eXueed Medical

Revolutionizing cancer clinic and research with AI and big data


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Elevator Pitch

Summary of market size,
problems and solutions

In 2018, there were 17M and 9.5M new cancer cases and deaths worldwide, respectively. By 2040, it's anticipated that there will be 27.5M and 16.3M new cancer cases and deaths. Each of these cases and deaths are individuals and all deserve the best treatment to save their lives. 

However, the current treatment solutions are inconsistent because there are thousands of cancer clinics worldwide. Each clinic will have its own doctoral opinions and treatment options, leading to huge variabilities in the clinical decision.

Unfortunately, this often results in suboptimal outcomes due to inefficiencies, ineffective process, poor understanding of treatment evidence and the lack of standardization. eXueed offers a smart and standardized tool powered with latest outcomes data to help doctors treat every patient with the most effective option available, regardless of where you are in the world.


Ongoing Problems
Patients & Clinicians Continue to Suffer

Clinical decision-making process has been following the traditional way primarily
with human knowledge and reasoning, so, it’s opinion dependent.

Such a practice often leads to suboptimal outcomes and huge variabilities in radiation therapy, therefore, directly affecting cancer patient survival and quality of life.

The challenge in cancer therapeutics is due to complex information and overwhelming data.


eXueed Medical
Is the Solution the World Needs

Our solution will be the first to use computer analytics and machine learning to revolutionize the decision-making process in radiation therapy that will achieve optimal outcomes in treatment and great efficiency in the process.

There are two main optimization problems in the process, including the process itself:

We have developed a prototype software that streamlines the clinical workflow of treatment decision-making process in radiation therapy. The process is supported by computer optimization and big data analytics. The software can help clinicians select the best treatment plan achievable with the latest clinical evidence efficiently and effectively for each individual patient.


Why We Can Lead

As both clinicians and physicists, we understand what the clinic needs and what the technology can do …

The clinical decision-making in RT is one of the most complex process in modern medicine, involving in huge amount of data, sophisticated technologies and understanding of the evidence.

The current practice based on clinicians' experience and opinion is very inefficient and inconsistent. Treatment outcomes are often suboptimal due to the limitations in the understanding of the evidence.

The consequence of suboptimal treatment outcomes includes cancer recurrence, lower survival rate and worsen quality of life.


Why We’ll Dominate


Traction & Accomplishments

Our prototype has been tested fully functional as the research version.

We have received very positive feedback from clinicians about the usefulness of our software and the future expectation.
They believe this tool will help improve the clinical quality and efficiency of their work, so achieve better treatment outcomes.

In doing so, we can save more lives of cancer patients at a lower cost. CDSS can
be mandated in the near future. FDA published the first draft guideline in 2019.


Meet the Team

Jinyu Xue, Ph.D.

  • Medical physicist faculty at NYU Langone Medical Center
  • Previously at MD Anderson Cancer Center and Jefferson Hospital
  • Expert in RT technologies and clinical outcomes research
  • Have 80+ research publications and several industrial grants

Jun Lian, Ph.D.

  • Medical physicist faculty at University of North Carolina
  • Expert in optimization, automation and machine learning
  • Recipient of multiple government and industrial research grants
  • Published 100+ research papers, proceedings and book chapters

Jimm Grimm, Ph.D.

  • Medical physicist, currently at Geisinger Cancer Institute and previously at Johns Hopkins
  • Adjunct Professor, Thomas Jefferson University
  • Expert in data analysis, outcomes study and biological modeling
  • Developer of the DVH Evaluator, an FDA 501k approved software in RT 

Anand Mahadevan, M.D.

  • Radiation oncologist, professor and chair at Geisinger Cancer Institute
  • Previously affiliated with  Harvard Medical School
  • Recipient of multiple grants
  • Published 100+ research papers, proceedings and book chapters.

John Dooley, B.S.

  • Senior IT manager at a large academic cancer institution. Software expert with data extraction and processing
  • Years of experience working with academic and industrial projects. 

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