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Breaking news in the healthcare AI

Google's Health AI Updates: The Check Up 2025

At its recent "The Check Up 2025" event, Google unveiled several AI-powered healthcare innovations. Here's an objective summary of the key developments:

Search and Information Access

• AI Overviews Enhancement: Google improved its AI Overviews for health topics using Gemini models to provide more clinically accurate information across thousands of health conditions. These are now available in multiple languages, including Spanish, Portuguese, and Japanese.

• "What People Suggest" Feature: A new mobile feature in the U.S. that uses AI to aggregate patient experiences from online discussions, allowing users to see how others manage similar health conditions.

Data Management and Integration

• Medical Records APIs: Launched in Health Connect, these APIs enable apps to read and write medical information (allergies, medications, immunizations, lab results) using the FHIR standard format. The system supports over 50 data types while storing information locally on users' devices.

Wearable Technology

• Loss of Pulse Detection: This FDA-cleared feature for Pixel Watch 3 can detect when a user experiences a loss of pulse from events like cardiac arrest and automatically call emergency services if the user is unresponsive. It's scheduled to roll out in the U.S. by the end of March 2025.

AI for Medical Research

• AlphaFold Expansion: Google's protein structure prediction system has now modeled over 200 million proteins, supporting research on malaria and tuberculosis.

• TxGemma: An open AI model designed to accelerate drug discovery by predicting the safety and efficacy of potential therapies.

• AI Co-scientist: A multi-agent system powered by Gemini 2.0 that assists researchers with literature reviews and hypothesis development.

Healthcare Delivery Improvements

• Nurse Handoff Pilot: In partnership with HCA Healthcare, Google's AI reduced nursing handoff times from 90 minutes to 20 minutes.

• Vertex AI Search for Healthcare: Recently enhanced with Visual Q&A capabilities that can analyze tables, charts, and diagrams, helping clinicians access comprehensive patient information.

These developments represent significant steps in applying AI to healthcare challenges, though their real-world impact and integration into clinical workflows remain to be thoroughly evaluated

🧑🏼‍🔬 Bench to Bedside👨🏽‍🔬

Developments in healthcare AI research and innovations

Machine Learning in Celiac Disease Diagnosis

Celiac disease, an autoimmune condition, affects approximately 1 in every 100 individuals, with a wide range of presentation of symptoms like stomach cramps, diarrhea, skin rashes, weight loss, fatigue, and anemia, leading to diagnostic difficulties, with only about 30% of cases properly diagnosed. The use of AI in the diagnosis of celiac disease has been a major breakthrough for advanced medical sciences.

Key findings of this paper

Duodenal biopsy is the current gold standard for diagnosing celiac disease. Being a subjective finding, there is disagreement between pathologists in more than 20% of cases. Cambridge researchers developed an AI algorithm capable of diagnosing celiac disease with 97% accuracy, matching the precision of experienced pathologists. The AI was trained on nearly 3,400 biopsy images with 95% sensitivity & 98% specificity. It’s the first AI tool to match pathologist-level accuracy for diagnosing celiac disease. The AI allows for the earlier detection of celiac disease, potentially up to four years earlier than traditional methods, leading to improved patient outcomes and faster healing, and potentially decreasing morbidity as well as mortality.

Outcomes of Use of AI in Celiac Disease

• AI improves diagnostic accuracy and increases the accuracy of diagnosis.

• AI can forecast the risk with meticulous health record analysis and help foster preventative strategies.

• AI can automate administrative duties and help healthcare providers invest more time in prioritizing patient care and cure.

• AI customizes therapeutic plans, optimizing outcomes for individual patients.

🧑🏽‍⚕️ AI in Clinic 🏥

Developments in healthcare AI research and innovations

AI Agents vs Chatbots: Understanding the Differences

Though they share some overlapping capabilities, AI agents and chatbots represent two distinct approaches to conversational AI technology. While both interact with users through text-based interfaces, they differ significantly in their design, capabilities, and applications.

What Are Chatbots?

Chatbots are AI-powered conversation systems designed primarily for specific, structured interactions. They excel at handling routine customer service inquiries and providing immediate assistance.

Key Characteristics of Chatbots:

• 24/7 Availability: Chatbots can operate continuously without breaks, making them ideal for global customer bases across different time zones.

• Multilingual Support: They can communicate in multiple languages, eliminating the need for language-specific human representatives.

• High Volume Handling: A single chatbot can manage numerous inquiries simultaneously.

• Structured Responses: Chatbots typically follow predetermined conversation flows and provide templated answers

• Self-Service Model: They enable users to get immediate assistance without human intervention.

Popular chatbots include ChatGPT, Resolution Bot, Ada, and Gorjas, each with specific strengths in customer service applications.

What Are AI Agents?

AI agents represent a more advanced evolution of conversational AI, functioning as comprehensive assistants with greater autonomy and reasoning capabilities.

Key Characteristics of AI Agents:

• Real-Time Information Retrieval: AI agents like Perplexity can search the web in real-time, pulling data from multiple sources.

• Source Citation: They often provide citations for information, increasing transparency and credibility.

• Reasoning Capabilities: Advanced AI agents can synthesize information and analyze beyond simple retrieval.

• Contextual Understanding: They maintain conversation context and can follow complex threads

• Multimodal Capabilities: Some AI agents can process and generate different types of content, including text, images, and code.

Key Differences Between AI Agents and Chatbots

1. Information Access and Processing

Chatbots typically rely on pre-trained knowledge bases with limited or no real-time information access. They excel at providing consistent answers to common questions but may struggle with novel or complex queries.

AI Agents actively search the internet and other data sources to retrieve current information. This allows them to provide up-to-date, research-backed responses with citations.

2. Reasoning and Analysis Capabilities

Chatbots are primarily designed for straightforward question-answering and task completion within defined parameters. They follow programmed conversation flows with limited deviation.

AI Agents demonstrate more sophisticated reasoning, synthesize information from multiple sources, and perform more profound analysis. They can handle more complex requests and adapt to user needs.

3. Use Case Optimization

Chatbots are optimized for:

• Customer service and support

• Answering FAQs

• Guided conversations

• Routine task automation

AI Agents are better suited for:

• In-depth research assistance

• Complex problem-solving

• Creative content generation

• Personalized recommendations based on data analysis

The Future Landscape

The distinction between chatbots and AI agents continues to blur as technology advances. Modern systems increasingly combine elements of both approaches, with traditional chatbots gaining more agent-like capabilities and AI agents becoming more conversationally fluent.

As these technologies evolve, we can expect to see more specialized applications where the strengths of each approach are leveraged for specific use cases, from customer service automation to complex research assistance and creative collaboration

🤖 Miscellaneous🤖

What else is trending in Health AI

Trending AI Tools in Healthcare

We hear about a new company or groundbreaking research in health AI almost every day. So, we cover here some of the trending Health AI Tools and products.

1. Ambient Listening Tools

These tools, like those powered by machine learning, are gaining traction for their ability to listen to patient-provider conversations in real time, extract key information, and generate clinical notes. This reduces the administrative burden on clinicians, allowing them to focus more on patients. A prominent example is Microsoft's Dragon Copilot, which listens to consultations, summarizes them, and automates clinical tasks, enhancing workflow efficiency. Microsoft Dragon Copilot (Nuance DAX), Abridge, DeepScribe are market leaders in this technology.

2. Medical Imaging AI

AI tools designed for analyzing X-rays, CT scans, and MRIs are widely adopted, with over 75% of FDA-approved AI tools in 2025 focusing on medical imaging. These tools excel at detecting conditions like strokes, tumors, and fractures faster and often more accurately than human eyes. ChestView, recently cleared by the FDA, enhances detection of abnormalities on chest X-rays while reducing reading time, exemplifying this trend. Aidoc.com Enlitic.com Qure.ai are some of the market leaders here which we have covered before.

3. AI-Powered EHR Solutions

• Epic with Cognitive Computing: Epic’s EHR system, enhanced with AI features like predictive analytics and smart summaries, is a dominant player in large hospital networks for its interoperability and data management.

• MAIGPT: An emerging tool that integrates with EHRs to provide intelligent patient summaries and predictive insights, gaining traction for its clinician-friendly interface.

• Cerner with AI Enhancements: Cerner’s EHR platform, augmented with AI for real-time analytics and decision support, is widely used in U.S. healthcare systems for its robust integration capabilities.

5. Mental Health Chatbots

• Woebot: One of the most used AI chatbots for mental health, offering CBT-based support for anxiety and depression. It’s popular among healthcare providers and patients for its accessibility via app.

• Wysa: Known for its evidence-based therapy tools and conversational AI, Wysa is widely used to manage stress and anxiety, often prescribed by clinicians or offered through health systems.