{"id":2705,"date":"2022-11-09T07:00:30","date_gmt":"2022-11-09T07:00:30","guid":{"rendered":"https:\/\/magazines.uthscsa.edu\/schools\/?p=2705"},"modified":"2022-11-09T15:36:30","modified_gmt":"2022-11-09T15:36:30","slug":"harnessing-ai-to-optimize-patient-treatments","status":"publish","type":"post","link":"https:\/\/magazines.uthscsa.edu\/schools\/2022\/11\/09\/harnessing-ai-to-optimize-patient-treatments\/","title":{"rendered":"Harnessing AI to optimize patient treatments"},"content":{"rendered":"

One machine, multiple targets<\/h2>\n

By Michael Seringer<\/em><\/h4>\n

Imagine the cancer cell under the microscope as an aerial photograph of a city. The human eye can recognize distinctive features of the city and tell if a building is on fire. An algorithm, backed by ever-increasing processing power, can count every building, stop sign and pothole, take measurements between each, discover new features altogether and even throw in some Euclidean geometry around specific features to help predict problems on city streets.<\/p>\n

\n

\"\"<\/p>\n

Bradley B. Brimhall, MD<\/strong><\/h4>\n
Professor of Pathology and Laboratory Medicine; Medical Director of Clinical Informatics, Health Care Analytics and Bioinformatics; Staff Pathologist, Hematopathology and Transfusion Medicine<\/em><\/h5>\n
Diagnostic:<\/strong><\/span> Predictive artificial intelligence disease detection tool<\/h6>\n
Indication:<\/strong><\/span> Cancer and urinary tract infection<\/h6>\n
Stage:<\/strong><\/span> Pre-seed, proof of concept<\/h6>\n
Timing:<\/strong> <\/span>Approximately three years<\/h6>\n
Funding:<\/strong><\/span> $200,000 \u2014 awards and grants<\/h6>\n
\n
The use of AI in medical decision-making is the future, and physicians can lead that change or be swept before it.<\/span><\/em><\/h6>\n<\/blockquote>\n
\n

Bradley Brimhall and collaborative teams of researchers are harnessing the power of artificial intelligence and predictive modeling to develop leading-edge diagnostics.<\/p>\n

\u201cWe are using machine learning and artificial intelligence to segment the image, recognize features, give them each a name, count them, calculate some geometric parameters and hopefully make the images more usable and quantifiable,\u201d he said.<\/p>\n

The advantage of machine learning-based diagnostics is that the core technology can be used not only to scan slide images, but also to consume large datasets of anonymous patient information to develop accurate prediction models. One of the first disease targets of such algorithms is predicting urinary tract infections using aggregated patient data. The idea is that physicians could use this AI tool to predict \u2014 with a high\u00a0degree of accuracy \u2014 a positive urinary tract infection test well before the test results are in. Predictive diagnostics would allow physicians to optimize treatments, improving patient outcomes.<\/p>\n

It takes a great team<\/strong><\/p>\n

Brimhall stresses the importance of a cohesive, multidisciplinary team in developing novel diagnostic tools and believes his partnerships with Southwest Research Institute and The University of Texas at San Antonio\u00a0have been key to the early success of their algorithm. Relying on his past experience leading both for-profit and nonprofit institutions, Brimhall engaged with a multidisciplinary team of skilled scientists from the start.<\/p>\n

\u201cIt takes a team to develop an AI diagnostic tool,\u201d Brimhall said. \u201cGood data science and analytics requires people. Data are the raw materials and software is the tool, but it\u2019s the people who make things happen.\u201d<\/p>\n

A diversity of experience and skill sets improves the overall product, Brimhall said. Each team member approaches the problems from a unique viewpoint, providing more opportunities for creative solutions. For a medical technology startup to succeed, it requires knowledge of both the business and academic worlds. The business, clinical and science team members all need to be aligned to improve the odds of product success.<\/p>\n

\n
\n
\"\"\u201cGood data science and analytics requires people. Data are the raw materials and software is the tool, but it\u2019s the people who make things happen.\u201d<\/em><\/span><\/h6>\n<\/blockquote>\n

Even the best AI diagnostic tool is essentially worthless if it does not become part of a medical system\u2019s workflow. The tool won\u2019t make it out of the research institution if the market is too small or the team lacks a broad perspective.<\/p>\n

\u201cResearch teams can get caught up in an academic myopia where the long view is obscured by small details,\u201d Brimhall said. A balance between the two worlds ensures a more efficient go-to market strategy.<\/p>\n

A new regulatory frontier<\/strong><\/p>\n

To gain marketing approval from the Food and Drug Administration for a diagnostic tool, investigators must show the rate of false positives and negatives in a clinical trial. However, AI-based diagnostic platforms have an easier time getting marketing approval because as tools that assist pathologists in reviewing slides, they are considered low risk.<\/p>\n

These diagnostic tools are reviewed through the FDA\u2019s \u201cDe Novo\u201d classification pathway for novel devices in which two types of classifications, I and II, are assigned based on the risk associated with the tool.<\/p>\n

\u201cThe FDA pathway for devices is much easier and cost effective compared to medications,\u201d Brimhall said.\u00a0 Getting FDA device approval is much less than the cost to develop a new drug.\u201d<\/p>\n

Another important trait of machine learning is the evolution of the algorithm over time. As the AI-based\u00a0prediction tool consumes more and more data, it improves. The machine is always learning and sharpening its accuracy.<\/p>\n

The FDA has recognized this characteristic of machine learning by providing guides and best practices for AI-enabled diagnostics and devices. The FDA also maintains a searchable database of approved, AI-based\u00a0algorithms that gives researchers a free resource and technical road map to help them determine market size, understand the competitive landscape and develop a regulatory strategy.<\/p>\n

Proving the concept<\/strong><\/p>\n

The lifecycle of a business starts with a proof of concept for the product. This feasibility stage for AI-based diagnostics requires sufficiently large data sets. Data use agreements and patient privacy must be worked out along with specific data requirements.<\/p>\n

Once the algorithm is tested using large amounts of data, proof of concept can be determined quickly. Large data volumes yield significant findings to reduce risk and engender confidence for potential investors.<\/p>\n

Information gained by running AI-enabled tools also provides confidence for potential partners. Going into talks with electronic medical record companies or leading testing labs is made easier when the diagnostic tool has been proven using large amounts of data.<\/p>\n

Information is power, and machine learning and AI generate such power.<\/p>\n

\u201cThe most dangerous thing is showing up to a negotiation and not having information,\u201d Brimhall said. \u201cMore than 90% of a negotiation occurs before you even show up to the table. The key is the preparation that goes into the negotiation.\u201d Another important aspect of medical AI development is to make sure that the unique aspects of the program are well protected by patents.<\/p>\n

Medical AI is like the wild west, with many companies with sharp elbows competing for a small amount of intellectual space. Without such intellectual property and the willingness to fight for it, smaller AI companies will get rolled over.<\/p>\n

Finally, there is an inherent tension in medical AI development because the end user \u2014 physicians \u2014 are reluctant to test it. This is true for two reasons. First, many physicians mistrust a computer\u2019s judgment when it contradicts their own, because their own judgment was hard won over many decades of work.<\/p>\n

Second, subconsciously physicians may wonder if they will be replaced by AI, and they need to justify their existence. In truth, medicine needs to lead AI or AI will lead medicine, Brimhall said.<\/p>\n

Physicians can remain relevant by managing AI if they will take the time to understand it. The use of AI in medical decision-making is the future, and physicians can lead that change or be swept before it.<\/p>\n","protected":false},"excerpt":{"rendered":"

Bradley Brimhall and collaborative teams of researchers are harnessing the power of artificial intelligence and predictive modeling to develop leading-edge diagnostics. The use of AI in medical decision-making is the future, and physicians can lead that change or be swept before it.<\/p>\n","protected":false},"author":598,"featured_media":2723,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"magazine":[22],"issue-year":[48],"featured-story":[37],"class_list":["post-2705","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","magazine-future","issue-year-48","featured-story-landing-page"],"yoast_head":"\nHarnessing AI to optimize patient treatments - Magazines of the Schools at UT Health San Antonio<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/magazines.uthscsa.edu\/schools\/2022\/11\/09\/harnessing-ai-to-optimize-patient-treatments\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Harnessing AI to optimize patient treatments - Magazines of the Schools at UT Health San Antonio\" \/>\n<meta property=\"og:description\" content=\"Bradley Brimhall and collaborative teams of researchers are harnessing the power of artificial intelligence and predictive modeling to develop leading-edge diagnostics. 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