Corti has launched FactsR, a real-time agentic reasoning system designed to enhance ambient documentation during clinical consultations by reducing extraneous content and minimizing the need for post-visit edits.

The system integrates with ambient AI tools and uses a recursive, fact-first architecture to identify, validate, and structure clinical information—such as symptoms, vitals, and medications—as conversations unfold. According to Corti, early tests show that FactsR reduces AI-generated note bloat by over 65%, helping clinicians produce more concise and relevant documentation.

Unlike traditional large language models retrofitted for healthcare, FactsR is powered by Corti’s recursive fact-first reasoning loop, a purpose-built engine designed to surface, validate, and structure clinical knowledge in real time as conversations unfold. The tool is delivered as a modular API, allowing developers to embed it directly into healthcare applications.

Why a Recursive Approach Matters

Many traditional ambient documentation tools process raw transcripts through general-purpose language models after a consultation ends, often producing lengthy summaries that clinicians spend up to three hours a week correcting.

FactsR takes a different approach, from passive summarization to active reasoning. Its workflow consists of four key stages:

1. Listen and Extract in Real Time: As the consultation unfolds, FactsR continuously identifies and surfaces structured clinical “facts”—such as symptoms, vitals, medications, and social history.
2. Vet and Refine with Specialized AI: Each fact is automatically reviewed and improved through an AI-driven feedback loop. If something is unclear, the system refines it until it is accurate, consistent, and ready to use.
3. Clinician-in-the-Loop: Clinicians can review, accept, or adjust facts as they go. Early adopters report far fewer post-visit edits and rarely need to add missing information after the consultation, according to Corti. 
4. Generate EHR-Ready Notes: Once the facts are finalized, the system assembles a concise summary that’s free from long, verbose summaries or irrelevant content.

Reported benefits include:

1. Less Screen Time: Early trials show that users spend minutes, not hours, on corrections
2. Better Patient Focus: Real‑time reasoning means decisions stay in the consultation, not in hindsight.
3. Audit‑Ready Transparency: Every fact carries a timestamp, confidence score, and link back to the conversation.
4. Healthcare AI that Delivers: Reduces general-purpose AI-driven “note bloat” by 65%.

The innovation behind FactsR has been published together with evaluation results on the public benchmark Primock57 dataset. The evaluation shows that FactsR increases clinical completeness by 13%, capturing significantly more of the relevant medical information compared to traditional ambient scribes, while reducing note bloat by over 65% with a clinician in the loop.

FactsR is offered today through a consumption‑based API with enterprise‑grade HIPAA and GDPR compliance. 

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A new 2025 Future Ready Healthcare Survey Report from Wolters Kluwer Health, conducted in partnership with the independent marketing research firm Ipsos, reveals that while healthcare professionals widely recognize the transformative potential of generative AI (GenAI), most organizations are not yet ready to harness its full value.

The survey identifies strong enthusiasm for using GenAI to address the current challenges of workforce shortages, burnout, high healthcare costs, and rising administrative burdens, as well as keen interest in leveraging GenAI to achieve the next level of innovation and efficiency across the enterprise.

However, the data also show a clear disconnect between what organizations say they want to achieve with GenAI and how prepared they are to deliver on that promise. For example, while 80% of respondents cited “optimizing workflows” as a top organizational goal, only 63% feel prepared to use GenAI to do so.

[RELATED READ: Why Health Systems Should Ensure BMETs Are Comfortable and Confident with AI]

“GenAI has the potential to be a powerful tool for supporting sustainability in healthcare organizations right now, as well as preparing them for a more efficient future,” says Greg Samios, CEO of Wolters Kluwer Health, in a release. “The challenge is developing a strategy that can both optimize the current state in a highly volatile environment and simultaneously equip organizations with the digital capabilities they need to remain competitive over the next several years. Right now, organizations are at risk of falling behind unless they take a more cohesive approach to making GenAI standardized, scalable, and impactful.”

Healthcare’s GenAI Aspirations Collide with Operational Gaps

Key Findings:

• Nurse staffing and workforce concerns are at the top of the priority list for health GenAI applications: 85% of respondents cited “recruiting/retaining nursing staff” as a top priority, while 76% identified “reducing clinician burnout” as a main concern.
• Leaders are focusing on the basics to keep the enterprise running: GenAI-driven technologies are likely to be part of the solution for longstanding challenges, such as addressing the burdens of prior authorizations (67%), electronic health record management (62%), cybersecurity preparedness (68%), and supporting telehealth/virtual care programs (65%).
• But clinical staff expect more from the GenAI revolution: In qualitative responses, participants said they understand and acknowledge the need for workflow optimization but also want to see innovative capabilities such as ambient listening, clinical decision support leveraging GenAI, and assistance with communication and documentation utilizing GenAI.
• Formal GenAI policies and guidance are scarce: Only 18% of respondents were aware of formal organizational policies governing GenAI use, and only 1 in 5 reported being required to take structured training.
• As a result, concerns about appropriate implementation persist: More than half (57%) believe that overreliance on GenAI may erode clinical decision-making skills, while 55% are concerned that lack of transparency around GenAI’s potential role in making diagnoses could contribute to unclear reasoning behind patient-facing decisions.

“To successfully integrate GenAI, organizations must recognize its current limitations, as well as anticipate its realistic evolution and the regulatory landscape,” says Peter Bonis, MD, chief medical officer at Wolters Kluwer Health, in a release. “It is also vital to select GenAI applications that align with both clinical and financial goals while fitting into existing workflows. Establishing robust and ongoing governance will be essential to succeed.”

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Clairity Inc, a digital health company advancing artificial intelligence (AI)-driven healthcare solutions, has received US Food and Drug Administration (FDA) De Novo authorization for Clairity Breast, a novel, image-based prognostic platform designed to predict five-year breast cancer risk from a routine screening mammogram. 

With this authorization, Clairity is planning to launch among leading health systems through 2025.

Clairity Breast analyzes subtle imaging features on screening mammograms that correlate with future breast cancer risk, making early risk prediction feasible based on a screening mammogram alone. The result is a validated five-year risk score delivered to healthcare providers through existing clinical infrastructures, supporting more personalized follow-up care.

“For more than 60 years, mammograms have saved lives by detecting early-stage cancers. Now, advancements in AI and computer vision can uncover hidden clues in the mammograms—invisible to the human eye—to help predict future risk,” says Connie Lehman, MD, PhD, founder of Clairity, who is also a breast imaging specialist at Mass General Brigham, in a release. “By delivering validated, equitable risk assessments, we can help expand access to life-saving early detection and prevention for women everywhere.”

Limitations of Traditional Risk Models

Each year, more than 2.3 million new cases of breast cancer are diagnosed worldwide, including over 370,000 cases in women in the United States. Early detection and risk reduction are powerful tools to save lives, but their most effective deployment depends on accurate risk assessment. Most risk assessment models rely heavily on age and family history to predict risk. 

However, 85% of women diagnosed with breast cancer have no family history, and nearly half have no identifiable risk factors. In addition, traditional risk models, built on data from predominantly European Caucasian women, have not generalized well to women of diverse racial and ethnic backgrounds.

“Personalized, risk-based screening is critical to improving breast cancer outcomes, and AI tools offer us the best opportunity to fulfill that potential,” says Robert A. Smith, PhD, senior vice president of early cancer detection science at the American Cancer Society, in a release. “By integrating AI models that assess individual risk, we can better identify women at higher risk, and those who may benefit from supplemental screening methods, such as MRI, improving early detection and more effective prevention strategies.”

Photo caption: Clinical workflow within Clairity Breast

Photo credit: Clairity

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Rad AI has secured an additional $8 million in Series C funding from four US health systems, advancing its push to scale generative AI tools across hospitals and clinics.

The investment—led by Advocate Health, Memorial Hermann Health System, Corewell Health, and Atlantic Health System—brings Rad AI’s total Series C funding to $68 million. These health systems are partnering with Rad AI to expand the use of generative AI across complex clinical environments, aiming to improve workflow efficiency, care quality, and patient outcomes at scale. 

Collectively, the organizations operate over 100 hospitals and serve millions of patients nationwide, providing real-world insights to help accelerate the development and deployment of AI-driven solutions in radiology and follow-up care.

“The impact of AI in healthcare is no longer theoretical—it’s happening right now at scale,” says Doktor Gurson, co-founder and CEO of Rad AI, in a release. “It’s an honor to collaborate with these leading health systems to expand that impact even further, building solutions that improve care for millions of patients.”

Rad AI’s Radiology Solutions

Rad AI’s solutions support providers responsible for nearly half of all imaging volume in the US, according to a release from the company, helping radiologists work faster, reduce dictation load, and improve care quality.

“Memorial Hermann invests in AI not just to adapt to technology, but to transform patient care and improve workflows,” says Feby Abraham, PhD, executive vice president and chief strategy officer for Memorial Hermann, in a release. “We are always seeking partners who can deliver measurable impact on outcomes, efficiency, and improving the patient experience, and Rad AI checks all those boxes.”

Rad AI delivers an integrated solution to multifaceted clinical challenges:

• Rad AI Impressions — a generative AI application in radiology that automates the impression section of reports, saving radiologists more than an hour per shift on average.
• Rad AI Reporting — designed to enhance the radiology workflow with features like Omni Unchanged, which pulls stable findings from prior reports with one phrase and cuts follow-up dictation time by 50% and spoken words by 90%. Omni Report enables radiologists to dictate naturally and auto-fills structured templates, up to doubling reporting speed and easing cognitive load.
• Rad AI Continuity — closes the loop on incidental findings by automatically tracking and coordinating follow-up care. Health systems using Continuity have improved follow-up exam completion rates from approximately 30% to over 75%, helping ensure critical findings don’t fall through the cracks.

“We are pleased to invest in a company that is at the forefront of developing technology to make the jobs of physicians more convenient and efficient,” says Christian Rische, vice president and managing director, Corewell Health Ventures, in a release. “The products of Rad AI will help lead to a reduced workload for providers and, most importantly, potential improvements in patient care.”

Photo caption: Rad AI reporting illustration

Photo credit: Rad AI

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BrightHeart announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its second device, B-Right Views, an artificial intelligence (AI)-powered tool that automatically detects the standard views required for second and third-trimester fetal heart ultrasound evaluations within routine anatomy scans. 

The software supports fetal heart exams by confirming when all recommended views are captured and documented, enabling exam completeness and consistency regardless of operator experience, according to a release from the company.

This marks the third clearance for the company. In November 2024, the company received its initial 510(k) clearance for BrightHeart’s B-Right Screen AI software, which flags structural markers that are suggestive of congenital heart defects to assist in their detection during second-trimester fetal ultrasounds. Earlier this month, the company received its second clearance for platform expansion, enabling clinicians to access the B-Right Screen AI feedback directly via the cart-side tablet.

Real-Time Feedback on Fetal Heart Documentation

With this latest clearance, BrightHeart offers clinicians an integrated solution that is designed to provide real-time feedback on fetal heart documentation while simultaneously flagging a comprehensive set of structural markers that may be suggestive of severe congenital heart defects. 

As part of this clearance, BrightHeart achieves approval for its Predetermined Change Control Plan. This plan allows BrightHeart to implement pre-authorized future improvements to the AI at the core of the device, without requiring separate FDA submissions. With this upfront alignment in place, BrightHeart says in a release that it is poised to deliver product enhancements efficiently.

“BrightHeart’s AI has the potential to offer immediate workflow benefits and measurable clinical value,” says Nathan Fox, MD, clinical professor in the Raquel and Jaime Gilinski Department of Obstetrics, Gynecology, and Reproductive Science at the Icahn School of Medicine at Mount Sinai, partner at Carnegie Imaging for Women, host of Healthful Woman Podcast, and co-author of “The Unexpected,” in a release.  “By providing real-time alerts, BrightHeart helps sonographers to identify and correct missing views during the exam, reducing the need for repeat scans while potentially boosting sonographer confidence and facilitating earlier triage of high-risk cases.”

BrightHeart is preparing for a limited market release.

Photo caption: Example output screen the B-Right AI Platform

Photo credit: BrightHeart

Royal Philips released its 10th annual Future Health Index (FHI) report, which indicates that artificial intelligence (AI) holds promise for transforming care delivery, but gaps in trust threaten to stall progress.

“The need to transform healthcare delivery has never been more urgent,” says Carla Goulart Peron, MD, chief medical officer at Philips, in a release. “In more than half of the 16 countries surveyed, patients are waiting nearly two months or more for specialist appointments, with waits in Canada and Spain extending to four months or longer. As healthcare systems face mounting pressures, AI is rapidly emerging as a powerful ally, offering unprecedented opportunities to transform care and overcome today’s toughest challenges.”

The FHI 2025 report reveals 33% of patients have experienced worsening health due to delays in seeing a doctor, and more than 1 in 4 end up in the hospital due to long wait times. “Cardiac patients face especially dangerous delays, with 31% being hospitalized before even seeing a specialist. Without urgent action, a projected shortfall of 11 million health workers by 2030 could leave millions without timely care,” Peron adds in a release.

Clinician Burnout and Data Burdens Call for Digital Relief

More than 75% of healthcare professionals report losing clinical time due to incomplete or inaccessible patient data, with one-third losing over 45 minutes per shift, adding up to 23 full days a year lost by each professional. 

“These inefficiencies amplify stress on already understaffed teams and contribute to burnout,” says Gretchen Brown, RN, vice president and chief nursing information officer at Stanford Health Care, in a release. “Recognizing this, as clinicians, we see AI as a solution and understand that delayed adoption can also carry major risks.”   

Of the nearly 2,000 healthcare professionals surveyed, if AI is not implemented:

• 46% fear missed opportunities for early diagnosis and intervention
• 46% cite growing burnout from non-clinical tasks
• 42% worry about an expanding patient backlog

Trust Gaps Remain the Biggest Barrier to Widespread AI Adoption

While clinicians are generally optimistic, the FHI 2025 report highlights a significant trust gap with patients—34% more clinicians see AI’s benefits than patients do, with optimism especially lower among patients aged 45 and older. Even among clinicians, skepticism remains: 69% are involved in AI and digital technology development, but only 38% believe these tools meet real-world needs. 

Concerns around accountability persist, with over 75% unclear about liability for AI-driven errors. Data bias is another major worry, as it risks deepening healthcare disparities if left unaddressed. “To build trust with clinicians, we need education, transparency in decision-making, rigorous validation of models, and the involvement of healthcare professionals in every step of the process,” Brown adds. 

The Path Forward: Human-Centric AI Integration

Patients want AI to work safely and effectively, reducing errors, improving outcomes, and enabling more personalized, compassionate care. Clinicians say trust hinges on clear legal and ethical standards, strong scientific validation, and continuous oversight. As AI reshapes healthcare, building trust is essential to delivering life-saving innovation faster and at scale.

“To realize the full potential of AI, regulatory frameworks must evolve to balance rapid innovation with robust safeguards to ensure patient safety and foster trust among clinicians,” says Shez Partovi, chief innovation officer at Philips, in a release. “By 2030, AI could transform healthcare by automating administrative tasks, potentially doubling patient capacity as AI agents assist, learn, and adapt alongside clinicians. To that end, we must design AI with people at the center—built in collaboration with clinicians, focused on safety, fairness, and representation—to earn trust and deliver real impact in patient care.”

The FHI global survey analyzes the priorities and perspectives of healthcare professionals and patients across multiple countries. The FHI 2025 investigates how innovative technologies, particularly AI, can empower healthcare professionals to deliver better care to more people.

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GE HealthCare announced the launch of CleaRecon DL, technology powered by a deep-learning algorithm, to improve the quality of cone-beam computed tomography (CBCT) images. 

This artificial intelligence (AI)-driven solution is designed to remove streak artifacts caused by the pulsatile nature of blood flow in the arteries and changes in the distribution of contrast during CBCT acquisitions in liver, prostate, neuro, and endovascular aortic repair procedures. CleaRecon DL recently received US Food and Drug Administration 510(k) clearance and CE mark and will be available for use on the Allia platform. 

CBCT is used in interventional suites to provide cross-sectional imaging during procedures. However, the quality of CBCT reconstructed images may be diminished due to artifacts resulting from vessels’ pulsatility, which can reduce image clarity and accuracy. These limitations can impact the confidence in CBCT image interpretation and its adoption in routine clinical practice. Despite these challenges, CBCT remains crucial in interventional procedures for its ability to provide comprehensive visualization of anatomical structures and may enhance procedural accuracy. 

“The introduction of CleaRecon DL represents a leap forward in the interventional suite and for the advancement of CBCT. By improving image quality and reducing artifacts, this technology can empower clinicians to perform procedures with greater precision and confidence,” says Arnaud Marie, general manager, interventional solutions at GE HealthCare, in a release. “This solution builds on our portfolio of tools aimed at improving the user experience and workflow efficiency, enabling clinicians to deliver more accurate and effective interventions for enhanced patient outcomes.” 

Enhancing Imaging With AI Tech

Deep learning is an AI technology that has become the state-of-the-art machine learning technique for image processing and is trained to output data and perform specific tasks. It is based on population representative data collection and thorough tests with clinical domain experts. CleaRecon DL harnesses deep-learning algorithms designed to provide clearer and more accurate imaging, enabling healthcare professionals to make better-informed decisions and improve their patient care. 

During clinical validation testing, a recent survey noted that in 98% of cases, CBCT images reconstructed with CleaRecon DL are clearer than conventional CBCT images. This technology was also shown to improve CBCT image interpretation confidence in 94% of cases.

“CleaRecon DL takes CBCT to the next level, enabling clinicians to confidently use CBCT on patients with tools that help us provide the highest quality imaging and treatment across a wide range of clinical scenarios,” says Charles Nutting, DO, FSIR, interventional radiologist at Image Guided Therapy in Denver, Colorado, in a release. “This advancement improves our ability to perform precise interventions, with less manipulation of the image and eliminates artifacts that have historically hindered image clarity, ultimately helping improve the care clinicians can provide to patients.” 

CleaRecon DL is available in the United States and the European Union. CleaRecon DL on the Allia platform will be showcased at the Global Embolization Symposium & Technologies 2025 Annual Meeting taking place on May 15-18, 2025, in New York.

Photo caption: CleaRecon DL

Photo credit: GE HealthCare

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By Dev Nag 

Artificial intelligence (AI) isn’t a sci-fi episode or something out of an Aldous Huxley novel. It has been here since the 1950s, and a version of AI has been in medicine since the 1970s. AI, as we know it today—a language-learning machine—has been slowly integrated into many healthcare practices, and the COVID-19 pandemic accelerated this growth, thanks to telemedicine and advanced AI chatbots.  

AI is already changing the way healthcare professionals interpret scans, diagnose illnesses, and plan treatments, especially in radiology. A 2024 study found that over half of radiologists (52%) actively embrace AI in their practice, while 93% view it favorably overall, signaling broad professional support for its integration into clinical imaging.¹

These tools are not experimental; they’re becoming integral to daily clinical workflows, improving outcomes and efficiency. Another study found that AI-assisted radiologists were 12% more accurate in identifying breast cancer compared to unaided professionals.²

With radiology departments processing millions of scans each year, even incremental improvements can have a wide-ranging impact on diagnosis and patient care.

A Second Set of Eyes, Only Sharper

In clinical practice, subtle anomalies can often be the difference between catching a disease early and missing it entirely. AI tools, trained on thousands or even millions of imaging datasets, can spot patterns that human eyes might overlook. These algorithms don’t tire; in fact, they improve over time as they view more images and continue to learn and identify patterns.

This doesn‘t mean radiologists are out of a job; it enhances the radiologist’s judgments, offering another layer of analysis that improves confidence in diagnoses.

Take, for example, lung cancer detection. Traditional CT scans may reveal tiny nodules, but distinguishing between benign and malignant growths isn’t always straightforward. AI models can detect changes in size, shape, and texture to help radiologists determine whether further testing is needed.

It’s not about replacing clinicians—it’s about empowering their decision-making.

Faster Results When Every Minute Counts

Speed is critical in many clinical settings, particularly in trauma, stroke, and emergency care. AI significantly reduces the time required to process and interpret imaging data. Tasks that once took hours can now be completed in minutes, helping healthcare teams make informed decisions faster.

Hospitals using AI-based tools for stroke triage, for instance, can detect hemorrhages or blockages in brain scans almost immediately. That kind of acceleration directly supports earlier treatments, which can preserve neurological function and improve recovery outcomes.

Personalized Imaging for Personalized Care

AI is also pushing the boundaries of personalized medicine. By analyzing a patient’s unique anatomy and pathology, AI models enable clinicians to design treatment plans tailored to the individual, rather than relying on generalized protocols.

In oncology, for example, AI can help define tumor boundaries with remarkable accuracy, allowing surgeons to operate with more confidence. It can also track how a tumor responds to therapy, providing data that informs whether the treatment should continue, change, or be stopped altogether.

This level of granularity opens the door to care that is not only more precise but also more compassionate, responding to what each patient’s body actually needs.

Spotting Disease Before Symptoms Appear

Early detection is one of the most promising benefits of AI in medical imaging. Trained on extensive databases, AI can recognize disease markers and patterns that indicate early-stage illnesses, long before symptoms appear or traditional scans raise concerns.

In mammography, some AI systems have demonstrated the ability to detect signs of breast cancer up to two years earlier than standard reads. This leads to earlier diagnoses, more treatment options, and, in many cases, better outcomes.

The same holds true for chronic illnesses, such as diabetic retinopathy or cardiovascular disease, where early signs in retinal or cardiac imaging can now be detected with increasing accuracy, thanks to AI models.

Always Learning, Always Evolving

Unlike static software, AI systems improve over time. As they continue to process more data across diverse patient populations and imaging technologies, they evolve, becoming more nuanced, inclusive, and reliable.

This adaptive quality allows AI to grow alongside the field itself, staying relevant and responsive to the needs of both patients and practitioners. As regulatory frameworks mature, more providers are integrating these tools in a way that respects privacy, ensures equity, and aligns with ethical standards.

The Future of AI in Medical Imaging

The role of AI in medical imaging continues to expand rapidly. As healthcare systems around the world continue to digitize, the demand for smarter, faster diagnostic tools will only grow.

In the near future, we can expect AI models to become more specialized, trained not just on broad image categories but on highly specific conditions and population data. This could improve accuracy across age groups, ethnicities, and rare disease presentations that conventional datasets have historically underserved.

Another step forward is real-time diagnostics. AI is already accelerating image interpretation, but in the next few years, we may see systems that analyze scans in tandem with the imaging process itself, providing guidance or alerts while the scan is still in progress. This could be especially valuable in time-sensitive scenarios like trauma care or neonatal intensive care.

Integration with other data sources, such as electronic health records, genetic profiles, and wearable health data, is also on the horizon. As AI becomes more capable of synthesizing multiple data points, it could help form a more complete picture of a patient’s health, turning imaging from a standalone step into part of a continuous, personalized feedback loop.

Regulatory bodies are also catching up. Frameworks that ensure safety, transparency, and equity in AI tools are evolving, and future approvals are likely to emphasize fairness and explainability just as much as accuracy.

One thing is certain: The future of medical imaging isn’t about replacing the radiologist. It’s about giving them better tools, deeper insights, and more time to focus on what matters most: patient care.

About the author: Dev Nag is the CEO/Founder of QueryPal. He was previously on the founding team at GLMX, an electronic securities trading platform in the money market, with over $3 trillion in daily balances. He was also CTO/founder at Wavefront (acquired by VMware) and a senior engineer at Google, where he helped develop the back-end for all financial processing of Google ad revenue. He previously served as the manager of business operations strategy at PayPal and also launched eBay’s private-label credit line in association with GE Financial. Dev received a dual-degree BS in mathematics and BA in psychology from Stanford. In conjunction with research teams at Stanford and UCSF, he has published six academic papers in medical informatics and mathematical biology.

References

1. Liu H, Ding N, Li X, et al. Artificial intelligence and radiologist burnout. JAMA Netw Open. 2024 Nov 4;7(11):e2448714. 
2. Lång K, Josefsson V, Larsson AM, et al. Artificial intelligence-supported screen reading versus standard double reading in the mammography screening with artificial intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023;24(8):936-44. 

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JMIR Publications invites submissions to a new theme issue titled “AI Applications in Biomedical Engineering” in its open-access journal JMIR Biomedical Engineering. The peer-reviewed journal is indexed in PubMed, PubMed Central, Scopus, DOAJ, Sherpa/Romeo, and EBSCO/EBSCO Essentials.  

AI is rapidly advancing biomedical engineering, with the potential to contribute to medical device development, personalized diagnostics or treatment, patient outcome prediction, or drug discovery. Specifically, it can assist in disease diagnosis and treatment optimization to predict patient prognoses. AI and machine learning applications can also enhance biological signal analysis and image processing in biomedical technologies, advancing fields such as brain-computer interfaces, neuroprosthetics, and medical imaging. 

This themed section aims to showcase current research on AI applications in biomedical engineering from engineers, clinicians, and industry experts.

Topics for this new section include, for example, AI applications for: 

• Developing, designing, or improving medical devices, systems, and products
• Augmenting biomedical processes (eg, diagnosis, drug delivery, disease management, or treatment)
• Enhancing medical imaging technologies or processing (eg, image segmentation, image synthesis, and image analysis)
• Developing intelligent prosthetics, artificial limbs, or biomechanical enhancements
• Addressing ethical issues related to the design and applications of AI in biomedical engineering

All submissions will undergo a rigorous peer-review process, and accepted articles will be published as part of the theme issue.

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SimonMed Imaging has announced a partnership with Lunit, a company specializing in artificial intelligence (AI)-driven cancer diagnostics and therapeutics, to bring AI-powered breast cancer detection to patients nationwide. 

SimonMed selected Lunit INSIGHT DBT, in combination with Volpara Analytics, to power its personalized breast cancer detection service, designed to provide patients with a precise, highly personalized, and data-driven screening experience.

“This is more than just an upgrade—it’s an important step forward for our patients’ health,” says Sean Raj, chief innovation officer at SimonMed Imaging and fellow of The Society of Breast Imaging, in a release. “We’ve evaluated a range of AI technologies, and Lunit’s performance, speed, and clinical validation stood above the rest. This partnership enables us to offer the most precise and personalized mammogram experience to date—and when paired with our board-certified breast radiologists, we believe it offers the most advanced mammogram available today.” 

Lunit INSIGHT DBT is trained on millions of images and engineered to detect even the most subtle signs of breast cancer, improving both sensitivity and specificity. Integrated into SimonMed’s national network of over 170 accredited centers and supported by more than 200 subspecialty-trained radiologists, this AI solution is designed to help ensure earlier detection, faster turnaround times, and actionable insights for patients.

“This is one of the most advanced AI solutions we’ve ever adopted,” says John Simon, MD, founder and CEO of SimonMed Imaging, in a release. “It reflects our unwavering commitment to providing every patient we see with the most accurate, affordable, and proactive care possible.”

Photo caption: Lunit INSIGHT DBT

File photo

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