Predictive Oncology Inc. (NASDAQ: POAI) Set to Capitalize on Increased AI Usage within Oncological Treatments

• Artificial Intelligence is being increasingly used to help understand and accurately predict how drug therapies can interact with specific patients
• POAI’s TumorGenesis subsidiary is also creating laboratory-grown tumors to help clinicians and researchers to better test therapies

Predictive Oncology (NASDAQ: POAI), a knowledge-driven precision medicine company that focuses on applying data and artificial intelligence (“AI”) to cancer personalized medicine and drug discovery, is set to play a key role as the integration of AI technology within cancer care continues to improve the accuracy and speed of diagnosis, aid clinical decision-making and lead to better health outcomes. The National Cancer Institute (“NCI”) in the United States has separately revealed that it is increasingly looking to invest in supporting research and developing infrastructure centered around the usage of AI in developing oncological treatments (https://nnw.fm/4aaAL) – a niche where Predictive Oncology is increasingly building a dominant presence. In addition, the White House has announced $2 billion funding for public and private sector “AI hubs” designed to drive key advances in AI as well as other computational technologies (https://nnw.fm/KN9GV).

A primary goal of precision cancer medicine is the accurate prediction of optimal drug therapies based upon the personalized molecular profiles of patient tumors; ideally, such predictions are based upon well-established molecular cause-and-effect relationships that are disrupted in cancer calls (https://nnw.fm/iKs1p). The foundation of these accurate predictions is built upon extensive bodies of data, with the volume of cancer-relevant data being generated and electronically stored on a daily basis vastly exceeding what could even be imagined only a few decades ago. As such, the search for significant correlations within cancer-relevant “big” datasets is a task which is ideally suited for artificial intelligence and machine learning-based approaches.

Predictive Oncology focuses on building AI-driven predictive models of tumor drug response and outcomes from its unique real-world longitudinal data of drug-response and genomic profiles gathered from more than 150,000 de-identified cancer cases. Through its subsidiary Helomics, POAI is seeking to bring this cutting-edge technology to cancer research, where it is working with the pharmaceutical, diagnostic and biotech industries to develop predictive models of how tumors respond to drugs, which can be used both for clinical decision support and research into new therapies.

Meanwhile, the company’s wholly-owned TumorGenesis subsidiary specializes in the field of ovarian cancer, creating laboratory-grown cancer cells which can then be used to assist researchers and clinicians in identifying which cancer cells bind to specific biomarkers. Once the biomarkers are identified, they can be used in TumorGenesis’ proprietary Oncology Capture Technology Platform, which is able to grow the patient’s heterogeneous tumor sample in the laboratory, enabling oncologists to develop a patient-specific treatment plan (http://nnw.fm/kGMxW).

Going forward, the use of artificial intelligence will help oncological researchers quickly understand how cancer cells become resistant to anti-cancer drugs, which can help improve drug development and adjust drug use. The technology can also be used to manage the use of chemotherapy drugs and predict a patient’s tolerance levels, thus allowing for chemotherapy regimens to be optimized. In the future, by leveraging Predictive Oncology’s AI-driven solutions, doctors will be able to make better-informed treatment decisions, thus reducing the need for unnecessary or poorly targeted treatments and improving patient outcomes.

For more information about the company, visit www.Predictive-Oncology.com.

NOTE TO INVESTORS: The latest news and updates relating to POAI are available in the company’s newsroom at http://nnw.fm/POAI

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