AI Revolution in Healthcare: ChatGPT & Google Bard's Breakthroughs - Diagnosis, mRNA Tech, Cancer Detection & More
AI Revolution in Healthcare: ChatGPT & Google Bard’s Breakthroughs – Diagnosis, mRNA Tech, Cancer Detection & More.
Dive into the latest AI breakthroughs transforming healthcare since ChatGPT and Google Bard’s inception. Discover GPT-4’s rapid diagnostics, Moderna & IBM’s mRNA tech advancements, cutting-edge cancer detection methods, and more. Stay ahead in AI healthcare news with our comprehensive coverage on AI-powered drug discovery, early Alzheimer’s detection, and groundbreaking AI tools in medicine. Join us as we explore each major AI development that’s reshaping healthcare.
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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Master GPT-4, Gemini, Generative AI & LLMs – Simplified Guide for Everyday Users: Demystifying Artificial Intelligence – OpenAI, ChatGPT, Google Bard, AI ML Quiz, AI Certifications Prep, Prompt Engineering,” available at Etsy, Shopify, Apple, Google, or Amazon
Welcome to “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, Latest AI Trends,” where we dive deep into the complexities of AI and bring forth the latest developments in an easy-to-understand format. Today, we’re tackling a series of compelling updates from the AI frontier in the medical field and beyond. In a remarkable medical application, GPT-4, OpenAI’s newest language model, has been put to the test by Dr. Isaac Kohane of Harvard. Impressively, GPT-4 has been reported to perform better than many human doctors, correctly answering medical exam questions over 90% of the time. But what’s truly astonishing is its ability to diagnose a rare 1 in 100,000 condition in just seconds, a task that draws upon the depth of a seasoned physician’s experience. Despite these advances, Dr. Kohane’s book, ‘The AI Revolution in Medicine,’ brings us back to earth, reminding us that GPT-4 is not infallible, presenting a balanced view with examples of the model’s errors ranging from minor clerical issues to math mistakes.
hifting gears, we look at how pharmaceutical giant Moderna and tech behemoth IBM are joining forces to push the boundaries of mRNA technology. Their collaboration intends to combine generative AI and quantum computing, potentially accelerating the discovery of new therapies and vaccines. This is underpinned by using IBM’s MoLFormer, which is expected to enhance Moderna’s understanding of mRNA medicines. In a leap toward precision medicine, Google Cloud has recently launched two AI-powered tools geared at revolutionizing drug discovery. These innovative tools focus on predicting protein structures and managing vast amounts of genomic data, potentially shaving off years in drug development time. We also witness the rise of BiomedGPT, touted as one of the most sophisticated AI medical models, outperforming predecessors across multiple biomedical modalities. This model appears to be a game-changer with its multi-modal and multi-task learning capabilities.
The competition intensifies in the healthcare AI space with Google’s Med-PaLM 2 going through testing at the Mayo Clinic, while Microsoft swiftly incorporates AI advances into patient care by deploying GPT algorithms via cloud services. Furthermore, MedPerf emerges as a new beacon, an open benchmarking platform introduced by MLCommons, aimed to evaluate medical AI models on diverse datasets, prioritizing patient privacy and aiming to enhance AI’s generalizability in healthcare. Adding to an already impressive array of advancements, we have AlphaMissense by Google DeepMind, which is honing the ability to predict genetic diseases, and Google Cloud briefing the healthcare sector with new capabilities to sift through clinical data more efficiently. And finally, EVEscape, a new AI tool with the potential to predict future viral variants—imagine its profound implications had it been available at the onset of the COVID-19 pandemic!
To cap off, studies suggest that AI models like ChatGPT can outdo doctors in providing unbiased treatment recommendations for depression and that AI algorithms are increasingly crucial in cellular research, changing the landscape of biological imaging experiments. Before we conclude, let’s not forget about AMIE, Google’s Articulate Medical Intelligence Explorer, an AI system optimized for diagnostic reasoning that is giving medical professionals a run for their money. For those seeking a deeper understanding of these advancements, the book “AI Unraveled: Master GPT-4, Gemini, Generative AI & LLMs – Simplified Guide for Everyday Users: Demystifying Artificial Intelligence – OpenAI, ChatGPT, Google Bard, AI ML Quiz, AI Certifications Prep, Prompt Engineering,” is available on various platforms including Etsy, Shopify, Apple, Google, and Amazon. That brings us to the end of today’s episode. We hope you’ve gained new insights into the dynamic and revolutionary world of AI, especially its influence on healthcare. Join us next time on “AI Unraveled” as we continue to explore cutting-edge AI trends that are transforming our lives. Till then, this is your host signing off. Keep questioning, keep learning, and remember—the future is AI.
Dr. Isaac Kohane, a physician and computer scientist at Harvard, has tested the newest AI model, GPT-4, in a medical setting. According to his findings, GPT-4 performs better than many doctors, as it can answer medical exam licensing questions correctly more than 90% of the time, translate information for patients, and give doctors helpful suggestions about bedside manner.
Kohane tested GPT-4 on a real-life case and found that it could correctly diagnose a rare condition just as he would with all his years of experience. However, GPT-4 isn’t always reliable, and his latest book ‘The AI Revolution in Medicine’ is filled with examples of its blunders, ranging from clerical errors to math mistakes.
Moderna and IBM are partnering to advance mRNA technology using generative AI and quantum computing, which could speed up Moderna’s discovery and creation of new messenger RNA vaccines and therapies. Moderna’s scientists will have access to IBM’s generative AI model known as MoLFormer, which will help understand the characteristics of potential mRNA medicines and design a new class of vaccines and therapies.
This agreement comes as Moderna is trying to harness its mRNA technology to target other diseases, while IBM is ramping up its investment in AI with new partnerships, largely driven by the release of OpenAI’s ChatGPT.
Why does this matter?
The use of quantum computing and AI could help Moderna accelerate the discovery and creation of these new vaccines and therapies by solving problems too complex for traditional computers. The development of these new medicines could potentially benefit the general public by providing more treatment options for a range of diseases.
An AI model developed by experts at the Royal Marsden NHS foundation trust, the Institute of Cancer Research, London, and Imperial College London can accurately identify cancer, potentially speeding up diagnosis and treatment. The algorithm, which analyzes CT scans to determine if abnormal growths are cancerous, reportedly performs more efficiently and effectively than current methods.
Why does this matter?
The AI tool may help doctors make faster decisions about patients with abnormal growths that are currently deemed medium-risk. The model, which is still in its early stages, will require further testing before it can be introduced in healthcare systems. However, researchers hope the AI tool will eventually speed up cancer detection by fast-tracking patients to treatment.
Researchers at UT Southwestern Medical Center have found that AI-powered voice analysis can help diagnose Alzheimer’s and cognitive impairment in early stages. If confirmed by larger studies, these findings could primary care providers with an easy-to-perform screening tool for at-risk individuals.
The research used advanced ML and natural language processing (NLP) to identify even the subtlest changes in language and audio that individuals may not easily recognize.
Why does this matter?
Before ML and NLP, detailed speech studies were often unsuccessful as early changes were often undetectable to human ears. However, with advancements in AI, such novel testing methods have performed significantly better than standard cognitive assessments in detecting even mild impairments. Also, it took less than 10 minutes to capture a patient’s voice, outdoing the traditional tests, which took hours to administer.
Only a few days ago, researchers developed an AI model that outperformed traditional methods in identifying cancer. Does this indicate AI leading the charge in reducing overall healthcare costs with improved patient outcomes?
Google Cloud has launched two AI-powered tools to help biotech and pharmaceutical companies accelerate drug discovery and advance precision medicine. The Target and Lead Identification Suite aims to streamline the process of identifying a biological target and predicting protein structures, while the Multiomics Suite assists researchers in ingesting, storing, analyzing, and sharing large amounts of genomic data. Both tools aim to significantly reduce the time and cost associated with drug development.
Several companies, including Pfizer, Cerevel Therapeutics, and Colossal Biosciences, have already been using these products. Cerevel Therapeutics estimates that it will save at least three years on average by using the Target and Lead Identification Suite to discover new drugs.
Why does this matter?
AI seems to benefit humanity the most through its use in medicine and diagnostics. This launch from Google and the subsequent adoption by a pharma giant like Pfizer indicate the swift mainstreaming of the tech.
BiomedGPT is a unified and generalist Biomedical Generative Pre-trained Transformer model. BiomedGPT utilizes self-supervision on diverse datasets to handle multi-modal inputs and perform various downstream tasks.
Extensive experiments show that BiomedGPT surpasses most previous state-of-the-art models in performance across 5 distinct tasks with 20 public datasets spanning over 15 biomedical modalities.
The study also demonstrates the effectiveness of the multi-modal and multi-task pretraining approach in transferring knowledge to previously unseen data.
Why does this matter?
This research represents a significant advancement in developing unified and generalist models for biomedicine, holding promising implications for enhancing healthcare outcomes, and it could lead to discoveries in biomedical research.
In addition to its potential benefits for healthcare, BiomedGPT could also be used in drug discovery & medical education.
Reportedly, Google’s Med-PaLM 2 (an LLM for the medical domain) has been in testing at the Mayo Clinic research hospital. In April, Google announced its limited access for select Google Cloud customers to explore use cases and share feedback to investigate safe, responsible, and meaningful ways to use it.
Meanwhile, Google’s rivals moved quickly to incorporate AI advances into patient interactions. Hospitals are beginning to test OpenAI’s GPT algorithms through Microsoft’s cloud service in several tasks. Google’s Med-PaLM 2 and OpenAI’s GPT-4 each scored similarly on medical exam questions, according to independent research released by the companies.
Why does this matter?
It seems Google and Microsoft are racing to translate recent AI advances into products that clinicians would use widely. The AI field has seen rapid advancements and research in diverse domains. But such a competitive landscape accelerates translating them into widely available, impactful AI products (which is sometimes slow and challenging due to the complexity of real-world applications).
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MLCommons, an open global engineering consortium, has announced the launch of MedPerf, an open benchmarking platform for evaluating the performance of medical AI models on diverse real-world datasets. The platform aims to improve medical AI’s generalizability and clinical impact by making data easily and safely accessible to researchers while prioritizing patient privacy and mitigating legal and regulatory risks.
MedPerf utilizes federated evaluation, allowing AI models to be assessed without accessing patient data, and offers orchestration capabilities to streamline research. The platform has already been successfully used in pilot studies and challenges involving brain tumor segmentation, pancreas segmentation, and surgical workflow phase recognition.
Why does this matter?
With MedPerf, researchers can evaluate the performance of medical AI models using diverse real-world datasets without compromising patient privacy. This platform’s implementation in pilot studies and challenges for various medical tasks further demonstrates its potential to improve medical AI’s generalizability, clinical impact, and advancements in healthcare technology.
Google and DeepMind have introduced Med-PaLM M, a multimodal biomedical AI system that can interpret diverse types of medical data, including text, images, and genomics. The researchers curated a benchmark dataset called MultiMedBench, which covers 14 biomedical tasks, to train and evaluate Med-PaLM M.
The AI system achieved state-of-the-art performance across all tasks, surpassing specialized models optimized for individual tasks. Med-PaLM M represents a paradigm shift in biomedical AI, as it can incorporate multimodal patient information, improve diagnostic accuracy, and transfer knowledge across medical tasks. Preliminary evidence suggests that Med-PaLM M can generalize to novel tasks and concepts and perform zero-shot multimodal reasoning.
Why does this matter?
It brings us closer to creating advanced AI systems to understand and analyze various medical data types. Google DeepMind’s MultiMedBench and Med-PaLM M show promising performance and potential in healthcare applications. It means better healthcare tools that can handle different types of medical information, ultimately benefiting patients and healthcare providers.
Scientists from St. Petersburg University and other organizations have created a database of eye movement strategies of PC users in different states of fatigue. They plan to use this data to train neural network models that can accurately track the functional state of operators, ensuring safety in various industries. The database includes a comprehensive set of indicators collected through sensors such as video cameras, eye trackers, heart rate monitors, and electroencephalographs.
An example of human fatigue analysis using video recording.
Why does this matter?
The scientists believe that this approach will allow for remote assessment of fatigue severity, and the database will be accessible to software developers for testing their products.
Paige, a technology disruptor in healthcare, has joined forces with Microsoft to build the world’s largest image-based AI models for digital pathology and oncology.
Paige developed the first Large Foundation Model using over one billion images from half a million pathology slides across multiple cancer types. Now, it is developing a new AI model with Microsoft that is orders-of-magnitude larger than any other image-based AI model existing today, configured with billions of parameters.
Paige will utilize Microsoft’s advanced supercomputing infrastructure to train the technology at scale and ultimately deploy it to hospitals and laboratories across the globe using Azure.
Why does this matter?
This will help realize the potential of generative AI at an unprecedented scale, introduce completely novel capabilities of AI, and serve as the cornerstone for the next generation of clinical/healthcare applications built with AI.
Google DeepMind’s new system, called AlphaMissense, can tell if the letters in the DNA will produce the correct shape. If not, it is listed as potentially disease-causing.
Currently, genetic disease hunters have fairly limited knowledge of which areas of human DNA can lead to disease and have to search across billions of chemical building blocks that make up DNA. They have classified 0.1% of letter changes, or mutations, as either benign or disease-causing. DeepMind’s new model pushed that percentage up to 89%.
Why does this matter?
AI is changing nearly everything we do at the moment and might revolutionize molecular biology and life sciences, too. This development is expected to speed up diagnosis and help search for better genetic disease treatments.
Google Cloud introduced new Vertex AI Search features for healthcare and life science companies. It will allow users to find accurate clinical information much more efficiently and to search a broad spectrum of data from clinical sources, such as FHIR data, clinical notes, and medical data in electronic health records (EHRs). Life-science organizations can use these features to enhance scientific communications and streamline processes.
Why does this matter?
Given how siloed medical data is currently, this is a significant boon to healthcare organizations. With this, Google is also enabling them to leverage the power of AI to improve healthcare facility management, patient care delivery, and more.
A new AI tool named EVEscape, developed by researchers at Harvard Medical School and the University of Oxford, can make predictions about new viral variants before they actually emerge and also how they would evolve.
In the study, researchers show that had it been deployed at the start of the COVID-19 pandemic, EVEscape would have predicted the most frequent mutations and identified the most concerning variants for SARS-CoV-2. The tool also made accurate predictions about other viruses, including HIV and influenza.
Why does this matter?
The information from this AI tool will help scientists develop more effective, future-proof vaccines and therapies. If only this AI boom happened a little earlier, it could have prevented the Covid-19 pandemic. But I guess no more pandemics, thanks to AI?
According to new study, ChatGPT makes unbiased, evidence-based treatment recommendations for depression that are consistent with clinical guidelines and outperform human primary care physicians. The study compared the evaluations and treatment recommendations for depression generated by ChatGPT-3 and ChatGPT-4 with those of primary care physicians.
Vignettes describing patients with different attributes and depression severity were input into the chatbot interfaces.
Why does this matter?
Compared with primary care physicians, ChatGPT showed no bias in recommendations based on patient gender or socioeconomic status. This means the chatbot was aligned well with accepted guidelines for managing mild and severe depression.
A new paper by Nature details how AI-powered image analysis tools are changing the game for microscopy data. It highlights the evolution from early, labor-intensive methods to machine learning-based tools like CellProfiler, ilastik, and newer frameworks such as U-Net. These advancements enable more accurate and faster segmentation of cells, essential for various biological imaging experiments.
Cancer-cell nuclei (green boxes) picked out by software using deep learning.
Why does this matter?
The short study highlights the potential for AI-driven tools to revolutionize further biological analyses. The advancement is crucial for understanding diseases, drug development, and gaining insights into cellular behavior, enabling faster scientific discoveries in various fields like medicine and biology.
MedLM is a family of foundation models fine-tuned for the healthcare industry, generally available (via allowlist) to Google Cloud customers in the U.S. through Vertex AI. MedLM builds on Med-PaLM 2. Google will soon add Gemini-based models into the MedLM suite to offer even more capabilities.
Why does this matter?
Google isn’t done yet. While its impressive Gemini demo from last week may have been staged, Google is looking to fine-tune and improve Gemini based on developers’ feedback. In addition, it is also racing with rivals to push the boundaries of AI in various fields.
Google developed Articulate Medical Intelligence Explorer (AMIE), an LLM-based research AI system optimized for diagnostic reasoning and conversations.
AMIE’s performance was compared to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors.
Why does this matter?
While further research is required before AMIE can be translated to real-world settings, it represents a milestone towards conversational diagnostic AI. If successful, AI systems such as AMIE can be at the core of next-generation learning health systems that help scale world-class healthcare to everyone.
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