2 posts tagged with "ai"

Artificial Intelligence (AI) in healthcare has long captured our imagination, fueled by portrayals in movies and TV shows where robotic surgeons and virtual assistants are the norm. But beyond science fiction, AI has profoundly impacted real-world healthcare. As it has evolved over the years, AI has transformed from an ambitious idea into a powerful tool, reshaping how we deliver care.

The Three Waves of AI in Healthcare​

The journey of AI in healthcare can be categorized into three distinct waves:

1. The First Wave: Foundations and Early Exploration​

The first wave emerged in the aftermath of World War II with the introduction of Machine Learning and the coining of the term "Artificial Intelligence." During this period, foundational concepts like the Turing Test were developed, laying the groundwork for future advancements. While the early stages of AI were exploratory, they set the stage for what was to come.

Colossus was the world's first electronic programmable computer, at Bletchley Park in Bedfordshire. Bletchley Park Trust/Science & Society Picture Library via Chris Monk

2. The Second Wave: Expert Systems and Rule-Based AI​

The second wave, spanning the 1980s, was marked by the development of expert systems such as MYCIN and DXPlain. These systems aimed to mimic human decision-making using large sets of rules to provide medical recommendations. However, their potential was limited by the complexity of managing thousands of interacting rules and the constraints of the data available at the time, which primarily came from textbooks and human experts.

DXPlain, one of the pioneering expert systems in healthcare, aimed to emulate human decision-making.

3. The Third Wave: AI Renaissance & ChatGPT​

The third and current wave of AI began in the 2010s, characterized by the introduction of deep learning and neural networks. This era has been driven by the availability of large datasets, advances in computing power (especially GPUs), and innovative architectures like transformers which gave AI the ability to understand bodies of text as a whole versus just sequences of words.

The "Attention is All You Need" paper introduced the transformer architecture, revolutionizing natural language processing.

The release of models like GPT-3 and ChatGPT has revolutionized natural language processing and catalyzed the development of AI-driven applications in healthcare, enabling conversational agents, predictive analytics, and personalized care. These same technologies are now being deployed to assist clinicians through prepopulating notes, automating repetitive tasks, and providing decision support.

Imaging, pathology, and radiology are some of the fields where AI is making significant inroads, helping to reduce the time and cost of diagnosis while improving accuracy. For example, AI can now analyze medical images with remarkable precision, often outperforming human experts in specific tasks like detecting diabetic retinopathy or analyzing radiology scans. They are also capable of operating in the background to find opportunistic diagnoses that may have been missed by human eyes. This concept is often overlooked and will become more important as costs continue to come down.

via Avicenna.AI a leading medical imaging AI company

One of the most exciting developments is AI’s ability to integrate multimodal data—combining text, images, biosensor data, and even genomic information to provide a more comprehensive understanding of a patient’s condition. This capability allows AI to go beyond simple image recognition and contribute to the overall diagnostic process, taking into account the patient’s medical history and up to date data.

The Da Vinci Surgical System is a prime example of how AI is transforming surgery (from their website)

While fully autonomous surgery may still be a decade away, the integration of AI into the surgical process is already enhancing precision and outcomes. The da Vinci Surgical System, which combines robotic surgery with AI, is a prime example of how technology is pushing the boundaries of what’s possible.

AI in Outreach and Care Delivery​

At CareLaunch, we recognize the transformative power of AI and are integrating advanced AI capabilities into our CRM platform to support healthcare providers. AI is a strong fit for our mission to streamline patient communication, optimize workflows, and provide actionable insights. For instance, our AI-powered features can predict patient needs based on their historical data, enabling proactive outreach through our CRM. This is particularly valuable in managing chronic conditions, where timely interventions can prevent complications and improve quality of life. At the core of any AI or healthcare based initiative is a strong data foundation. The CareLaunch platform leverages HL7 FHIR standards to ensure that data is interoperable and secure. This also allows for the integration of AI models and other third-party tools to enhance the platform’s capabilities.

Our CRM system takes the guesswork out of patient communication by automating outreach campaigns with data-driven precision. Whether it's reminding patients about upcoming appointments, sending post-visit follow-ups, or launching preventive care initiatives, CareLaunch ensures that every message is sent at the right time to the right patient. This automation reduces administrative burden on healthcare providers, allowing them to focus more on patient care while ensuring that no critical communication is overlooked.

The Mayo Clinic's use of AI in diagnostics offers a real-world example of how AI-driven insights can lead to better patient care, a vision we share and strive to achieve with our platform.

At CareLaunch, we’re redefining what’s possible in healthcare outreach and patient relationship management. Our AI-driven CRM platform is designed to help you deliver personalized care, streamline communications, and ultimately improve patient outcomes. Ready to see how CareLaunch can transform your practice? Schedule a demo today and discover how our innovative solutions can help you connect with your patients in more meaningful ways.

For more insights into how CareLaunch is leveraging AI to transform healthcare, visit our blog or explore our platform to see how we can help you enhance patient care and operational efficiency.

Meta’s Newest Model: Llama 3.1 - Surprises, Impressions, and Use Cases​

This week, Meta released their newest and most advanced open-source model yet, Llama 3.1. This announcement is special for a few reasons. Not only does this advanced model outperform OpenAI’s latest GPT-4o, but Meta has also released it as an “open model”, allowing everyone to build on top of the technology at no cost.

Let's break down some quick impressions, surprises, and interesting use cases.

Surprises:​

1. Model Size and Variants:

   • The size of the model is unprecedented at 405 billion parameters.
   • Meta also released smaller versions of the model, down to 8 billion parameters, which can be run locally on consumer-grade equipment.

2. Math Benchmark Performance:

   • Surprisingly, the 405 billion parameter model underperformed on math benchmarks, which is unexpected as Llama models are typically strong in this area.

3. Software Integration:

   • Llama 3.1 excels at integrating with other software, such as web browsers, enhancing its practical applications.

4. Responsible AI features:

   • On top of being open-source, Meta has implemented several safety measures, including Llama Guard (a multilingual safety model) and Prompt Guard (a prompt injection filter)

Impressions:​

1. Advancement in Open-Source Language Models:

   • Llama 3.1 is considered a significant advancement in open-source language models. The 405 billion parameter version performs exceptionally well on most benchmarks.

2. Free and Permissive License:

   • The model is free and has a permissive license, allowing for fine-tuning and customization, which is seen as a major advantage over closed-source models.

3. Performance of Smaller Versions:

   • Some users found the 8 billion parameter version to be less capable than its Llama 3 counterpart for certain tasks. This is an important consideration for those using tools like Ollama for running models locally.

Use Cases:​

1. Multilingual Dialogue:

   • Llama 3.1 is optimized for multilingual dialogue use cases, making it versatile for global and clinical applications.

2. Research and Development:

   • Meta is making it easier to train new models on Llama 3.1, which could lead to rapid advancements in AI research and development.
   • The model's advanced capabilities make it suitable for reviewing and summarizing medical research, which could aid healthcare professionals in staying up-to-date with the latest findings

3. Advanced reasoning for complex medical queries

   • Llama 3.1, especially the 405 billion parameter model, has strong reasoning capabilities that can be applied to answer complex medical questions or analyze patient data.

4. Conversational Patient Intake:

   • Groq has already demonstrated how Llama 3.1 can be used for conversational patient intake, showcasing its potential in healthcare applications.

5. Developing intelligent healthcare agents

   • Llama 3.1's enhanced tool use and function calling capabilities allow for the creation of complex AI agents that can automate sophisticated healthcare tasks and answer intricate medical queries

6. Customized healthcare applications

   • Healthcare organizations can fine-tune Llama 3.1 models with their proprietary medical data to create specialized AI systems for specific medical domains or use cases

Conclusion:​

The open nature of the model has sparked discussions about potential risks and the need for responsible AI development. Mark Zuckerberg argues that open models are inherently safer, as they can be scrutinized by the community, but others worry about the potential for misuse.

At CareLaunch, we see AI as another tool we can make available to our partners. Rapid advances are happening in the space, and we hope to democratize the availability of these technologies to care providers, both small and large. By integrating advanced models and technologies, we aim to enhance our offerings and support our clients in delivering exceptional care. The potential for creating HIPAA-compliant solutions and improving outreach aligns with our commitment to providing secure and efficient healthcare communication tools.