{"id":3614,"date":"2023-03-14T13:56:45","date_gmt":"2023-03-14T13:56:45","guid":{"rendered":"https:\/\/ieeecscai.wpengine.com\/2023\/?page_id=3614"},"modified":"2023-05-18T05:59:36","modified_gmt":"2023-05-18T05:59:36","slug":"khai-minh-pham","status":"publish","type":"page","link":"https:\/\/cai.ieee.org\/2023\/khai-minh-pham\/","title":{"rendered":"Khai Minh Pham"},"content":{"rendered":"\t\t
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Khai Minh Pham
\nFounder and CEO, ThinkingNodeLife.ai

\nAI in Healthcare<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\"\"<\/p>

June 6, 11;00am
Location: Santa Clara I<\/h5>

A Survey of AI for Drug R&D and Why Explainable AI Matters
<\/span><\/h4>

To bring a new drug to the market, the average cost is around $2 billion, and it takes approximately 10 to 12 years. This trend has become more challenging as the development of \u201clow hanging fruit\u201d drugs has already taken place. However, the advancements in computing hardware and software, along with the adoption of AI\/ML, have shown promising results by shortening the time from discovery to clinical trials. The successful application of AI in drug R&D depends on the availability of high-quality data and knowledge and the use of the right type of AI based on the development phase. Contrary to popular belief, drug R&D is primarily knowledge-driven and not solely data-driven. For instance, applications like self-driving cars are data-driven because they mainly require statistical pattern recognition capabilities without the need for in-depth comprehension of the decision processes. In contrast, drug R&D requires explainable AI, which is not only vital to ensure transparency, accountability, and trustworthiness of the models but also to understand the pathobiology of the disease and the mechanisms of action of the drug for better intervention strategies to increase the efficacy and lower the risk of negative side effects. The successful development of efficient explainable AI requires the understanding of the level of explanation that is needed for a given audience and is based on the type of AI that has been used.<\/p>

\"\"<\/a>Combining Medicine (MD) and Artificial Intelligence (PhD), including working extensively on both Machine Learning and Reasoning Computing for 25+ years, Dr. Khai Pham<\/strong> has been a successful innovator and entrepreneur in Silicon Valley. He moved to San Diego to be closer to its amazing Life Sciences community to apply its next-gen Distributed Unified Reasoning AI. He pioneered in the Machine Learning market as the founder & CEO of DataMind, a leading AI company using an Intelligent Agent Technology. Benchmarked by Andersen Consulting Research as the best technology, it was used by major financial institutions, marketing centers, and medical manufacturers, and Bank of America acquired the patent in 2012. Later renamed RightPoint, it was acquired by E.piphany (Nasdaq:EPNY). Today, with ThinkingNodeLife.ai (TNL), Dr. Pham is pioneering in Generative Reasoning AI that differs from existing Generative AI that is used in applications, such as ChatGPT, by two main elements: it is based on reasoning and not pattern recognition and instead of generating text, image, or video, it generates models. TNL focuses on generating Digital Human Cell Clones for drug R&D. M.D. and a Ph.D. in AI both from Sorbonne University in Paris. Published several scientific papers, received international grants, taught AI at LISH-CNRS, received an entrepreneur award from BP, was EIR\/Mentor at CONNECT, talked as keynote in conferences.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Khai Minh Pham Founder and CEO, ThinkingNodeLife.ai AI in Healthcare June 6, 11;00amLocation: Santa Clara I A Survey of AI for Drug R&D and Why Explainable AI Matters To bring a new drug to the market, the average cost is around $2 billion, and it takes approximately 10 to 12 years. This trend has become […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"class_list":["post-3614","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3614","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/comments?post=3614"}],"version-history":[{"count":0,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3614\/revisions"}],"wp:attachment":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/media?parent=3614"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}