AI and the RAN

Expert: AI Can Help In The Fight Against Rare Cancers

This article was originally published in German and has been automatically translated.

Thanks to machine learning and, in particular, deep learning using artificial neural networks and the increasing availability of digital health data, the diagnosis and treatment of cancer is making great progress. Biologist Marc Bovenschulte writes this in a short study recently published by the Office of Technology Assessment at the German Bundestag (TAB). According to him, people suffering from rare cancers could also benefit from such approaches based on artificial intelligence (AI). However, such further developments, which involve dealing with increasingly personalized and data-driven medicine, face numerous technical and regulatory challenges.

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Rare tumors are defined as those that affect fewer than 6 in 100,000 people. In Germany, these include esophageal, laryngeal and thyroid cancer, Hodgkin's disease (malignant disease of the lymphatic system) and certain forms of leukemia. According to the study, considering the individual genetic, physical and morphological characteristics of patients increases the chance of "a precisely tailored treatment that is as effective as possible and has as few side effects as possible". AI approaches could be used here in the diagnosis, the selection of suitable therapeutic measures, the prognosis of the course of the disease and the therapeutic support of those affected. The technology also plays a role in the development of medication and new, personalized therapeutic approaches.

AI systems are particularly well suited to evaluating different data such as X-ray images, molecular biological information, sequence information from DNA analyses or literature databases, comparing them with each other, relating them to each other and drawing conclusions from them, explains Bovenschulte. CAD systems, for example, carry out an analysis of the image content in addition to humans and incorporate patterns from comparative or reference data to highlight conspicuous areas. However, this is less successful with small numbers of cases. In a study on the detection of heterogeneous tumors, however, a deep learning AI was almost twice as good at assessing the degree of aggressiveness of the disease based on computer tomography images compared to the classic method. The author also describes the creation of a digital twin to model treatment based on a virtual replica of patients as promising, as well as personalized mRNA vaccines in the fight against recurrent cancer.

Approval regime does not match individual therapy

Considering the boom in generative AI, experts are also exploring the extent to which ChatGPT & Co. Can be used in precision medicine for cancer treatment, according to Bovenschulte. The background to this is that knowledge of the biology of a tumor also improves the possibilities for its treatment. In initial experiments at Charité, for example, the bots have provided "some useful suggestions and clues" and in two cases even "unique therapeutic approaches" that no one had come up with before. As a rule, however, the results have not yet come close to the quality of human experts. Sometimes fictitious information (hallucinations) is integrated. In addition, the consistency of the results across different versions of the large language models is low.

According to the author, however, the individualization of therapeutic approaches described is "difficult to fit into the framework of existing approval regimes", which have so far been based as far as possible on extensive clinical studies with numerous test subjects, standardized products and procedures. Experimental treatments are permitted within the framework of therapeutic freedom and as therapeutic trials within narrow limits to treat individual patients with novel and less tested approaches. Nevertheless, Bovenschulte considers "a reliable framework" to be necessary in order to prove the effectiveness of personalized diagnosis and therapy. This also applies to AI-based procedures. A "diffusion of responsibility" involving hospital management, IT specialists or manufacturers in addition to medical professionals should be avoided.

(vbr)

Ask The Expert: Why AI Is Worth All The Hype

Q Why are we still hearing about generative artificial intelligence over a year after it burst on the scene? Is it really worth the hype?

A Yes, generative AI is worth the hype. Accountants are still facing significant staffing issues, resulting in having to do more work in the same or less time. Generative AI is a unique technology, with the potential to drive efficiency in everyday tasks and make professionals more productive. Its emergence jump-started a conversation about how to interact differently with technology to produce better results and drive additional benefits.

While there are quite a few stand-alone generative AI tools on the market, Wolters Kluwer believes that AI's true value comes from integrating generative AI into the firm's existing tech stack. Whether AI-powered automation drives a digital tax workflow or sifts through vast datasets to help auditors detect patterns and anomalies, when AI is embedded into everyday tools, accounting professionals can maximize their expertise, solve customer challenges, and drive superior outcomes for clients and the firm.

Q What's a key challenge facing AI right now?

A According to our customer surveys, trust is the top concern for tax and accounting professionals regarding AI. There are far too many stories and/or experiences with generative AI giving incorrect guidance or "hallucinations." However, most also see the potential for AI to make positive changes in how they approach work, especially when it comes to research, client communications, marketing, and automation.

A fair amount of the trust issues originate with many generative AI systems relying primarily on publicly available data, raising concerns about who's curating the data, when it was last updated, and the data source. This is why Wolters Kluwer has grounded our research-based generative AI capabilities in expert-curated tax and accounting content that is continuously updated and reviewed to ensure trustworthy, current, and relevant results.

Q How does AI impact smaller firms differently?

A AI doesn't necessarily impact smaller firms differently; rather, it can provide an outsized impact to smaller firms. At the heart of the opportunity to leverage AI is automating existing processes. Because AI is capable of learning, it improves over time without additional assistance, helping achieve greater efficiency without sacrificing quality or accuracy. Smaller firms are likely to notice the benefits of higher efficiency more quickly than their larger peers.

Similarly, firms that use a trusted research platform with generative AI capabilities will be able to access the answers they need more quickly and accurately. Because fewer small firms have dedicated research staff, the benefits of searching tax research platforms and guidance like the U.S. Master Tax Guide using everyday language and receiving results quickly and in simple terms that don't require extensive interpretation of the tax code will be more noticeable.

Health AI Expert Nathan Price Joins Buck Faculty

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Nathan Price, PhD

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Credit: Buck Institute

The Buck Institute for Research on Aging announces the appointment of Nathan Price, PhD, to Professor and Co-Director of the Center for Human Healthspan.  Price specializes in systems biology, artificial intelligence, and bioengineering. He has published more than 200 scientific papers and is co-author, with Buck Chief Innovation Officer and Distinguished Professor Lee Hood, of "The Age of Scientific Wellness." Price has been named one of 10 Emerging Leaders in Health and Medicine by the National Academy of Medicine and is a member of the Board on Life Sciences of the National Academies of Sciences, Engineering, and Medicine.

Dr. Price is Chief Scientific Officer of the healthy aging company Thorne. Price was also a Professor and Associate Director at the Institute for Systems Biology, where he was co-director of the Hood-Price Lab for Systems Biomedicine, and an Affiliate Faculty at the University of Washington in bioengineering and computer science. He received his PhD in bioengineering from the University of California, San Diego.

"I am really excited to welcome Nathan to the Buck faculty," says Buck President and CEO Eric Verdin, MD. "Nathan's expertise and experience is a terrific addition as we expand our focus into the human biology arena".  Buck Distinguished Professor Lee Hood is delighted to reunite with his former lab partner. "Having Nathan here as co-director of the Center for Human Healthspan will significantly propel our efforts," says Hood. "We have so many projects in the pipeline. I am delighted to have the opportunity to work with him again."

"I'm thrilled to join the Buck at this time of unprecedented progress in the field of research on aging," says Price.  "The Buck's focus on healthspan and the deep well of expertise of my new Buck colleagues in aging research were key factors in my decision to join them.  With recent advancements in AI and systems biology, there is no limit to what we can accomplish together."

About the Buck Institute for Research on Aging

At the Buck, we aim to end the threat of age-related diseases for this and future generations. We bring together the most capable and passionate scientists from a broad range of disciplines to study mechanisms of aging and to identify therapeutics that slow down aging. Our goal is to increase human health span, or the healthy years of life. Located just north of San Francisco, we are globally recognized as the pioneer and leader in efforts to target aging, the number one risk factor for serious diseases including Alzheimer's, Parkinson's, cancer, macular degeneration, heart disease, and diabetes. The Buck wants to help people live better longer. Our success will ultimately change healthcare. Learn more at: https://buckinstitute.Org

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