🚨 Pulse of Innovation 🚨

Breaking news in the healthcare AI

Back Flipping Humanoid Robot: Future of Rehab

The integration of humanoid robots into healthcare is rapidly advancing, promising to reshape patient care and medical procedures. While the field is still developing, the potential is vast, with companies like Boston Dynamics and Unitree Robotics leading the charge.

Boston Dynamics, renowned for its agile and dynamic robots like Atlas, is exploring healthcare applications by focusing on mobility and manipulation. Their robots, with their ability to navigate complex environments, could be crucial in assisting with patient transport, delivering supplies within hospitals, and aiding in emergency response scenarios. While not strictly healthcare-focused yet, their advancements in robotics are foundational for future medical applications.

Unitree Robotics, known for its affordable and versatile humanoid robots such as the H1, is also contributing to the field. Their robots, designed for human-like movement and interaction, could potentially assist with patient rehabilitation, provide companionship for elderly patients, and perform repetitive tasks in medical settings, freeing up healthcare professionals for more critical duties.

Real-world healthcare scenarios are already emerging. Robots are being used for:

• Remote Patient Monitoring: Robots equipped with sensors can monitor vital signs and transmit data to healthcare providers, enabling remote care for patients with chronic conditions.  

• Surgical Assistance: While not fully humanoid, robotic arms are used in minimally invasive surgeries, increasing precision and reducing recovery times. Future humanoid models could enhance this further. Da Vinci and CorPath are

• Patient Transport and Logistics: Robots are being deployed to transport medications, supplies, and even patients within hospitals, improving efficiency and reducing workload on staff.  

• Rehabilitation: Robots can assist patients with physical therapy, providing personalized exercises and monitoring progress.   As technology progresses, we can expect humanoid robots to play an increasingly vital role in healthcare, improving patient outcomes and revolutionizing medical practices.

These advancements will not only improve patient care but also enhance the efficiency and effectiveness of healthcare delivery systems worldwide. Right now we are excited to see robots performing back flips and side flips but soon these agile functions will be utilized in healthcare world particularly early adaptation in the rehabilitation care.

🧑🏼‍🔬 Bench to Bedside👨🏽‍🔬

Developments in healthcare AI research and innovations

Future Unleashed: Clinically Proving an AI Pathology Game-Changer for Metabolic dysfunction-associated steatohepatitis (MASH) Scoring

Obesity, Type 2-Diabetes or prediabetes, high level of bad cholesterol i.e. LDL and metabolic syndrome are the potential risk factor for development of Metabolic dysfunction-associated steatohepatitis (MASH). At present, liver related morbidity and mortality has escalated exponentially and MASH is the leading cause of it. It affects a significant portion of the global population i.e. 25% in 2024 which is estimated to increase more than 55% by 2040.Womens and young adolescents are the major group affected by it. As MASH treatment lacks a standardized FDA and/or EMA approved therapeutic interventions is the leading cause of liver transplants at present which will be a huge global economic burden.

Current challenges related to MASH: Manual liver biopsy scoring, the gold standard for MASH diagnosis and trials, suffers inter-reader and intra-reader variability, risking unreliable results, higher costs, and drug approval delays.

Need for AI: The study addresses the urgent need for a standardized, reproducible tool to improve the accuracy of histologic assessments in MASH clinical trials.

AI-based measurement of metabolic dysfunction-associated steatohepatitis(AIM-MASH) Development and Validation:

Tool Description: AIM-MASH, an AI pathology tool, helps pathologists score MASH features like steatosis, inflammation, ballooning, and fibrosis-on liver biopsy whole-slide images using convolutional and graph neural networks.

Validation Scope: This study, the largest multisite validation of AIM-MASH to date, includes about 13,000 reads from over 1,400 biopsies across four global MASH trials: REGENERATE, FALCON 1, FALCON 2, and Novo Nordisk’s semaglutide trials.

Methodology: AIM-MASH’s accuracy was validated against expert consensus in 1,481 cases, with pathologists using AI overlays to score features. It showed >85% consistency across scanners and sites, with strong heatmap accuracy (e.g., 0.87-0.97 true positives).

Results:

• The AI tool outperformed manual scoring for inflammation and ballooning, improved MASH resolution accuracy, and matched steatosis/fibrosis reads, with significant superiority (p < 0.0001).

• Hepatocellular ballooning’s lower success rate (0.87) suggests variability challenges in pathological interpretation, highlighting a need for further standardization.

Clinical Implications: AIM-MASH could enhance trial endpoint reliability, reduce reader variability, and accelerate MASH drug approvals like RESMETIROM, with potential future use in diagnostics pending further validation.

Peculiarity of this study:

• The study’s large scale, multisite design, and collaboration with regulatory bodies (FDA, EMA) lend credibility.

• The AI’s ability to outperform manual scoring in key areas addresses a critical bottleneck in MASH research. Limitations of study:

• The variability in hepatocellular ballooning assessment suggests that AI, while advanced, still relies on human-defined criteria that may need refinement.

• The lack of demographic data limits generalizability assessments.

Relevance of AIM-MASH and its future: This advancement could transform MASH clinical trials by reducing costs and improving outcome consistency, potentially benefiting patients awaiting effective treatments. Trailblazing firms such as PathAI are transforming the landscape of AI-enhanced Metabolic-dysfunction Associated Steatohepatitis (MASH) diagnosis through the development of groundbreaking AI-based measurement (AIM) tools. These state of the art technologies equip Clinical Research Networks (CRNs) with accurate MASLD Activity Score (MAS) component evaluations and fibrosis staging, setting the stage for remarkably sophisticated and efficient AI-supported diagnostic systems.

🧑🏽‍⚕️ AI in Clinic 🏥

Developments in healthcare AI research and innovations

PathAI: AI-Powered Pathology for Faster, Smarter Diagnoses

Revolutionizing Pathology with AI

PathAI is at the forefront of AI-driven pathology, aiming to enhance diagnostic accuracy and efficiency. By integrating AI into pathology workflows, it helps pathologists interpret medical images with greater precision, improving patient outcomes. The company collaborates with biopharma, labs, and clinicians to advance drug development and clinical diagnostics.

Key Offerings

PathAI’s AI-powered tools optimize pathology assessments in research and clinical settings:

• PathExplore – AI-driven tumor microenvironment analysis

• AIM-PD-L1 – Automated PD-L1 expression detection for immunotherapy decisions

• AIM-HER2 – AI-powered HER2 scoring with heatmap visualizations for breast cancer

• AISight™ – Digital pathology platform for case and image management

Recent Innovations

• NASH/MASH AI Development – In November 2023, PathAI launched an expert advisory network to guide AI-powered diagnostics for liver disease.

• Roche Collaboration – Partnered in February 2024 to expand AI-driven digital pathology for companion diagnostics.

Challenges & Limitations

• Data Variability – AI accuracy depends on diverse, high-quality datasets.

• Workflow Integration – Adapting AI to existing lab systems is complex.

• Regulatory & Ethical Concerns – Compliance and transparency remain key hurdles.

FDA Clearance & Impact

In August 2022, PathAI’s AISight Dx platform received FDA 510(k) clearance and CE Mark, allowing its use for primary diagnosis in clinical settings—an important step toward mainstream AI adoption in pathology.

With AI-driven insights, PathAI is accelerating precision medicine, making pathology faster, more consistent, and ultimately, more effective.

🤖 Patient First, AI Second🤖

Ethical and Regulatory Landscape of Healthcare AI

WHO Partners with Delft University to Champion Ethical AI in Healthcare

On March 6, 2025, the World Health Organization (WHO) designated the Digital Ethics Centre at Delft University of Technology in the Netherlands as a Collaborating Centre on Artificial Intelligence (AI) for Health Governance.

Background

The Digital Ethics Centre at Delft University has been at the forefront of integrating ethical considerations into digital technology design for over two decades. Their pioneering work in responsible innovation aligns with WHO’s commitment to ensuring that AI technologies in healthcare are developed and implemented ethically.

Expected Impact

This collaboration aims to:

• Advance Research: Focus on priority areas in AI ethics to guide global health policies.

• Provide Expert Guidance: Offer insights for developing frameworks that ensure AI benefits are harnessed responsibly.

• Facilitate Knowledge Sharing: Conduct workshops and training sessions to promote ethical AI practices worldwide.

By emphasizing patient-centric approaches, this partnership seeks to build trust in AI applications, ensuring they enhance healthcare delivery without compromising ethical standards.

As AI continues to evolve, collaborations like this are crucial to navigate its complexities, ensuring that technological advancements serve the best interests of patients globally.