Ray Schroeder
UIS Professor Emeritus and UPCEA Sr. Fellow
These qualities are improving daily. Fortunately the advent of AI is incrementally addressing the historic male-bias in medical research that has stifled recognition of gender differences in conditions, treatments and delivery of most appropriate appropriate care.
Reasons include making diagnoses and learning more about lab results or treatments. Nearly all chatbot health users find this AI information helpful,
Key takeaways:
A quarter of Americans say they use AI chatbots to diagnose symptoms.
Similar shares use them to better understand information they may have gotten from healthcare providers, like to learn more about a doctor’s diagnosis or understand lab results.
Nearly all chatbot health users find this health information at least somewhat helpful.
But these users are split on whether they feel comfortable sharing personal health information with chatbots.
======
Presentation Outine
Examine exemplar AI impacts across the medical field
Identify ways to personally implement AI in your healthcare
Demonstrate some searches based on group input
Exemplar list of sites for Sept 9, 2026 presentation.
GRJ_e9naTNoY5EbCuyjuVd43mTFomBqo49h8mjFcPo/edit?usp=sharing
I provide a weekly report of research, clinical trials, and other new developments in the treatment of Glioblastoma and related quality of life issues for two patients - one, a woman in metro Boston and the other my brother in metro Seattle. Here is an example: This is completed automatically (as a form of Agentic AI) for me every Monday morning using GPT-5.6 Sol Advanced as part of my ChatGPT Plus account ($20/month) We will re-visit this with your suggestions at the end.
https://docs.google.com/document/d/11KD4WmiMzQyWRptwurB7OzKDx9e8oTUcObij2zX1WfQ/edit?usp=sharing
Ricardo Funke, the chief of surgery at Clinica Las Condes in Santiago, Chile, had a new assistant during a laparoscopic surgery on Monday - an autonomous artificial intelligence-guided camera that allowed him to carry out a gallbladder removal alone. The procedure combined magnetic surgical instruments with software that autonomously directs the surgical camera, tracking the surgeon’s tools and adjusting angles without a human assistant. "The camera was following me wherever I moved my hands and the whole process was excellent," Funke told Reuters after the surgery. "This camera lets us do the surgery alone, I did it alone with the robot." Companies, universities and research centers across the world have been developing AI-assisted tools to perform or assist in surgery.
Wharton professor Christian Terwiesch and two Wharton colleagues conducted an experiment to see whether people could tell the difference between Appiah’s advice or advice spit out by ChatGPT. In a blind study, even experienced professionals — including ethics professors and clergy — could not tell whether the AI-generated advice was more or less useful than the advice provided by Appiah. In fact, random participants preferred the AI-generated advice 59.6% of the time. An additional study found that participants who were initially resistant to taking advice from the chatbot became more accepting as they were shown the high quality of the AI reasoning.
https://knowledge.wharton.upenn.edu/article/would-you-trust-ai-for-ethical-advice/
A chatbot-delivered depression therapy was compared to a minimal level of bibliotherapy in a 16-week follow-up period. The therapy chatbot was able to provide depression intervention under cognitive behavioral therapy (CBT) principles. The therapy chatbot reduced depression in the 16 weeks intervention period and reduced anxiety in the first 4 weeks. A significantly better therapeutic alliance was achieved in the chatbot-delivered therapy compared to the bibliotherapy.
https://www.sciencedirect.com/science/article/pii/S2214782922000021
In this episode of JAMA+ AI Conversations, Roy Perlis, MD, MSc, and Joseph Geraci, PhD, of Queen’s University discuss using machine learning to analyze clinical trials and related FDA interactions. Geraci’s work focuses on identifying subpopulations with different responses to help plan and analyze future trials. Dr. Perlis discusses applying emerging machine learning methods to plan more efficient clinical trials. Although much of the conversation about AI in health has focused on clinical applications, these tools are increasingly applied both in drug discovery and in clinical trial design and analysis. We discussed what it means to develop enriched trials by focusing on people most likely to respond to a particular treatment, a strategy that may help to reduce the rates at which clinical trials fail.
https://jamanetwork.com/journals/jama/fullarticle/2852846
“This study highlights how the use of machine learning can be employed to identify women experiencing severe subjective cognitive decline during the menopause transition and potential associated factors. Early identification of high-risk persons may allow for targeted interventions to protect cognitive health. Future studies involving objective measures of cognition and longitudinal follow-up are crucial to better understanding these associations,” says Dr. Stephanie Faubion, medical director for The Menopause Society.
Pancreatic cancer remains one of the deadliest cancers today — with projections that it will become the second-leading cause of cancer death in the U.S. by 2030 since it so often goes undetected until its later stages. Artificial intelligence (AI)-powered detection methods under development at Mayo Clinic Comprehensive Cancer Center are changing that approach, helping physicians detect pancreatic cancer up to three years earlier, when it is more treatable. Ajit H. Goenka, M.D., a nuclear medicine specialist and radiologist at Mayo Clinic in Rochester, Minnesota, leads a group of physicians, data scientists and other experts who developed an AI model to identify pancreatic cancer earlier than previously thought possible. The group's findings were published in a 2026 issue of Gut.
AI is defined by Merriam-Webster as “software designed to imitate aspects of intelligent human behavior”. The AMA prefers to use the term “augmented intelligence” to reflect its perspective that AI tools and services support rather than explicitly replace human decision-making. AI has been around for some time now but has made significant progress in recent years in transforming the practice of medicine and the delivery of health care. AI has the potential to anticipate problems or deal with issues as they come up, thus operating in an intended, logical, and adaptive approach. It can be a powerful tool in health care settings, capable of providing faster, more accurate diagnoses and lesser medical errors. While level of adoption varies across specialties, there is significant alignment in areas of opportunities for AI in obstetrics and gynecology, including, but not limited to, imaging analysis, expanded patient screening, reducing administrative burden, and supporting clinical decision making.
https://www.acog.org/practice-management/health-it-and-clinical-informatics?utm_source=chatgpt.com
Genius Cervical AI is a new artificial intelligence (AI) for cervical cancer screening that assists in objectively identifying pre-cancerous lesions and cervical cancer cells, infectious organisms, and glandular component from patient samples imaged on the Genius Digital Imager. AI-guided review streamlines cancer screening and results in more sensitive disease detection.
https://www.hologic.com/hologic-products/cytology/genius-cervical-ai
With the incorporation of artificial intelligence (AI), significant advancements have occurred in the field of fetal medicine, holding the potential to transform prenatal care and diagnostics, promising to revolutionize prenatal care and diagnostics. This scoping review aims to explore the recent updates in the prospective application of AI in fetal medicine, evaluating its current uses, potential benefits, and limitations.
https://pmc.ncbi.nlm.nih.gov/articles/PMC11745059/
Researchers are studying if AI-assisted imaging might be helpful in finding breast cancers on screening and diagnostic tests. Early studies have shown that AI might help doctors review breast cancer screening tests more quickly and lower the chances of a false positive or false negative result. The use of AI in medical imaging is an area of ongoing research.
Three commercially available radiology AI systems have shown the potential to flag early signs of breast cancer up to six years before a diagnosis, according to a study published in Radiology. In a Swedish retrospective study, researchers tested three AI-based computer-assisted detection (AI-CAD) systems on mammogram data from a large screening population. They found that cancer prediction scores issued by AI-CAD were elevated, on average, for individuals who were eventually diagnosed with breast cancer, while scores were low for those who remained cancer-free. “Approximately 20% of breast cancer cases demonstrate mammographic signs that are already visible to AI around six years before diagnosis,” explained senior coauthor Fredrik Strand, MD, PhD, of Karolinska University Hospital in Stockholm. “Our study confirms the potential of AI to, in some cases, find signs of cancer in the mammograms much earlier than when radiologists detected it.”
https://www.rsna.org/news/2026/june/ai-flags-breast-cancer-early
Artificial intelligence (AI) is rapidly being adapted to improve quality of patient care in all areas of medicine. Although there are few widely adopted applications of AI to cardiovascular (CV) and stroke care, AI has the potential to improve patient outcomes within a wide range of cardiovascular disease. AI has advanced in several areas, including CV imaging, electrocardiography, in-hospital monitoring, implantable and wearable devices, DNA sequencing and analysis technologies, and electronic health records (EHR). This statement discusses these applications of AI to CV medicine and patient care. Challenges addressed include the need for larger datasets, disparities built into algorithms that may reflect bias, accountability and reliability of tools, cybersecurity, and the ethics of using patient data.
Artificial intelligence (AI) is revolutionizing traditional drug discovery and development models by seamlessly integrating data, computational power, and algorithms. This synergy enhances the efficiency, accuracy, and success rates of drug research, shortens development timelines, and reduces costs. Coupled with machine learning (ML) and deep learning (DL), AI has demonstrated significant advancements across various domains, including drug characterization, target discovery and validation, small molecule drug design, and the acceleration of clinical trials. Through molecular generation techniques, AI facilitates the creation of novel drug molecules, predicting their properties and activities, while virtual screening (VS) optimizes drug candidates. Additionally, AI enhances clinical trial efficiency by predicting outcomes, designing trials, and enabling drug repositioning.
https://www.sciencedirect.com/science/article/pii/S2095177925000656
Computational AI is shifting biology from an observational science to an engineering discipline aimed at treating aging as a programmable process rather than inevitable decay [04:27]. Fueled by billions in private investment [05:19], breakthrough technologies like DeepMind’s AlphaFold [02:44], de novo protein design [03:30], and cellular reprogramming [00:48] have accelerated the discovery pipeline from decades to weeks, with initial trials in gene therapy, cellular rejuvenation, and xenotransplantation entering human subjects [07:03]. However, the video underscores a key reality check: while reversing systemic biological aging remains an unproven moonshot facing steep translation hurdles from mice to humans [11:31], AI-driven diagnostic tools in cardiology and oncology are already extending lives in hospitals today by catching terminal conditions years before symptoms emerge [12:43], moving humanity toward a significantly extended healthy midlife [18:45]. (Summary by Google Flash Extended)
https://youtu.be/dQYKcjvXhIY?is=gCkruyWmW05_i63g.
Your AI Prompts Aren't Private: The Most (and Least) Invasive Chatbots, Ranked
You can ask an AI chatbot anything, but what do your questions and prompts say about you? More importantly, how much of your privacy are you giving away when you converse with ChatGPT or Claude? The research team at Incogni, a well-known purveyor of personal data removal services, set out to quantify the privacy risks of using popular LLMs, identifying which ones take the most care with your privacy—and the least. Incogni’s researchers selected 13 AI platforms for analysis. They focused on companies whose primary product is AI, as well as major players like Microsoft and Meta.
============
Preparing for an appointment:
“Help me organize these symptoms into a concise list for my physician.”
Understanding a test report:
“Explain this radiology report in everyday language and give me questions to ask my doctor.”
Comparing treatment choices:
“Create a table showing the benefits, risks and questions I should discuss with my physician about these three alternatives.”
Medication preparation:
“Here are my medications. What questions should I ask my pharmacist about interactions?”
Then verify with the pharmacist rather than treating AI as the authority.
Following a diagnosis:
“Find the questions a newly diagnosed patient should ask her specialist.”
A note about privacy
At all of the leading sites - referred to as frontier sites - there is an icon at the upper right of the prompt page to engage privacy or anonymous mode.
=========
Google's Gemini https://gemini.google.com/app
OpenAI's ChatGPT https://chatgpt.com/
Anthropic's Claude https://claude.ai/
==========
Ray Schroeder
UIS Professor Emeritus and UPCEA Sr. Fellow
https://sites.google.com/view/raysspace/home/schroeder-bio
http://sites.google.com/view/aiforwomenshealth/