As part of the cooperation, BIOTRONIK will distribute Cardiomatics’ AI-powered ECG analysis solution alongside its net_ECG advanced ECG-Patch Holter Monitor in Germany. This combined offering will provide healthcare professionals with enhanced capabilities for precise cardiac monitoring and analysis.

Rafał Samborski, CEO of Cardiomatics, expressed his belief that their AI-based ECG analysis software will create great synergies with the BIOTRONIK ECG-Patch product portfolio and will nicely fit into the journey of cardiac patients.

The significance of this cooperation is underlined by the alarming statistics surrounding cardiovascular disease, the leading cause of global mortality. With approximately 17.9 million annual deaths attributed to cardiovascular complications, the imperative for improved early detection and management is clear.

Prof. Dr. David Duncker, Head of Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany, expressed his excitement about the potential of AI to help in the in the early detection of cardiac rhythm abnormalities.

Cardiomatics has been at the forefront of developing AI solutions to enhance diagnostic cardiology since 2018. The Cardiomatics platform has analyzed over 250 thousand long-term ECG signals and garnered the distinction of being the first in its category to receive EU-MDR certification in October 2023.

Oliver Rehermann, Director of Marketing, e-Health & Connected Cardiac Care, BIOTRONIK Germany, stated that by combining Cardiomatics´ cutting-edge AI-powered ECG analysis solution with their net_ECG advanced ECG-Patch Holter Monitor, they aim to revolutionize the early detection and management of cardiac events. This collaboration supports their commitment to providing innovative solutions that enhance patient care and outcomes in cardiovascular health.

The study, led by Dr. Nathan Houchens from the University of Michigan and Veterans Affairs Ann Arbor Healthcare System, involved a cross-sectional assessment of patient preferences for various visual backgrounds during virtual medical encounters. The institutional review boards of the University of Michigan and Veterans Affairs Ann Arbor Healthcare System approved the research, which included a diverse sample of adult patients who had completed in-person or virtual outpatient visits within the previous year. Notably, the study collected race and ethnicity data but did not report it to protect participant confidentiality.

Key findings

Key findings from the study revealed that the solid color background and other professional environments received similar mean composite scores, indicating patients’ overall comfort and perception of physicians’ qualities such as knowledge, trustworthiness, care, approachability, and professionalism. However, the physician’s office displaying diplomas garnered the highest rating across these domains, signifying its strong appeal to patients during telemedicine interactions.

The preference for traditional healthcare settings as the background for video visits was further highlighted, with two-Participants further highlighted their preference for traditional healthcare settings as the background for video visits, with two-thirds favoring such environments across various physician types. They notably favored the physician’s office displaying diplomas, while they significantly less preferred the bedroom and kitchen backgrounds.

The study’s findings hold significant implications for telemedicine practices, suggesting that patients may harbor specific preferences regarding the background environment used during virtual medical encounters. Notably, the visual backdrop can influence patient trust and satisfaction, highlighting the importance of nonverbal communication in telemedicine interactions.

Limitations and highlights

While the study emphasizes the potential impact of the visual environment on patient perceptions, it acknowledges certain limitations, such as low response rates for mailed surveys and the focus on specific institutions within a single geographic region, which may affect the generalizability of the findings. Nonetheless, the results provide valuable insights for healthcare systems aiming to optimize the telemedicine experience for patients.

As telemedicine continues to evolve as a vital component of healthcare delivery, the study’s emphasis on the impact of background environments on patient preferences underscores the importance of considering such factors to ensure patient-centered and effective telemedicine practices.

The study also highlights the need for further research and consideration of nonverbal communication elements to enhance the overall telemedicine experience for patients. This study’s findings may contribute to the refinement of telemedicine practices by prioritizing the creation of traditional office or examination room environments, aligning with patient preferences for the visual backdrop during virtual medical visits.

In conclusion, the research underscores the significance of patient preferences for telemedicine video backgrounds and emphasizes the need for healthcare systems to integrate these insights into telemedicine practice guidelines and protocols.

Founded in 2020 with a mission to advance women and children’s health worldwide through medical innovations, Sonio has developed innovative IT solutions and AI features specifically tailored for obstetrics and gynecology ultrasound procedures. One of its flagship offerings, Sonio Detect, an FDA 510(k)-cleared AI assistant, utilizes sophisticated deep learning networks to ensure real-time image enhancement and elevate clinical accuracy, ultimately improving the quality of patient care.

Following the successful acquisition, Sonio will operate as an independent entity based in France, maintaining its autonomy in commercial expansion while ensuring the compatibility of its solutions with ultrasound devices from various manufacturers. By harnessing Samsung Medison’s expertise in ultrasound systems and combining it with Sonio’s cutting-edge AI capabilities, the collaboration aims to introduce groundbreaking AI-enhanced workflows that will drive significant advancements in patient outcomes and the quality of prenatal care.

Yong Kwan Kim, CEO of Samsung Medison, expressed enthusiasm about the partnership, stating, “Through the acquisition of Sonio, Samsung Medison remains committed to leveraging technology to enhance people’s lives. This collaboration holds the potential to revolutionize prenatal ultrasound examinations with state-of-the-art AI technology and reporting functionalities.“

Meanwhile, Cécile Brosset, CEO of Sonio, highlighted the synergistic benefits of the acquisition, emphasizing the growth opportunities it presents for both companies. “The partnership with Samsung Medison marks an exciting chapter in our journey to advance medical reporting technology and diagnostic software on a global scale. Together, we aim to drive innovation and address healthcare needs, including in underserved regions,” said Brosset.

The completion of the acquisition is contingent upon regulatory approvals, including the green light from the French Ministry of the Economy and Finance. As both companies gear up to combine their expertise and resources, the collaboration between Samsung Medison and Sonio sets the stage for transformative developments in the field of obstetrics and gynecology, ultimately enhancing patient care and medical diagnostics worldwide.

Artificial intelligence (AI) is rapidly revolutionizing the field of healthcare, but its integration in surgery has been relatively nascent. However, a recent study published in Nature¹ explores the potential of AI in surgery, highlighting its multifaceted improvements in preoperative, intraoperative, and postoperative care.

Artificial Intelligence in surgery: the study

The study emphasizes the significant global burden of surgical procedures, with over 330 million performed annually. It also highlights the existing challenges in terms of access to surgery, postoperative complications, and mortality rates. With the potential to address these issues, AI offers a promising way forward.

Traditionally, minimally invasive surgery has been at the forefront of improving surgical outcomes. However, the study points out that data-driven methods, empowered by AI, will play an increasingly important role in advancing surgical care and patient outcomes.

Integration of novel AI-powered digital interventions in the intraoperative setting

AI applications in surgery currently mostly rely on unimodal deep learning techniques. But transformer models, a recent breakthrough in neural network architectures, show great promise in enabling multimodal AI and foundation models. These models have the potential to handle diverse data formats and optimize surgical care across all stages of the process.

The integration of AI in surgery encompasses a wide range of applications. These include clinical risk prediction, automation and computer vision in robotic surgery, intraoperative diagnostics, enhanced surgical training, postoperative monitoring through advanced sensors, resource management, and discharge planning, among others.

The study envisions AI as a key tool to improve various aspects of surgical care. Preoperatively, AI can aid in patient selection, diagnostics, and surgical planning. For example, reinforcement learning algorithms have shown promise in identifying and maximizing tumor tissue removal while minimizing damage to functional anatomical tissues during neurosurgery. Virtual reality combined with AI-based segmentation systems can also enhance preoperative planning by aiding in complex surgical cases.

AI and preoperative diagnosis: the outcomes

Accurate preoperative diagnosis is crucial in surgical practice, and AI can significantly contribute to this area. The study highlights the successful application of AI algorithms in extracting unstructured radiological report data to enhance breast cancer diagnostics. Additionally, three-dimensional convolutional neural networks have demonstrated high accuracy in detecting pancreatic ductal adenocarcinomas, enabling early intervention and treatment.

AI plays a pivotal role in clinical risk prediction as well. By accurately assessing patient risks, it can facilitate enhanced patient selection, informed consent, and shared decision-making. However, the study also emphasizes the need for further research to mitigate biases and improve the overall accuracy of clinical risk scores.

By integrating AI into surgical care, healthcare professionals can optimize surgical workflows, reduce complications, and improve postoperative monitoring. The study concludes with a call for the development of novel transformer models capable of integrating and analyzing vast amounts of data, paving the way for even greater advancements in surgical AI in the near future.

In conclusion, the study published in Nature highlights the immense potential of AI for enhancing patient outcomes and surgical care. With its applications in diagnosis, risk prediction, and surgical planning, AI is poised to revolutionize the field of surgery. As technology continues to advance, the integration of AI promises a new era of surgical innovation and improved patient care.

1 Varghese, C., Harrison, E.M., O’Grady, G. et al. Artificial intelligence in surgery. Nat Med (2024). https://doi.org/10.1038/s41591-024-02970-3

The DETECT-1 Study

The DETECT-1 study is a prospective multicenter study conducted in three university medical centers in the Netherlands. The study involved adult patients (aged 18 years or older) in whom short-lasting circulatory arrest was induced as part of routine practice.

The Innovation

The researchers developed an algorithm for automated circulatory arrest detection using wrist-derived photoplethysmography from patients with induced circulatory arrests. This innovative approach offers a potential solution to provide early help in cases of unwitnessed out-of-hospital cardiac arrest, which is associated with low survival chances due to the delayed activation of the emergency medical system.

The Results

The study enrolled 306 patients between March 14, 2022, and April 21, 2023, of which 291 patients were included in the data analysis. The results were impressive. In the development phase, the first, second, and third training sets yielded a sensitivity for circulatory arrest detection of 100%. In the validation phase, the sensitivity for circulatory arrest detection was 98%, with a positive predictive value of 90%.

The Impact

The DETECT-1 study represents a significant step forward in the field of cardiac arrest detection. The automated detection of induced circulatory arrests using wrist-derived photoplethysmography is feasible with good sensitivity and low false positives. This could potentially revolutionize the way we respond to cardiac arrests, especially those that occur out of hospital settings.

The study’s findings open up new possibilities for the use of wearable technology in healthcare, particularly in the early detection and response to cardiac arrest. This could ultimately lead to improved survival rates and outcomes for patients. The DETECT-1 study is indeed a beacon of hope in the fight against cardiac arrest.

Understanding the study

The study focused on evaluating an online, school-based program aimed at preventing mental health issues such as anxiety, depression, and substance misuse among Australian adolescents. The study spanned over 72 months and involved a cluster-randomized controlled trial.

Key Insights from the Study

The findings of the study are crucial, adding to the growing evidence supporting the implementation of online programs in schools to tackle mental health problems among adolescents. The main reasons for school and student dropout included lack of time to conduct the study, changes in teachers, and failure to return the informed consent form.

What does this mean?

The outcomes of this study have significant implications for adolescent mental health. It highlights the potential of online programs in schools to effectively address and prevent mental health issues such as anxiety, depression, and substance misuse among adolescents.

Wrapping Up

In conclusion, this research offers valuable insights into the effectiveness of a universal, school-based, online program for the prevention of mental health issues among Australian adolescents. The results highlight the potential of such programs for addressing and preventing mental health issues among adolescents. Further research is needed to understand the long-term impacts of these programs and their applicability in different contexts.

For more detailed information, please refer to the full study.

HapSense: wearable skin sensor

HapSense emerges as a patent-pending wearable skin sensor that aims to streamline the analysis of skincare and cosmetic products. Developed through a collaborative effort between the P&G Singapore Innovation Centre, NTU Singapore, and the Agency for Science, Technology, and Research (A*STAR), HapSense sets out to deliver precise, objective, and quantifiable measurements of tactile sensations. By doing so, it effectively eliminates the guesswork often associated with skincare product development, offering invaluable insights for formulators seeking to create products that resonate with consumers.

Resembling a sleek signet ring, HapSense is comfortably worn on the fingertip, making it both convenient and unobtrusive. Its primary function lies in capturing data on friction and pressure in near real-time, allowing scientists and researchers to gain a deeper understanding of the effects various skincare products exert on the skin. What sets HapSense apart is its remarkable efficiency, speeding up product analysis by up to 10 times when compared to traditional skin-testing panels. Furthermore, its cost-effectiveness ensures accessibility, offering a viable alternative to conventional methods that often come with hefty price tags.

While HapSense finds its initial application in skincare and cosmetics, its versatility transcends boundaries, finding relevance across diverse industries.

• Textile and Fabric Care: Manufacturers can leverage HapSense to enhance the quality and sensory experience of fabrics, catering to consumers’ growing demand for comfort and luxury.
• Hair Care: Precise measurements of tactile sensations facilitated by HapSense hold the potential to revolutionize hair care product development, ensuring optimal performance and user satisfaction.
• Baby Care and Feminine Care: In sensitive areas such as baby care and feminine hygiene, HapSense plays a crucial role in ensuring safety and comfort, underscoring its significance in promoting overall well-being.

Ring-type sensor from NTU Singapore

NTU Singapore introduces a ring-type sensor, offering a parallel solution for measuring skin feel. Worn on the fingertip akin to HapSense, this innovative device captures data on friction and pressure, providing precise, objective, and quantifiable measurements of tactile sensation. Its development represents a significant stride towards enhancing skincare product analysis, offering researchers a powerful tool to unravel the intricacies of product efficacy and user experience.

The ring-type sensor from NTU Singapore brings forth a host of advantages, including:

• Accelerated Analysis: With the capability to expedite skincare product analysis by up to 10 times, the ring-type sensor streamlines the research and development process, enabling faster iterations and improvements.
• AI-Driven Insights: By harnessing multi-year data on product effects, the ring-type sensor facilitates AI-driven big data analysis, unlocking valuable insights that pave the way for personalized skincare regimes tailored to individual needs and preferences.

Beyond its primary application in skincare, the ring-type sensor holds promise across a spectrum of industries, including baby care, feminine hygiene, and more. Its versatility underscores its potential to revolutionize product development and enhance user experiences across various domains.

Market Snapshot: Verily and L’Oréal

In a testament to the growing convergence of technology and skincare, Alphabet’s Verily partners with L’Oréal to delve deeper into dermatological health. Leveraging AI and sensor technologies, this collaboration seeks to develop digital tools for skincare, further cementing the significance of technological innovation in reshaping the skincare landscape.

In summary, HapSense and the ring-type sensor represent a paradigm shift in skincare product analysis, heralding a new era of precision and efficiency. These wearable devices not only empower scientists and researchers but also hold the potential to transform the way skincare products are developed, marketed, and experienced. As the skincare industry continues to evolve, embracing technological innovations such as HapSense and the ring-type sensor will be paramount to driving progress and meeting the ever-evolving needs of consumers worldwide.

Under the visionary leadership of CEO Stéphane Bancel, Moderna has embarked on an ambitious mission to infuse AI into every aspect of its business, from research and manufacturing to legal and commercial operations.

Driven by a commitment to maximizing patient impact, Moderna’s adoption of ChatGPT Enterprise is not just a technological upgrade but a strategic move towards operational excellence. Moderna ensures that every employee equips themselves with the skills to leverage AI effectively, leading to rapid adoption and proficiency through a comprehensive transformation program.

The success story begins with mChat, an internal AI chatbot tool that quickly gained traction among employees, setting the stage for the seamless integration of ChatGPT Enterprise. With its launch, Moderna witnessed a surge in productivity as employees harnessed the power of AI to create hundreds of innovative solutions tailored to their needs.

Key milestones include the deployment of 750 GPTs company-wide, with 40% of weekly active users actively creating GPTs and engaging in 120 ChatGPT Enterprise conversations per week on average.

Dose ID, a standout application, streamlines clinical trial development by automating data analysis and augmenting decision-making processes as a GPT pilot. Additionally, ChatGPT Enterprise has empowered legal and corporate brand teams by streamlining compliance processes and enhancing storytelling capabilities.

Moderna’s commitment to AI-driven innovation and OpenAI’s cutting-edge technology set the stage for continued advancements in mRNA medicines and a positive impact on patients’ lives.

Published in The Lancet Digital Health, the study represents a collaborative effort among experts from diverse regions, pooling data from an extensive cohort of 37,407 healthy individuals across 87 datasets spanning the globe. Through meticulous analysis and comparison of eight distinct algorithms, researchers embarked on a quest to identify the most effective approach for normative modeling of brain morphometric data.

Multivariate fractional polynomial regression (MFPR) emerged as the standout algorithm, boasting exceptional accuracy and stability across various age groups. Researchers underscored the significance of this breakthrough, highlighting its profound implications for both research and clinical practice.

The study revealed that covariates and sample size critically determine model efficacy. Optimized MFPR models, enriched with non-linear fractional polynomials of age and linear global neuroimaging metrics, remarkably captured deviations from typical brain structural changes throughout the lifespan.

Beyond their academic implications, the study’s findings hold profound promise for diagnostic and neuropsychiatric disorder comprehension. By providing a more nuanced understanding of normative brain morphology, researchers anticipate paving the way for enhanced diagnostic precision and personalized treatment strategies in the future.

The study’s impact is poised to reverberate widely within the scientific community, catalyzing further exploration and innovation in the neuroimaging and brain research domains. As researchers continue to refine and expand these methodologies, the potential for deeper insights into the complexities of the human brain remains vast.

In summary, the groundbreaking study represents a quantum leap forward in our quest to unravel the mysteries of the brain. With its far-reaching implications and transformative potential, it sets the stage for a new era of precision medicine and neuroscientific discovery.

This innovative platform integrates an extensive molecular model database, the Daewoong Advanced Virtual Database (DAVID), with an AI-targeted substance discovery system. DAISY is set to transform the drug development landscape by enabling the rapid discovery of compound substances and predicting drug properties with unprecedented efficiency.

About DAISY

DAISY represents the culmination of over 40 years of research and development by Daewoong Pharmaceutical. The system is poised to challenge global pharmaceutical giants in the realm of AI-driven drug development, reminiscent of the biblical tale where David triumphed over Goliath.

Key Components:

1. Daewoong Advanced Virtual Database (DAVID) To harness the power of AI, the globally available open-source compound substance data underwent essential preprocessing. DAVID, the result of this meticulous process, is a repository of 800 million preprocessed molecular models that serve as the foundation for AI-driven drug discovery.
2. AI-based Virtual Screening (AIVS): A crucial tool within DAISY, AIVS employs 3D modeling and artificial intelligence to discover active substances targeting specific proteins. The utilization of AIVS has led to significant reductions in development time, particularly in areas such as obesity, diabetes, anti-cancer drugs, and the development of protein degraders.

Efficiency gains and promising results

Daewoong Pharmaceutical reports a substantial decrease in development time by utilizing DAISY to identify active substances targeting specific proteins. The application of AI technology has demonstrated promising results in various therapeutic areas, including obesity, diabetes, and anti-cancer drugs. DAISY significantly improves the development of protein degraders by simplifying processes through the design of antibodies and evaluating stability at the same time.

Prospects for the future

The company envisions expanding the application of AI drug development systems across different stages of drug development, encompassing pre-clinical, clinical, and post-marketing phases. Daewoong aims to leverage AI technology to reduce the drug development period to seven years and costs to approximately 600 billion won ($450 million). The National Institute of Food and Drug Safety Evaluation reported that this signifies a revolutionary shift from the conventional 15-year drug development timeline and development costs ranging from 2 trillion to 3 trillion won.

Daewoong Pharmaceutical’s DAISY system signifies a paradigm shift in drug development, where the synergy of molecular databases and artificial intelligence accelerates the discovery of innovative drug candidates. As the company continues to pioneer advancements in AI-driven pharmaceuticals, the industry may witness a transformative era characterized by shorter development timelines, reduced costs, and increased success rates in drug discovery and commercialization.