To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe). You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…
There’s huge potential for artificial intelligence in the field of medicine. AI is already driving scientific discovery, outperforming doctors on diagnostic tests, saving time on health care administrative tasks and much more, according to Stanford University’s new 2025 AI Index Report, which provides an overview of the current state of AI.
A leading use of AI in medical research is protein sequencing. AI researchers are uncovering proteins’ 3D shapes and other qualities, which will lead to vast new insights into the body’s cellular processes. Alphabet’s AlphaFold, a leading AI program that predicts protein structure, won its developers a 2024 Nobel Prize in Chemistry. “The problem it solved—predicting a protein’s 3D structure based solely on its amino acid sequence — was long considered intractable,” notes a report by deep tech venture capital firm DCVC. AlphaFold has uncovered over 200 million protein structures, far more than the 200,000 or so known to scientists in 2020. The upshot for doctors and patients: Better treatments, faster.
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Designing better drug candidates for trials is another example. By using AI to analyze huge amounts of biological and chemical data. Companies harnessing AI and data include Relation, Recursion, Nosis and Tahoe.
Creating virtual autonomous researchers that can pore over mountains of medical studies to summarize evidence and uncover new insights. Researchers at Stanford even created a virtual lab with AI models trained on specific medical knowledge to collaborate on new drugs.
Helping with clinical drug trials. Life sciences firm Saama has an AI model that generates protocols, study reports and other documents to save time. Amazon’s generative AI medical business touts generative AI for better matching patients with the right trials and for detecting adverse drug reactions in trials underway.
Plus, there is human brain mapping, genomic analysis, AI-enabled microscopes, early disease detection and many other uses of AI to analyze huge troves of data. For example, “a team at Google’s Connectomics project has reconstructed a one-cubic-millimeter section of the human brain at the synaptic level,” notes the Stanford AI report. The researchers used a multibeam scanning electron microscope and used AI to process the huge dataset of 57,000 cells and 150 million synapses.
AI is already gaining traction among doctors, with more uses on the horizon. One leading example is automated note-taking of doctor visits via speech recognition to create transcripts, summaries and bullet points in real time, a fast-growing use. One recent study found it could save 30 seconds per note and 20 minutes per day. Meditech is just one vendor that has already integrated AI scribes in its electronics health records software.
Virtual doctor assistants. Just as people use ChatGPT and other AI chatbots to answer questions and get more info, doctors will have an AI assistant at the ready. AI models can even be virtual specialists focused on specific disciplines, such as oncology, ophthalmology and radiology. AI will also help draft patient messages for doctors, even adding a dose of empathy.
Assistants for patients, too, with the aim of improving health outcomes. AI chatbots approved by doctors could help patients fully understand health issues and treatments with a 24/7 conversational chatbot that demystifies medical jargon. Generative AI “can transform complex medical information into understandable, patient-friendly formats,” according to a recent publication by the National Academy of Medicine.
AI will also help with medical coding, public health, medical image analysis, personal health monitoring, disease screening, contacting insurers and much more.
Note that there are still many challenges. Generative AI can create incorrect info. Medical professionals will need to be in the loop to design, test and refine AI tools. There’s even the potential that using AI could sometimes take longer, a big downside as doctors are already strapped for time. With sensitive health info, privacy and security issues will be ongoing challenges.
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