IHF Innovation Hub, World Hospital Congress 2026, recognizing its potential to support earlier detection and clinical decision-making in cardiovascular care. 

Looking ahead, CARE-X can be extended beyond its current capabilities through structured report generation, richer differential diagnosis support, and tighter integration of tools within the model itself. The framework could also benefit from incorporating broader clinical context, including laboratory results and patient history, enabling more comprehensive clinical reasoning. 

CARE-X is a research model, not a Microsoft product offering or medical device. It has not been cleared or approved by any regulatory authority and is not intended or validated for clinical diagnosis, screening, patient care, or clinical decision-making. The results described are retrospective research findings and do not establish safety, effectiveness, or suitability for clinical use.  

Paper co-authors:

Mercy Ranjit, Anirban Porya (opens in new tab), Niharika Vadlamudi (opens in new tab), Nikhilesh E (opens in new tab), Sathvik Joel (opens in new tab), Prasanth V V (opens in new tab), Tanuja Ganu, Abhyuday Swamy (opens in new tab), Pranay Umredkar (opens in new tab), Pradeep Narayan (opens in new tab), Vivek Rajagopal (opens in new tab)

Collaborators: Medhai AI, Narayana Health (opens in new tab)

Opens in a new tab

The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research.

Image editors and desktop publishing tools keep getting better all the time. While I love a good dedicated publishing tool like Scribus, sometimes I need something quick to test layouts or make images/videos fast. There are plenty of image editing tools out there, but Canva and PenPot are two of the most powerful that I've used. So I decided to compare them and see how they both fit into my workflow.

SAVE $700.99: As of August 11, get the Ecovacs Goat A3000 Robotic Lawn Mower for $1,799 at Amazon, down from its usual price of $2,499.99. That's a discount of 28%.

Opens in a new window Credit: Amazon Ecovacs Goat A3000 robotic lawn mower $2,499.99 at Amazon
  Get Deal

If the idea of having to spend time mowing your lawn sounds like the worst time possible, it might be a good idea to invest in a robot to handle it for you. Robotic lawn mowers are a great way to keep a tidy lawn without actually having to do anything, and you can get one on sale for an excellent price right now.

As of August 11, get the Ecovacs Goat A3000 Robotic Lawn Mower for $1,799 at Amazon, down from its usual price of $2,499.99. That's $700.99 off and a discount of 28%.

SEE ALSO: The best robot lawn mowers in 2025

This robot vacuum requires no perimeter wire installation and lets you go wire-free thanks to the HoloScope 360 Dual-LiDAR navigation system and AI camera. It can handle itself while mapping your yard, up to 3/4 acre, and can remain accurate up to less than an inch even when dealing with less than optimal conditions.

It uses a TruEdge trimmer to keep borders around your lawn clean-looking, so you don't have overgrown grass and weeds around your flower beds, driveway, or other lawn decor. And with a massive 7,500 mAh battery that can be charged back up in just over an hour, it can get the job done without you having to sit and wait for it to juice back up for too long.

Though we're heading into fall, you're still going to have to contend with keeping your lawn fresh and clean. Make it easier on yourself with this robot and write off one more home task that you won't have to deal with.

Some tech projects generate a lot of information, and that information isn't always used as well as it could be. I have a Raspberry Pi project running bird detection software, and it's identified a large number of species. I decided to see if I could take the audio that it had captured and turn it into an educational game.

Sumerian Pictures' new trailer for Josephine is a tense experience, teasing a chilling mystery around one question: What did Josephine (newcomer Mason Reeves) see happen in the park? But the film, of course, digs even further.

This Sundance Grand Jury and Audience Award-winner from writer and director Beth de Araújo (Soft & Quiet) is based on her own life experiences. Channing Tatum and Gemma Chan lead the film as parents of eight-year-old Josephine, who witnesses a crime in San Francisco's Golden Gate Park. How can her parents help her process it?

Mashable entertainment editor Kristy Puchko called Josephine "easily the most buzzed-about title out of Sundance."

"Not a tear-jerker but a nuanced family drama that's ripe in emotional intelligence and thought-provoking sequences, Josephine is a hard watch and a must-see," she writes in her Sundance round-up.

The cast also includes Philip Ettinger, Syra McCarthy, and Eleanore Pienta.

Josephine hits cinemas Nov. 20.

You might want to think carefully the next time you copy-paste text from an AI chatbot. Anthropic has confirmed that its Claude AI models now watermark generated text and files, making it easier to gauge the authenticity of material — with major ramifications for slop and privacy issues.

With Nvidia's RTX 50 Super a pipe dream, and GPU prices crazier than ever, now is not the time to get a new graphics card. Look, I wouldn't mind, but no. The prices are way too wild.

There's a secret lurking at the heart of Hellcat, a horror film soon to hit Shudder.

On the festival circuit, critics cheered writer/director Brock Bodell's feature film debut for its intensity and its surprising twist. To tantalize would-be viewers, Shudder has shared with Mashable an exclusive clip from Hellcat. And as someone who knows its secrets, I can assure you that this clip has some telling clues about the movie's secret subgenre.

But I'm getting ahead of myself.

SEE ALSO: Best horror movies of 2026, and where to watch them now

Hellcat stars Dakota Gorman as Lena, a young woman who awakes to find herself in a mobile home that she's never seen before. The windows are covered, but she can tell she's speeding along down some road. But who put her here? And why is she locked inside? Soon, she'll learn her captor is a man named Clive (Todd Terry), who communicates with her from the cab of his tow truck through a PA system.

In the clip above, Lena's searching the trailer when she finds a mini-fridge, which, alongside some condiments, holds a rack of needles that she notes are filled with lorazepam (an anti-anxiety medication), along with a plate of bloody, raw meat.

Clive bellows, "Do not touch my medication!" but he won't explain more. But before Lena can demand answers, she's attracted to the bloody meat. And then, she hears a strange sound — a woman's whimper — coming from the back of the camper.

What's going on?! You'll have to watch Hellcat to find out.

Hellcat premieres on Shudder on Aug. 14.

SAVE $70: As of Aug. 11, new and former subscribers can score a full year of ad-free Paramount+ Premium for just $69.99 instead of the usual $139.99 with the code saveannual50. That's a 50% discount.

Opens in a new window Credit: Paramount+ Paramount+ Premium $69.99/year for 1 year (save $70) with code saveannual50 Get Deal

If you've been hunting for a good streaming deal this summer, your search ends here. A new promo code for ad-free Paramount+ just dropped — and this time it's for a full year instead of just a couple of months.

As of Aug. 11, new and former Paramount+ subscribers can unlock an entire year of the ad-free Premium plan for just $69.99 with the code saveannual50. That's $70 or 50% in savings. Crucial: On the sign-up page, be sure to toggle the plan from "monthly" to "annual" and select the Premium ad-free option. On the checkout screen, click on "Have a promo code?" and enter saveannual50. The discount will then automatically apply.

Don't forget to enter the promo code at checkout. Credit: Paramount+

The code is actively working as of Aug. 11, but the end date is unspecified. So if you want to lock in the savings, do it ASAP.

A Premium subscription includes ad-free streaming (except on live TV), access to your local CBS network live, the entirety of Showtime's catalog, the ability to download shows and movies for offline viewing, and a robust library of Original Series, Paramount Pictures films straight from the theater, nostalgic shows from MTV, Nickelodeon, Comedy Central, and more. And as of this year, it's also the exclusive home of UFC's marquee events as well as 30 Fight Nights — including the upcoming UFC 330 event. That's a whole lot of content for what breaks down to just $5.83 per month.

If you're looking to diversify your streaming lineup while beating the summer heat, half-priced Paramount+ Premium is an excellent value. Just be sure to set your calendar alert to cancel it a year from now if you want to avoid paying full cost.

I've used Windows for most of my life, but I've become increasingly frustrated with the way Microsoft expects me to work. Search is still unreliable, useful context-menu options are hidden behind an extra click, and Windows can use my internet connection to help distribute updates to other PCs through Delivery Optimization. Add in Microsoft's recommendations I never asked for and constant interface changes, and there's no shortage of reasons to consider leaving Windows.

Something's very off in the trailer for Gentle Monster, with Léa Seydoux starring as a woman facing dark truths in her own home. And though the film's absolutely not about the Korean eyewear brand, the director was inspired by the company's name.

Written and directed by Marie Kreutzer, the film follows concert pianist Lucy (Seydoux) whose idyllic home life with her husband Philip (Laurence Rupp) and son is shattered when the police show up with a search warrant, accusing Phillip of a heinous crime. Forced to reckon with the accusations against the person she loves, Lucy has more than one lie to untangle, and her son to protect.

It's a complicated film, thematically, for Kreutzer, whose 2022 film Corsage starred actor Florian Teichtmeister, who was charged for similar crimes. The director told The Hollywood Reporter in May 2026, "In the middle of that whole scandal and shit storm, I thought maybe I cannot make Gentle Monster anymore, because everyone will always make that connection. It didn’t take me very long until to realize that I might have to make it even more."

From the trailer, we get a glimpse of director of photography Judith Kaufmann's cinematography, music by Camille, and a powerful performance from Seydoux, who stars alongside Jella Haase and Catherine Deneuve.

Gentle Monster will premiere at the Toronto International Film Festival (TIFF) in September, before its release in select cinemas Oct. 30. The film will then be streaming on Netflix from Nov. 18.

Want more film and TV news and reviews delivered straight to your inbox? Sign up for Mashable’s Top Stories newsletter or get Mashable push alerts today.

SAVE $400.01: As of August 11, get the Bluetti Elite 300 portable power station for $1098.99 at Amazon, down from its usual price of $1,499. That's a discount of 27%.

Opens in a new window Credit: Amazon Bluetti Elite 300 portable power station $1,098.99 at Amazon
$1,499 Save $400.01   Get Deal

Though summer is quickly fading away into fall, that doesn't mean severe weather just goes away. Heavy storms can often bring power failures and outages, and if you're someone who tends to deal with losing your electricity when they roll through, a portable power station could be the ticket to easier times. If you're looking to get one right now, this deal is a great pick that can help you out in a variety of situations.

As of August 11, get the Bluetti Elite 300 portable power station for $1098.99 at Amazon, down from its usual price of $1,499. That's $400.01 off and a discount of 27%.

SEE ALSO: The 8 best portable power stations to stay charged up at home and on the go

This portable power station is the perfect solution to losing electricity or staying powered up while camping or going off-grid. It's a larger power station that boasts about 3,000Wh of capacity with a 2,400W output. But even though it's got plenty of juice, it's still relatively small at just 14 inches wide, 12 inches tall, and 12 inches in depth. It weighs about 60 pounds, so it might be a bit more difficult to tote around, but if you want to give it a more permanent placement, it's definitely the right fit.

It has a large enough battery capacity to keep you going for quite some time though, no matter the use case. It can recharge a 60W laptop 33 times or a 10W phone over 90 times. Plus, if you need Wi-Fi while the power's out for internet or even VoIP phone service, it'll keep you running for over 85 hours.

Now's a great time to go ahead and be ready for a variety of power-centric situations, so be sure to grab yours while this Bluetti model is on sale.

In Home Alone 2, Kevin McCallister scoffs at Cedric when asked if he knows how the television works. "I'm 10 years old. TV's my life," Kevin tells Cedric before tipping him with gum. I'm much older than Kevin, but I can relate to what he said about TV. It's not my entire life, but it's certainly a crucial part, considering I write for How-To Geek's streaming vertical.

Few things are more frustrating than a phone that suddenly starts feeling sluggish. It's even worse when there's no obvious cause, like overheating or running a demanding app. That's because Android is a surprisingly complex operating system, and the real reason behind a slowdown isn't always easy to pinpoint.

Are you looking for a new laptop as school approaches? Look no further than the Lenovo IdeaPad Slim 3i 15 Gen 11. With a 15.3-inch 16:10 120Hz display, the latest Intel Core 7 processor, and ample I/O, this laptop is sure to be the perfect mix of work and play that you've been looking for.

Anthropic's Claude will start watermarking AI-generated content in compliance with EU rules, the company announced on Tuesday.

In a Claude support document, Anthropic wrote that it has signed the EU AI Act's Article 50(2) Code of Practice on Transparency of AI-Generated Content, and explains how it plans to put those rules into practice.

SEE ALSO: Claude Code’s auto mode will be on by default, Anthropic confirms

Models launched in the EU on or after Aug. 2, 2026, wrote Anthropic, will support watermarking at launch. This includes output from all models including Claude Platform (API), Claude, Claude Code, Claude Cowork, and Claude Tag. Generated text will have embedded watermarks, while generated files will include "digitally signed provenance metadata where supported."

Notably, it won't apply only to Europe; instead, the watermarks will appear wherever Claude is offered, worldwide, though Anthropic warns that some platforms or features may not support certain watermarking types.

Featured Video For You Is ChatGPT Changing the Way We Write?

The watermarks won't be visible if you don't know what you're looking for. For text files, the watermark will be "part of the text" and it will "travel with the text when it's copied and pasted elsewhere," but Anthropic doesn't really go into the technicalities of how this will work. For files such as images, Anthropic will use the Coalition for Content Provenance and Authenticity (C2PA) open standard. Details on how to recognize these watermarks will be shared in "forthcoming documentation," wrote Anthropic.

As for existing models that were launched before Aug. 2, 2026 (that's pretty much all of them at writing time), Anthropic says it's working to add watermarking support to those as well.

Watermarking won't be foolproof, warns Anthropic. For example, heavily edited, paraphrased, or translated text could not carry a detectable watermark, and a file's metadata could be stripped through format conversion, screenshots, or other means.

Anthropic's move follows similar commitments by AI companies including OpenAI, Google, and Meta.

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There are plenty of smart devices that are significant upgrades over their dumb equivalents. A video doorbell, for example, is much more useful than a standard doorbell. While smart locks do offer some useful features, there are plenty of reasons why replacing your current lock might not be the best idea.