In a remarkable leap forward for medical technology, a team of researchers led by Zou, X., Zhang, J., and Chan, C.P.Y. have unveiled a groundbreaking advancement in haptic feedback systems designed specifically for endoscopic teleoperation and remote palpation. Detailed in their recent publication in npj Flexible Electronics, this innovative approach employs dense elastomeric liquid metal microcoil-based interfaces to transform how surgeons and medical professionals interact with remote internal environments. The intersection of flexible electronics, smart materials, and precision control opens new vistas for minimally invasive procedures and telemedicine.

At the heart of this breakthrough lies the integration of liquid metal microcoils encapsulated within elastomeric substrates, forming a highly sensitive yet mechanically robust haptic interface. These microcoils are capable of generating precise electromagnetic fields which can be finely tuned to mimic various tactile sensations encountered during palpation of organic tissues. The elastomeric medium ensures that the device remains soft and compliant, paralleling the flexibility of biological tissues, a critical characteristic for interfacing with delicate anatomical structures during endoscopic maneuvers.

Traditionally, the lack of tactile feedback in endoscopic teleoperation has been a significant barrier limiting the surgeon’s ability to perform nuanced examinations and manipulations remotely. Without real-time haptic cues, operators must rely heavily on visual data, often constraining them to less precise interventions. The dense microcoil architecture introduced here circumvents this limitation, delivering graded force feedback that enhances situational awareness and operational dexterity. This certainly augurs well for improved patient outcomes and expanded applicability of robotic-assisted minimally invasive surgeries.

The engineering challenge of embedding electrically conductive liquid metals within stretchable matrices is particularly formidable due to factors such as metal leakage, durability under repeated mechanical deformation, and signal fidelity. By leveraging advanced microfabrication techniques, the research team has managed to arrange multiple liquid metal microcoils in closely packed networks, maintaining electrical continuity while allowing for substantial skin-like elasticity. The resulting elastomeric platform simultaneously exhibits excellent mechanical endurance and electromagnetic responsiveness, making it highly suitable for continuous use in clinical settings.

Further advancing the technology, the microcoils’ electromagnetic emissions can be dynamically modulated to emulate various textures and firmness levels characteristic of different tissue types. Thus, the interface can replicate sensations ranging from firm nodules to soft cystic formations, invaluable during diagnostic remote palpation. This capacity to differentiate subtle differences in tissue compliance could revolutionize tele-diagnostic methodologies and enable the virtual presence of expert clinicians regardless of their physical location.

Additionally, the researchers demonstrated that the compactness and biocompatibility of their design allow seamless integration with existing endoscopic instruments. Since these haptic interfaces are fabricated on flexible substrates that conform to complex geometries, retrofitting current devices for enhanced tactile perception becomes feasible without significant hardware overhaul. This compatibility introduces a pragmatic pathway for rapid clinical adoption and accelerates the translation from lab prototype to bedside tool.

Significantly, the device’s exceptional spatial resolution stems from the microcoil density that surpasses conventional haptic arrays by an order of magnitude. This density enables highly localized force feedback, empowering surgeons to discern minute anatomical features and manipulative pressures, akin to natural touch. Enhanced spatial acuity not only minimizes inadvertent tissue damage but also refines the precision of surgical gestures performed remotely, a vital enhancement for robotic endoscopy.

The durability tests conducted reveal that the elastomeric microcoil arrays withstand thousands of mechanical cycles without degradation in electrical or tactile function. This endurance is crucial for clinical reliability, as surgical procedures often extend over hours and necessitate consistent tactile feedback. The elastomer’s resistance to bodily fluids and sterilization processes further underscores its practicality for repeated use within sterile operating room environments.

Beyond endoscopy, the principles underpinning this technology have vast implications across medical robotics and wearable health devices. From remote rehabilitation to advanced prosthetics, densely packed liquid metal microcoil arrays embedded in soft elastomers could deliver highly intuitive haptic sensations that bridge the gap between man and machine. Their ability to accurately simulate touch and force presents a paradigm shift in direct human-machine interaction technologies.

Expanding the scope of teleoperation, this innovation paves the way for real-time remote palpation performed by specialists thousands of miles away. The combination of high-fidelity haptic feedback with tactile sensation mapping can enable remote diagnosis with unprecedented accuracy. This is particularly transformative for geographically isolated healthcare facilities, where access to expert practitioners is limited, enhancing healthcare equity on a global scale.

Further insights from the study highlight that the electromagnetic microcoils can operate efficiently with low power consumption, vital for portable and battery-operated surgical tools. Efficient energy usage not only extends operational time but reduces heat generation, an important factor in preserving tissue safety and user comfort. The smart material approach balances functionality with operational pragmatism, addressing key limitations in current haptic technologies.

From a materials science perspective, the marriage of liquid metal and elastomer illustrates a masterclass in multifunctional composite design. The liquid metal component, inherently conductive and deformable, harmonizes with the elastomer’s resilience to achieve electro-mechanical transduction without compromising softness. This synergy redefines the possibilities of flexible electronics by providing a multi-physics platform capable of sensing, actuating, and adapting simultaneously.

Another notable aspect lies in the scalability of the fabrication method. This enables mass production of the microcoil arrays with reproducible quality and design flexibility tailored to varied endoscopic devices. Scalability is essential for widespread clinical deployment and cost-effectiveness. By providing a reliable route for industrial manufacturing, the researchers have addressed one of the critical barriers to translating cutting-edge soft electronics into real-world medical accessories.

In conclusion, Zou et al.’s pioneering contribution in developing dense elastomeric liquid metal microcoil-based haptic interfaces represents a landmark achievement with far-reaching consequences for telemedicine and minimally invasive surgery. Their meticulous integration of fluid mechanics, materials engineering, and electromagnetic control offers a new standard for tactile interaction in remote medical procedures. This breakthrough invigorates the field of flexible electronics and sets a blueprint for next-generation human-machine interfaces enhancing surgical precision and patient care worldwide.

As the medical community faces the continuing challenge of providing expert care in increasingly complex and constrained environments, innovations such as these provide a beacon for safely extending human touch beyond physical boundaries. Dense elastomeric liquid metal microcoil haptics are poised to redefine surgical telepresence, promising a future where distance no longer limits the healing power of human touch.

Subject of Research: Dense elastomeric liquid metal microcoil-based haptic interfaces for improving tactile feedback in endoscopic teleoperation and remote palpation.

Article Title: Dense elastomeric liquid metal microcoil-based haptic interfaces for endoscopic teleoperation and remote palpation.

Article References:
Zou, X., Zhang, J., Chan, C.P.Y. et al. Dense elastomeric liquid metal microcoil-based haptic interfaces for endoscopic teleoperation and remote palpation. npj Flex Electron (2026). https://doi.org/10.1038/s41528-026-00608-7

Image Credits: AI Generated

In a groundbreaking advancement for artificial intelligence in healthcare, researchers at Mass General Brigham have unveiled BRIDGE, a comprehensive multilingual benchmark designed to critically assess how effectively large language models (LLMs) comprehend and interpret clinical patient-care text. Unlike existing AI evaluation metrics that rely primarily on structured and standardized medical exam questions, BRIDGE is engineered to engage with the multifaceted and complex language found in real-world clinical communications, including electronic health records (EHRs), case reports, and patient-doctor consultations, across nine different languages. This innovation represents a transformative step toward making AI tools more reliable and contextually aware in actual healthcare settings.

The existing paradigm in medical AI evaluation has predominantly centered on licensing exam questions composed in a controlled, formalized medical lexicon. These assessments, while rigorous, often fall short of reflecting the nuanced, variable, and sometimes ambiguous language used in actual clinical environments. The BRIDGE benchmark circumvents this limitation by employing authentic clinical texts, which capture the complexities and heterogeneity inherent to patient care dialogues, medical documentation, and clinical decision-making processes. This shift brings essential granularity and relevance to model performance metrics, providing clinicians and developers with more actionable insights.

The creators of BRIDGE demonstrated the stark contrast in performance between conventional exam-based evaluations and real-world clinical comprehension. For instance, the highest-performing LLM evaluated scored impressively on standard medical exams, achieving up to 92%. However, when subjected to BRIDGE’s rigorous clinical text test, the same model’s proficiency plummeted to only 44.8%. This significant disparity exposes considerable gaps in the AI’s ability to grasp the subtle clinical context, implicit meanings, and domain-specific language patterns prevalent in healthcare communications.

To validate the robustness and breadth of BRIDGE, the research team conducted a systematic performance evaluation of 95 distinct LLMs sourced from 59 different clinical AI initiatives. These models were subjected to a comprehensive battery of real-world clinical tasks encompassing the entire patient care continuum, ranging across 14 medical specialties. The tasks included patient triage, extraction of critical information from records, diagnostic reasoning, prognostic forecasting, and administrative functions such as billing code assignment. This extensive benchmarking provides a panoramic view of LLM capabilities and limitations in diverse clinical scenarios.

One of the more innovative features of BRIDGE is its public and continuously updated leaderboard hosted on the Hugging Face platform. This dynamic leaderboard catalogs the performance metrics of over 100 LLMs, enabling stakeholders including clinicians, health administrators, and AI developers to track comparative model efficacy in near real-time. The leaderboard thus fosters transparency and spurs iterative improvements by highlighting strengths and vulnerabilities within specific clinical tasks or language domains.

Another salient discovery made possible by BRIDGE is the identification of variability in AI performance across different medical specialties. Given that the benchmark corpus includes nine languages, the tool also illuminates disparities in model effectiveness when dealing with non-English clinical texts. This multilingual adaptability is particularly crucial as healthcare becomes more globally interconnected, underscoring the urgent need to develop culturally and linguistically sensitive AI applications that avoid exacerbating health inequities.

The scientific rigor of BRIDGE is underscored by its deep collaboration among experts spanning pharmacoepidemiology, pharmacoeconomics, clinical medicine, and computational modeling. The team includes senior authors such as Jie Yang, PhD, and Joshua Lin, MD, along with co-first authors Jiageng Wu and Bowen Gu, whose collective expertise was critical in ensuring the benchmark’s relevance and accuracy. Such interdisciplinary engagement is vital for bridging the gap between AI innovation and clinical applicability.

BRIDGE’s architecture and methodology leverage advanced computational simulation and modeling techniques, facilitating nuanced task designs that mimic real clinical workflows. This approach allows the benchmark to capture the dynamic and context-rich nature of clinical text interactions, incorporating elements like colloquial doctor-patient exchanges, complex diagnostic narratives, and procedural documentation. Consequently, BRIDGE functions as a high-fidelity proxy for real healthcare communication scenarios, offering a much-needed calibration tool for medical LLMs.

Funding generously provided by the Patient-Centered Outcomes Research Institute, the National Institutes of Health, and institutional scholarships reflects the strategic priority placed on refining AI’s role in healthcare delivery. Moreover, the rigorous conflict of interest disclosures and adherence to institutional compliance underscore the study’s commitment to transparency and ethical research standards. These factors enhance the credibility of BRIDGE as a benchmark tool destined to influence clinical AI development profoundly.

Importantly, BRIDGE is more than a passive evaluation tool—it is a catalyst for elevated AI design tailored to the clinical domain. By exposing the blind spots and differential performance across specialties and languages, it empowers AI developers to iterate more purposefully, embedding clinical nuance and real-world complexity into model training. This iterative feedback loop has the potential to accelerate the maturation of AI models from theoretical capabilities to practical clinical decision-support systems.

The release of BRIDGE is poised to address one of the persistent challenges in clinical AI—trust. Reliable understanding of patient-care language is paramount to fostering clinician confidence in AI-assisted diagnoses, prognoses, and patient management recommendations. The benchmark’s capacity to expose and rectify shortcomings before clinical deployment mitigates risks of errors caused by misinterpretation of nuanced clinical text, thereby safeguarding patient safety and improving care outcomes.

In closing, BRIDGE exemplifies a paradigm shift that acknowledges the inherent complexities of clinical language and seeks to elevate the fidelity of AI’s interpretative functions accordingly. As healthcare continues its digital transformation, integrating intelligent systems into everyday practice demands benchmarks as sophisticated and reflective as the environments they serve. BRIDGE sets a new gold standard in this endeavor, bridging the divide between cutting-edge AI performance and meaningful clinical utility.

Subject of Research: People

Article Title: BRIDGE: benchmarking large language models for understanding real-world clinical practice texts

News Publication Date: 17-Jun-2026

Web References:

• Mass General Brigham
• Nature Biomedical Engineering Article
• BRIDGE Medical Leaderboard

References: Wu, J. et al. “BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text” Nature Biomedical Engineering DOI: 10.1038/s41551-026-01719-2

Keywords: Artificial intelligence, machine learning, clinical medicine, large language models, electronic health records, multilingual AI, clinical text comprehension, medical AI benchmarking, healthcare AI, patient care AI

A Revolutionary Biomarker Emerges from CT Scans, Offering New Hope in Gastric Cancer Prognosis

In a groundbreaking study spearheaded by researchers at the State University of Campinas (UNICAMP) in São Paulo, Brazil, a novel biomarker has been identified that could transform prognostic assessment for gastric cancer patients. Gastric cancer—ranked as the fifth most prevalent cancer worldwide—has long posed a challenge in predicting disease progression accurately. This innovative marker, derived from routine computed tomography (CT) imaging data, integrates complex measurements of visceral fat and muscle radiodensity to stratify patient risk more effectively than traditional tumor staging alone.

The multidisciplinary team, drawing expertise from both the Faculty of Medical Sciences (FCM) and the Gleb Wataghin Institute of Physics (IFGW) at UNICAMP, embarked on this research with backing from multiple grants awarded by the São Paulo Research Foundation (FAPESP). Their efforts culminated in the development of what they have termed the Visceral Muscle Difference (VMD) marker—a composite variable that captures metabolic and inflammatory properties of patient body composition using quantitative imaging parameters.

Conventionally, gastric cancer prognosis centers around tumor staging, an approach focusing primarily on the characteristics and spread of the tumor itself. However, this research introduces a paradigm shift by emphasizing the patient’s overall physiological state—not just the malignancy. The investigative team, including Professor José Barreto and co-advisor Jun Takahashi, advocates a holistic perspective that scrutinizes how body composition influences cancer outcomes, highlighting that tailored treatment must address the patient’s systemic condition to improve survival.

Central to the study was the analysis of data collected over a decade from 461 patients treated for gastric cancer at UNICAMP. Researchers meticulously analyzed their CT scans, quantifying radiodensity values of visceral adipose tissue and skeletal muscle. Radiodensity, an indicator of tissue’s capacity to attenuate X-rays during CT scanning, holds clues to underlying biological processes such as inflammation and metabolic health—factors increasingly recognized as critical modifiers of cancer progression.

By integrating these radiodensity values into a single metric, the VMD marker captures the complex interplay between fat and muscle tissue states in cancer patients. Intriguingly, the research reveals an inverse prognostic relationship: elevated radiodensity in adipose tissue correlates with poorer outcomes, possibly signalling inflammatory activation within the fat stores, while higher muscle radiodensity aligns with better survival rates, reflecting preserved muscle quality.

Quantitative analysis demonstrated striking survival disparities based on VMD scores. Patients with elevated VMD—a signifier of detrimental body composition—experienced a median survival of just 13.8 months, starkly contrasted with 58.5 months for those exhibiting healthier VMD profiles. This prognostic ability surpasses traditional staging, offering oncologists a powerful tool to identify high-risk patients who may require intensified or alternative therapeutic approaches.

The robustness of the VMD marker is further enhanced by its design, which strategically utilizes the difference between fat and muscle radiodensity rather than relying on absolute values for each tissue. This approach mitigates variability introduced by different CT scanner calibrations or technical inconsistencies, ensuring more reliable clinical implementation across diverse healthcare settings.

Harnessing advanced artificial intelligence techniques, the team employed machine learning algorithms to sift through the extensive imaging and clinical data. Unlike traditional univariate analyses, this methodology enabled rapid testing of multiple radiodensity combinations, refining the marker until it achieved optimal prognostic precision. “Teaching the machine to align with expert clinical insight while scaling data analysis exponentially was key,” explains Takahashi.

The implications of VMD extend beyond prognostication. Integrating this biomarker into clinical workflows could revolutionize treatment decision-making by unveiling the patient’s metabolic and inflammatory status—critical determinants often overlooked in standard cancer care. Personalized treatment regimens could emerge whereby aggressive chemotherapy is selectively administered to those with high-risk VMD profiles, whereas patients with favorable metrics might avoid unnecessary toxicity post-surgery, fundamentally improving quality of life.

Despite these promising findings, researchers caution that the study’s retrospective nature necessitates validation in prospective, multicenter cohorts encompassing broader demographics. Ensuring reproducibility across different populations and clinical environments is essential before VMD can be fully incorporated into routine practice. Moreover, the potential to modify a patient’s body composition profile therapeutically remains an open question, with ongoing investigations exploring whether nutritional or metabolic interventions can positively impact prognosis.

This study situates itself firmly within the evolving landscape of precision oncology, where understanding the host’s systemic biology complements tumor biology to refine cancer management. By leveraging data from standard CT scans—already integral to patient assessment—the VMD marker offers a cost-effective, readily accessible addition to the oncologist’s toolkit without imposing extra procedural burdens on patients.

Early exploratory studies initiated by the team suggest that the predictive value of the VMD marker may extend to other cancer types, potentially heralding a universal biomarker of cancer-related frailty and inflammation. As these lines of research mature, clinicians may soon navigate cancer treatment armed with unprecedented insights into the intricate interplay between tumor and host, tailoring therapeutics with unparalleled precision.

The diligent efforts of the UNICAMP team, supported by FAPESP, exemplify how interdisciplinary collaboration, cutting-edge technology, and patient-centered philosophy can converge to solve complex medical challenges. Their work opens a new chapter in gastric cancer prognosis, where the narrative shifts from focusing solely on the tumor mass to embracing the multifaceted biological portrait of the patient as a whole—paving the way for a future where personalized medicine is truly realized.

Subject of Research: Biomarker development for prognosis in gastric cancer utilizing CT scan-derived body composition radiodensity variables

Article Title: Determination of a new gastric cancer mortality predictor based on body composition radiodensity variables

News Publication Date: March 21, 2026

Web References:

• https://www.fapesp.br/en
• https://www.agencia.fapesp.br/en
• DOI: 10.1016/j.clnesp.2026.103132

References:

• Original article published in Clinical Nutrition ESPEN, 2026

Image Credits: FCM-UNICAMP

Keywords:
Gastric cancer, biomarker, prognosis, radiodensity, visceral fat, muscle, CT scan, body composition, machine learning, personalized medicine, inflammation, metabolic state