The digital health space refers to the integration of technology and health care services to improve the overall quality of health care delivery. It encompasses a wide range of innovative and emerging technologies such as wearables, telehealth, artificial intelligence, mobile health, and electronic health records (EHRs). The digital health space offers numerous benefits such as improved patient outcomes, increased access to health care, reduced costs, and improved communication and collaboration between patients and health care providers. For example, patients can now monitor their vital signs such as blood pressure and glucose levels from home using wearable devices and share the data with their doctors in real-time. Telehealth technology allows patients to consult with their health care providers remotely without having to travel to the hospital, making health care more accessible, particularly in remote or rural areas. Artificial intelligence can be used to analyze vast amounts of patient data to identify patterns, predict outcomes, and provide personalized treatment recommendations. Overall, the digital health space is rapidly evolving, and the integration of technology in health

Tuesday, March 5, 2024

How Artificial Intelligence will add to the growth of PrecisionHealthLLM

Medicine today is imprecise. Among the top 20 drugs in the U.S., up to 80% of patients are non-responders. The goal of precision health is to provide the right intervention for the right people at the right time. The key to realize this dream is to develop a data-driven, learning system that can instantly incorporate new health information to optimize care delivery and accelerate biomedical discovery. In reality, however, the health ecosystem is mired in overwhelming unstructured data and excruciating manual processing. For example, in cancer, standard of care often fails, and clinical trials are the last hope. Yet less than 3% of patients can find a matching trial, whereas 40% of trial failures simply stem from insufficient recruitment. Discovery is painfully slow as a new drug may take billions of dollars and over a decade to develop.

In this tutorial, we will explore how large language models (LLMs) can serve as a universal structuring tool to democratize biomedical knowledge work and usher in an intelligence revolution in precision health. We first review background for precision health and give a broad overview of the AI revolution that culminated in the development of large language models, highlighting key technical innovations and prominent trends such as consolidation of AI methods across modalities. We then give an in-depth review of biomedical LLMs and precision health applications, with a particular focus on scaling real-world evidence generation and drug discovery. To conclude, we discuss key technical challenges (e.g., bias, hallucination, cost), societal ramifications (e.g., privacy, regulation), as well as exciting research frontiers such as prompt programming, knowledge distillation, multi-modal learning, causal discovery.

PrecisionHealthLLM: PrecisionHealthLLM

A non-exhaustive list of AI applications

Precision Health


              AI in health and medicine

Foundation models for generalist medical artificial intelligence

LLMs for Precision Health

GPT-4 in Medicine

Biomedical LLMs

LLMs for Real-World Evidence

LLMs for Drug Discovery

Application Challenges

Bias

Hallucinations

Research Frontiers

Prompt Programming

Retrieval-Augmented Generation (RAG)

Knowledge Distillation

Multi-modal learning

Causal Discovery

Thursday, February 22, 2024

HIPAA Breach Notification Letter -




HIPAA Breach Notification Letter - The Fox Group

ChatGPT had a high error rate for pediatric cases

Researchers found ChatGPT incorrectly diagnosed over 8 in 10 selected pediatric case studies, raising questions about some bots' suitability for helping doctors size up complex conditions.

The HEADLESS M.D.


The big picture: Large language models like OpenAI's ChatGPT are trained on massive amounts of internet data and can't discriminate between reliable and unreliable information, researchers at Cohen Children's Medical Center wrote.

  • They also lack real-time access to medical information, preventing them from staying updated on new research and health trends.

What they found: The chatbot misdiagnosed 72 of 100 cases selected and delivered too broad a diagnosis to be considered correct for another 11, the researchers wrote in JAMA Pediatrics.

  • It wasn't able to identify relationships like the one between autism and vitamin deficiencies, underscoring the continued importance of physicians' clinical experience.
  • However over half of the incorrect diagnoses (56.7%) belonged to the same organ system as the correct diagnosis, indicating more selective training of the AI is needed to get diagnostic accuracy up to snuff.
  • The study is thought to be the first to explore the accuracy of bots in entirely pediatric scenarios, which require the consideration of the patient's age alongside symptoms.

One takeaway is that physicians may need take a more active role in generating data sets for AI models to intentionally prepare them for medical functions — a process known as tuning.


ChatGPT had a high error rate for pediatric cases

9 Challenges for CMOs in 2024 | Insights | Traction

9 Challenges for CMOs in 2024 | Insights | Traction

Challenge #1: Picking the Right AI

According to theresanaiforthat.com, there are currently 10,861 AIs for 9,962 tasks and 4,847 jobs in their database. There are an estimated 57,000+ AI companies in the world today with at least 15,000 of them in the United States

No matter which way you cut it, that’s a lot.

But not all AIs are created equal. One of the biggest obstacles to AI adoption will be choice. Picking the best tools for the right use cases and using them in the right ways to get the best output is an opportunity for competitive advantage for savvy brands. While the pace of innovation means that what’s best today may not be best in a week.

Take a look at the output for the exact same prompt in MidJourney and Adobe Firefly and you’ll get dramatically different results.

Woman generated by MidJourney





Woman generated by Adobe Firefly



ChatGPT is great at writing emails or blog posts, but Pi.ai has a more natural human voice, Perplexity.ai offers sources to validate its responses, and Jasper.ai allows you to export copy for dozens of marketing formats at once.

CMOs should create a disciplined approach to identifying the most meaningful use cases, evaluating tools, documenting workflows, training teams to use them, driving the adoption of best practices among teams, and monitoring emerging innovations to continue to optimize performance.

Much of this waste comes from redundant platforms gathered over time. In a recent martech audit for one of our clients at Traction, we uncovered $3.5M in potential annual savings by consolidating vendors.

Another reason is that people either don’t know how to use the capabilities they have or don’t want to. In a conversation over drinks with a senior marketing director at a large brand, she shared with me. “We paid all this money for all this tech and no one uses it because it’s a pain in the ass.”


When choosing an A.I. analyze the  specific questions and roadblocks in your business