<![CDATA[Explore why sexual health matters after 65 and how clinician comfort boosts disclosure.]]>
Guarding biotech from China and big bets in longevity
On this week’s episode of “The Readout LOUD,” the hosts discuss STAT’s Breakthrough Summit West, where powerful leaders from health care and science rubbed shoulders. They share some of the juicy conversations and insights, including BridgeBio CEO Neil Kumar’s comments that the company is publishing “not the right structures” in their research publications.
Listeners also will hear Allison’s interview with Joe Betts-LaCroix, CEO of the Sam Altman-backed longevity company Retro Biosciences. Retro is preparing for its first clinical data readout and examining just how much impact AI can have in drug development.
Next-Generation Cardiac AI System Outperforms Existing Models
A next-generation artificial intelligence system can analyze complex heart scans better than existing models without the need for laboriously, manually labeled training data.
The system, outlined in Nature Communications, sets the scene for multimodal learning approaches to be further integrated into medical imaging, with the potential to improve diagnoses and patient outcomes.
The vision-language self-supervised learning framework for cardiac magnetic resonance (CMR) imaging uses contrastive language image pretraining (CLIP) and treats scans as videos of the beating heart.
The novel CMR-CLIP system outperformed existing models by 35% and was better at identifying common pathologies such as myocardial fibrosis and left ventricular hypertrophy.
It also beat other models at common computational tasks such as the retrieval of CMR studies or radiology reports and downstream disease classification tasks.
“Systems like CMR-CLIP have the potential to support clinicians through automated screening, and interpretation support, particularly in settings where expert readers are limited,” explained researcher David Chen, PhD, of Cleveland Clinic.
“Such reader assistant tools are critical to improving patient access to this powerful diagnostic technology.”
Cardiac magnetic resonance imaging is the definitive way to diagnose several cardiac diseases including valvular pathologies, cardiomyopathies, pericardial and aortic diseases.
However, interpreting and documenting each exam takes a great deal of time due to the amount of information collected in each CMR exam—often more than 40 minutes per study.
Vision-language models trained using self-supervised learning are therefore crucial to reduce dependency on large volumes of labeled data.
However, conventional self-supervised approaches that rely on precise image-text pairing are not always feasible for CMR, given that it is able to visualize cardiac anatomy, physiology, and microstructure in a single exam.
Unlike either generalists and other biomedical domain-specific models, which are trained using individual images or limited views, CMR-CLIP incorporates a wide variety of standard cardiac views and image types that represent of morphology, function, and myocardial viability.
The vision language model connects images and associated reports, treating the various views of the heart and image types as a sequence of images in video format.
The model was trained on over a million images from over 10,000 unique studies at a single institution and performed well on evaluation in both on internal and external datasets.
The researchers said it achieved “remarkable performance” at real-world clinical tasks, reaching accuracies of 88.5% for non-ischemic cardiomyopathy, 88.0% for ischemic cardiomyopathy, 96.2% for cardiac amyloidosis, and 98.6% for hypertrophic cardiomyopathy.
“This work demonstrates that domain-specific foundation models can significantly outperform general-purpose AI systems in specialized clinical applications,” said researcher Ding Zhao, PhD, Carnegie Mellon University.
“By designing models that reflect the structure and complexity of cardiac MRI data, rather than adapting generic image models, we can unlock new levels of performance and clinical utility.”
The post Next-Generation Cardiac AI System Outperforms Existing Models appeared first on Inside Precision Medicine.
Open-Source Algorithm Advances Precision Menstrual Health Beyond Fertility
Scientists at SRI International have developed an algorithm that analyzes menstrual cycle data to uncover hidden connections to overall health and aging, moving beyond the fertility focus of most previous research. In a study published in Science Advances, the tool revealed how aging influences key changes during the menstrual cycle and identified markers of individual variability that could be leveraged for the development of personalized approaches to menstrual health.
“Across the reproductive life stage, a woman living in the United States would have, on average, 450 menstrual cycles, out of which [approximately] 3.2 cycles result in pregnancy. Yet, most of the focus on menstrual health—including research, medical training, customers apps, and patents—are centered solely on the reproductive aspect, and fail to leverage these 99% non-conceptive menstrual cycles as health indicators,” writes Marie Gombert-Labedens, PhD, postdoctoral researcher at SRI International and lead author of the study.
The menstrual cycle is a complex process that is tightly connected to an individual’s health, influencing many physiological processes including metabolic and immune functions. Gombert-Labedens and colleagues believe that tracking the rhythms of the menstrual cycle can be a valuable yet underexplored diagnostic tool—similar to how cardiac rhythms are routinely monitored to diagnose a wide range of cardiovascular conditions or how circadian rhythms can indicate metabolic disorders.
However, more work needs to be done in menstrual health research to identify the most relevant metrics, their relationship with health conditions, and the extent to which individual variations may require a personalized approach. To aid the research community in this endeavor, the team developed the WAVES algorithm, which stands for ‘women’s health assessment through variability in endocrine-related signals.’
Using the WAVES algorithm, the researchers analyzed data from 5,674 menstrual cycles from 753 participants between 18 and 42 years old, including daily temperature measurements, vaginal secretions, age, reproductive history and sexual activity. Results showed that aging was associated with measurable changes in the menstrual cycle, including higher average temperatures, shorter cycles, and a decrease in regularity across multiple metrics.
Gombert-Labedens and colleagues also looked at individual variability across all metrics, as menstrual cycles are known to vary widely from person to person both in terms of length and regularity. “Although the menstrual cycle is typically described as 28 days long, research based on large datasets indicate that this is more the exception than the rule, as only 12.4% of individuals present 28-day cycles,” she stated.
Their analysis revealed that each participant showed individual patterns concerning mean body temperature across cycles, minimum and maximum temperature measurements, and the duration of both the full cycle and its phases. “These findings suggest that, across cycles from the same individual, each person has their own temperature baseline measurement around which the menstrual fluctuations are organized and are highly stable,” noted Gombert-Labedens.
As an open-source platform, the WAVES algorithm is now available to researchers worldwide studying menstrual cycle patterns, helping them parse through large amounts of data to identify relevant biomarkers associated with health, disease, and treatment response.
“The menstrual cycle is a rich yet underused source of physiological information,” Gombert-Labedens concludes. “This work suggests that the WAVES algorithm can be used for advancing digital biomarker discovery, and highlights the relevance of a personalized approach in the development of next-generation tools for women’s health.”
The post Open-Source Algorithm Advances Precision Menstrual Health Beyond Fertility appeared first on Inside Precision Medicine.
Oncology’s Next AI Battleground: Instant Clinical and Commercial Insight
Across the oncology pharmaceutical industry, the bar for precision is constantly being raised. Cancer drug development has become increasingly biomarker-driven, trial populations are narrowing, and the cost of identifying eligible patients for studies continues to rise. At the same time, life sciences organizations are under growing pressure to generate real-world evidence (RWE) faster for commercialization strategies as well as regulators and payers.
The race to operationalize artificial intelligence (AI) across oncology research has entered a new phase. After years of building massive catalogs of real-world data (RWD) from electronic health records (EHRs), molecular testing, and longitudinal patient outcomes, healthcare technology companies are now competing to transform those datasets into interactive intelligence systems capable of answering complex clinical and commercial questions in real time.
That convergence has fueled a wave of oncology AI platform development from companies including SOPHiA GENETICS, Ontada, COTA Healthcare, and now Flatiron Health. “As oncology becomes more complex, the ability to quickly identify the right patients and answer critical research questions is no longer a nice-to-have, it’s essential,” Kate Estep, chief product officer at Flatiron Health, told Inside Precision Medicine.
The move by Flatiron Health supports the continuing trend of data companies in oncology and across healthcare positioning themselves beyond simply aggregating clinical datasets toward creating AI-native research environments where clinicians, commercial strategists, and researchers can interact directly with data using natural language.
The need for speed
Historically, RWE generation has been labor-intensive. Pharmaceutical teams often rely on analysts or biostatistics groups to construct cohorts, validate inclusion criteria, and generate feasibility assessments—a process that can take days or weeks before a research question even begins to take shape. That workflow is increasingly incompatible with modern oncology development, where therapies are often targeted toward highly specific molecular subpopulations.
Cancer research may be uniquely suited for AI-native evidence generation systems. Compared with many therapeutic areas, oncology already produces unusually data-dense patient journeys involving pathology reports, genomic sequencing, imaging, biomarker testing, treatment lines, progression tracking, and survival endpoints. Oncology drug development is also increasingly dependent on identifying narrow molecular populations quickly and accurately. That complexity creates ideal conditions for conversational AI systems capable of navigating structured and unstructured clinical data simultaneously.
Flatiron Telescope attempts to address that bottleneck by giving users a conversational interface layered on top of Flatiron’s oncology-specific datasets. Researchers can describe inclusion and exclusion criteria in natural language, dynamically refine cohorts, and immediately view patient counts, attrition curves, treatment patterns, and survival analyses without writing code. “We were chatting with one of our early access partners last week, and this person was remarking, ‘I could answer my question in 30 minutes, and that would have taken me two days before waiting for my data team to come back to me,’” Estep said during a media briefing ahead of launch.
That acceleration may ultimately become the defining metric in the AI healthcare infrastructure market: not simply the size of a company’s dataset, but how quickly actionable insight can be extracted from it.
From data vendors to research platforms
But the challenge is not merely access to information. Trust and scientific validity remain central concerns. “One of the things our head of data science was sharing is that off-the-shelf models are roughly 60% accurate,” Estep said. “When built and trained with the clinical and scientific best practices that we have applied to model context because we have been asking cohort questions of our data for 15 years, that’s 90% plus accuracy.”
Those comments point toward an increasingly important divide in healthcare AI: the distinction between general-purpose AI models and clinically fine-tuned systems trained on domain-specific workflows. For companies like Flatiron, the competitive moat may ultimately come less from the underlying language models themselves and more from proprietary clinical context, curation methodologies, and validated evidence-generation pipelines.
The emergence of platforms like Telescope also reflects a broader transformation occurring across healthcare AI. The first generation of healthcare data companies focused primarily on aggregation, assembling electronic health record (EHR) data, claims data, genomic profiles, and imaging repositories into structured datasets. The second generation is now focused on orchestration: enabling users to interrogate those datasets continuously through AI-driven systems.
Flatiron is betting that domain specificity will matter more than generic AI capability. “Most people in the space either give you data, they give you analytics, or they give you a platform,” Estep said. “Very few cut across all three buckets.”
That positioning distinguishes Flatiron somewhat from competitors. Tempus AI has built a broad precision medicine tech ecosystem for both providers and life sciences companies. SOPHiA GENETICS has emphasized multimodal analytics and genomic interpretation. Ontada, backed by McKesson, combines oncology data assets with point-of-care tools and network analytics.
Flatiron, by contrast, is leaning heavily into its reputation for longitudinal oncology RWE and EHR-derived clinical depth. The company says Telescope is powered by more than 15 years of oncology-specific data infrastructure spanning over 4,700 providers and 1,600 clinical sites in the United States, representing approximately 40% of U.S. community oncology practices. Globally, the company now manages data from more than five million patient journeys across the U.S., U.K., Germany, and Japan.
That scale matters because oncology AI systems depend heavily on context-rich longitudinal data. Large language models alone are insufficient if the underlying clinical infrastructure lacks standardized outcomes, biomarker histories, treatment sequences, or progression events. “Flatiron has spent the last decade and a half building high-quality, oncology-specific real-world datasets,” Estep said. “Telescope really sits at the epicenter of that.”
Global oncology intelligence
Another major shift underway in oncology AI involves international interoperability. Historically, most RWE systems were fragmented geographically, with datasets built independently for different markets. But as pharmaceutical companies globalize clinical development programs, pressure is increasing to harmonize datasets across countries.
Flatiron says it is now building globally interoperable oncology datasets across the U.S., U.K., Germany, and Japan, beginning with prostate cancer data expected later this year. “We are ensuring that our datasets are interoperable from a global perspective,” Estep said. “Conclusions drawn on definitions of variables and data models in one market can easily be applied or explored in another.”
The long-term implications could be substantial. Globally harmonized oncology datasets would allow researchers to study treatment variation, biomarker prevalence, and outcomes across healthcare systems at a scale previously difficult to achieve. It may also help address longstanding concerns around representativeness in RWE generation. “Representativeness of a RWD is probably one of the single biggest requirements for us as we think about whether this dataset is considered reliable,” Estep said.
Perhaps the most important industry trend underlying Telescope’s launch is the democratization of advanced analytics. Historically, sophisticated oncology data analysis required teams of data scientists, epidemiologists, or biostatisticians. AI interfaces are beginning to collapse those barriers, enabling clinical operations leaders, medical affairs teams, and commercial strategists to interact directly with research-grade datasets. “There’s no coding required,” Estep said. “Any team member can use it, not just your data analysts.”
That shift could fundamentally change how oncology organizations make decisions, reducing delays between hypothesis generation and evidence generation while broadening access to sophisticated analytical capabilities across enterprise teams.
Whether Telescope ultimately becomes a dominant platform remains to be seen. But its launch reflects a broader reality now reshaping healthcare: in oncology, the future competitive advantage may belong not simply to companies with the most data but to those capable of turning clinical complexity into usable intelligence fastest.
The post Oncology’s Next AI Battleground: Instant Clinical and Commercial Insight appeared first on Inside Precision Medicine.
<![CDATA[Unpack gaming, gambling, social media, and exercise addiction, spotlighting comorbid anxiety, withdrawal signs, and harm-reduction treatment.]]>
Functional brain alterations associated with acupuncture for chronic pain: a scoping review of fMRI studies
BackgroundChronic pain (CP) is a public health challenge recognized as involving large-scale functional brain dysregulation. Acupuncture is widely used as a non-pharmacological intervention for CP, yet its central mechanisms remain incompletely understood. fMRI provides an approach for investigating acupuncture-related brain alterations in CP.MethodsEight databases were searched from inception to March 27, 2025 for fMRI studies investigating acupuncture’s central effects in CP. Eligible studies included randomized controlled trials and observational studies involving migraine, knee osteoarthritis, fibromyalgia, sciatica, chronic shoulder pain, chronic neck pain, cervical spondylosis, chronic low back pain, and lumbar disk herniation. Data on characteristics, acupuncture protocols, neuroimaging findings, and outcomes were extracted and narratively synthesized. Reporting quality of acupuncture interventions was assessed using STRICTA, risk of bias of randomized controlled trials using RoB 2, and methodological quality of observational studies using the Newcastle–Ottawa Scale.ResultsA total of 64 studies were included. CP was characterized by widespread functional brain abnormalities, mainly involving the default mode network, sensorimotor network, and pain- and emotion-related regions such as the anterior cingulate cortex, precuneus, insula, and thalamus. Across longitudinal and controlled analyses, acupuncture-related brain changes were most consistently reflected in altered functional connectivity, local neural synchrony, and regional spontaneous activity. Functional connectivity findings suggested a potentially ACC-centered circuit pattern, whereas regional homogeneity findings revealed bidirectional modulation across multiple brain regions. Comparative evidence further indicated that VA, SA, and EEA may engage partially overlapping but distinct neural processes. Reporting of core acupuncture protocol components was generally adequate, whereas methodological quality remained heterogeneous.ConclusionCurrent fMRI evidence suggests that CP involves large-scale network-level functional imbalance and that acupuncture may be associated with modulation of key abnormal nodes and circuits related to pain perception, sensory processing, and emotional regulation. The available evidence supports a cautious interpretation that acupuncture-related brain effects may predominantly reflect a state-dependent recalibration of dysregulated brain networks. Future studies should prioritize large-sample, multicenter, longitudinal, and multimodal designs, together with rigorous control settings and more rigorous, externally validated machine learning-based prediction studies, to better distinguish differential central effects across intervention conditions and advance mechanism-informed personalized acupuncture in CP management.
A compressed sensing neuromorphic processor for sparse signal classification
This paper presents a neuromorphic processing system integrating a compressed sensing spiking neural network (CSSNN) designed for sparse signal classification. The proposed CSSNN combines data coding, data compression, and SNN classification, enabling end-to-end optimization of network performance and model compression. Evaluated on the MNIST, N-MNIST, and DVS Gesture datasets, under uniform compression ratios (CRs) of 0.1, 0.05, 0.025, and 0.01, the proposed CSSNN consistently reduces the total number of network operations (OPs) by at least 80% compared with compressed learning methods using fixed Gaussian random matrix (GRM) sampling matrices, while maintaining minimal accuracy loss. A specialized CSSNN processor is designed based on a spike-driven processing flow. Validated on field-programmable gate arrays (FPGAs) and evaluated in the 40 nm CMOS process for application-specific integrated circuit (ASIC) design, this CSSNN processor achieves 96.12% classification accuracy with 8-bit fixed-point quantization on the MNIST dataset. The energy consumption of the ASIC is estimated to be 2.089 mW under a 1.1-V supply voltage and 100 MHz frequency.
Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables
We address the prediction of non-imaging variables based on structural brain connectivity derived from diffusion magnetic resonance images, using graph-based machine learning. We predict age and the mini-mental state examination (MMSE) score as examples of a demographic and a clinical variable. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity graph as input and processes the data separately through a parallel GCN mechanism with multiple branches. The novelty of our work lies in the model architecture, especially the Connectivity Attention Block, which learns an embedding representation of brain graphs while providing graph-level attention. We show experiments on publicly available datasets of PREVENT-AD and OASIS3. We validate our model by comparing it to existing methods and via ablations. This quantifies the degree to which the connectome varies depending on the task, which is important for improving our understanding of health and disease across the population. The proposed model generally demonstrates higher performance especially for age prediction compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning.