Generative AI is changing medicine, and it’s happening fast. HMS is getting a jump on this shift by training future doctors with skills in data and machine learning.
Harvard Medical School is building artificial intelligence into the curriculum to train the next generation of doctors.
Today, more than a quarter of all new code at Google is generated by AI, then reviewed and accepted by engineers.
More than a quarter of Google’s new code is being generated by artificial intelligence (AI), CEO Sundar Pichai revealed during Tuesday’s third-quarter earnings call for the leading tech company.
We’re also using AI internally to improve our coding processes, which is boosting productivity and efficiency, Pichai said during the call.
This was first reported by the Wall Street Journal. This comes after the company recently received a fundraise of $6 billion in a Series B round. The company said in a statement that the funding saw participation from several key investors, including Valor Equity Partners, Vy Capital, Andreessen Horowitz, Sequoia Capital, Fidelity Management & Research Company, Prince Alwaleed Bin Talal, Kingdom Holding, and others.
“Al-determined tumor volume has the potential to advance precision medicine for patients with prostate cancer by improving our ability to understand the aggressiveness of a patient’s cancer and therefore recommend the most optimal treatment,” said Dr. David D. Yang, MD.
How can artificial intelligence (AI) help medical professionals identify, diagnose, and treat prostate cancer? This is what a recent study published in Radiology hopes to address as a team of researchers developed an AI model designed to identify prostate cancer lesions, which holds the potential to help medical professionals and patients make the best-informed decisions regarding diagnoses and treatment options.
For the study, which was conducted between January 2021 to August 2023, the researchers had their AI model examine MRI scans from 732 patients, including 438 patients who underwent radiation therapy (RT) and 294 patients who underwent radical prostatectomy (RP). The goal was to compare a potential success rate of the AI model identifying tumors compared to patient treatment between 5 to 10 years after being diagnosed.
In the end, the AI model demonstrated an 85 percent accuracy in identifying cancerous lesions. Additionally, the AI model identified the larger volume lesions that resulted in failed treatment and metastasis, which is when cancer tumors spread beyond the original location within the body. Finally, the AI model determined that RT patients were at a decreased risk of metastasis based on their tumor volumes.
This level of precision could be a game-changer for therapies that require gene expression in one specific tissue, without impacting others.
By providing more control over where and when genes are activated, these AI-designed CREs could potentially be used in a variety of therapeutic applications, from treating genetic diseases to optimizing tissue regeneration.
As this AI-powered approach to designing CREs matures, the possibilities are vast. Beyond basic research, these synthetic DNA switches could be employed in biomanufacturing or to develop advanced treatments for a range of conditions, offering more effective ways to manipulate genes with unprecedented precision.
Caltech scientists have introduced a revolutionary machine-learning-driven technique for accurately measuring the mass of individual particles using advanced nanoscale devices.
This method could dramatically enhance our understanding of proteomes by allowing for the mass measurement of proteins in their native forms, thus offering new insights into biological processes and disease mechanisms.
Caltech scientists have developed a machine-learning-powered method that enables precise measurement of individual particles and molecules using advanced nanoscale devices. This breakthrough could lead to the use of various devices for mass measurement, which is key to identifying proteins. It also holds the potential to map the complete proteome—the full set of proteins in an organism.
Dr. Sanjeev Namjoshi, a machine learning engineer who recently submitted a book on Active Inference to MIT Press, discusses the theoretical foundations and practical applications of Active Inference, the Free Energy Principle (FEP), and Bayesian mechanics. He explains how these frameworks describe how biological and artificial systems maintain stability by minimizing uncertainty about their environment.
Namjoshi traces the evolution of these fields from early 2000s neuroscience research to current developments, highlighting how Active Inference provides a unified framework for perception and action through variational free energy minimization. He contrasts this with traditional machine learning approaches, emphasizing Active Inference’s natural capacity for exploration and curiosity through epistemic value.
The discussion covers key technical concepts like Markov blankets.
generative models, and the distinction between continuous and discrete implementations. Namjoshi explains how Active Inference moved from continuous state-space models (2003−2013) to discrete formulations (2015-present) to better handle planning problems.
He sees Active Inference as being at a similar stage to deep learning in the early 2000s — poised for significant breakthroughs but requiring better tools and wider adoption. While acknowledging current computational challenges, he emphasizes Active Inference’s potential advantages over reinforcement learning, particularly its principled approach to exploration and planning.
Namjoshi advocates for balanced oversight that enables innovation while maintaining appropriate safeguards. He expresses particular concern about the rapid pace of AI development potentially outpacing our understanding of risks and regulatory frameworks.
Dr. Sanjeev Namjoshi.
