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Application Process

Call for Proposals

The 2024 spring application cycle includes a general open call for proposals as well as two focused challenges:

  • In partnership with the Center for Rare Diseases, projects that address rare diseases.
  • In partnership with the Institute for Foundations of Machine Learning and in alignment with UT’s “Year of AI,” projects that advance artificial intelligence in healthLearn more about UT’s “Year of AI” initiative and other AI news and campus events at

Dell Medical School collaborates with the Cockrell School of EngineeringCollege of PharmacyCollege of Natural SciencesInstitute for Foundations of Machine Learning (IFML)Discovery to Impact, and the Center for Rare Disease to offer the spring application cycle.

Texas Health Catalyst seeks innovators committed to translating groundbreaking ideas in health and life sciences into impactful solutions. Innovators who have early proof-of-concept evidence demonstrating the potential to address clearly defined unmet needs and who are dedicated to commercializing their innovation are strongly encouraged to apply.

The program offers innovators who are open to feedback and guidance from experts the opportunity to receive seed funding and mentorship to advance their project towards successful commercialization.

The AI in Health Challenge, in collaboration with the Institute for Foundations of Machine Learning, invites pioneering proposals addressing critical areas of health care, including:

  • Early disease detection and diagnosis.
  • Precision medicine.
  • Drug discovery and development.
  • Remote patient monitoring.

From devising AI algorithms for personalized treatment based on genomic insights to harnessing Internet of Things devices for continuous patient monitoring, applicants competing in the AI in Health Challenge have innovations or ideas that hold the promise of reshaping health care delivery and enhancing patient outcomes.

The Rare Diseases Challenge, in collaboration with the Center for Rare Disease, welcomes proposals addressing the intricate challenges of early and accurate diagnosis; genomic and molecular understanding; personalized treatment formulation; and the manufacturing and administration of personalized therapies.

From pioneering advanced diagnostic tools to developing AI-driven algorithms for pattern recognition, applicants competing in the Rare Diseases Challenge have innovations or ideas that hold the potential to revolutionize treatment approaches and enhance the quality of life for patients grappling with rare diseases.

Eligibility, Timeline & More


Innovators who meet either of the following criteria are eligible to apply for the general open call, the AI in Health Challenge or the Rare Diseases Challenge:

  • University of Texas at Austin faculty.
  • University of Texas at Austin physicians in training (residents and fellows), medical students, postdoctoral students and graduate students (with faculty supervision).


  • Application opens: March 18.
  • Health product commercialization primer and prospective applicant networking event: April 3.
  • Application closes: April 30.
  • Projects advance to in-depth review and mentoring: May 31.
  • Mentor meetings: June 3 to July 1.
  • Final submissions and review: July 16.
  • Award announcement: July 30.
  • Project follow-up meetings: October 2024, January 2025, April 2025 and July 2025.

Award Funding

At Texas Health Catalyst, funding goes beyond traditional models to best ensure meaningful outcomes. The program understands that every project comes with its own set of challenges and opportunities. In addition to assessing projects for clinical and commercial potential, Texas Health Catalyst’s experienced project mentors work closely with awardees to identify critical milestones necessary for success.

Funding awarded through Texas Health Catalyst, up to $50,000, is strategically allocated to support each project’s journey from concept to impact. Examples of funding milestones include conducting essential research, developing a prototype, or securing a regulatory or reimbursement strategy.

  • Consultation and engagement with non-UT entities
  • Equipment and supplies
  • Principal investigator compensation (salary and fringe benefits)
  • Postdoctoral student, fellow and graduate student salaries
  • Research staff support
  • Software
  • Administrative support
  • Computers
  • Indirect costs on subcontracts
  • Hiring expenses (background checks)
  • Patient care
  • Tuition

Assessment Rubric

The program’s evaluation process ensures that proposals align with the overall goal of identifying health innovations with the highest potential for clinical impact and potential success. The rubric covers three key domains: clinical/scientific application, development and commercialization. Each domain includes specific criteria aimed at assessing the feasibility, potential impact and commercial viability of proposed innovationss.

The clinical/scientific application domain evaluates the potential impact of the innovation on patient outcomes and the scientific rationale behind it.

Texas Health Catalyst assesses whether the innovation addresses a significant unmet medical need, its potential to improve patient outcomes and its comparative advantage over existing therapies.

The development domain delves into the technical feasibility of the innovation, its intellectual property protection, resource availability, regulatory pathway and development timeline.

Texas Health Catalyst looks for a clear and realistic development plan, identification of key milestones and strategies to mitigate potential risks.

The commercialization domain explores the market potential, reimbursement landscape, competitive landscape and marketing strategy of the innovation.

Texas Health Catalyst assesses the potential market size, timing for commercialization, pricing strategy, barriers to entry and potential exit strategies.

Sample Problems & Solutions

Early disease detection and diagnosis: Diseases are often diagnosed at advanced stages, leading to limited treatment options and poorer outcomes.

  • Potential solution: AI algorithms that analyze diverse data sources (genomic, clinical, imaging) to identify early disease markers, enabling timely interventions and personalized treatment plans.

Precision medicine and personalized treatment: Patients respond differently to treatments based on their genetic makeup and lifestyle factors, requiring tailored approaches.

  • Potential solution: AI to analyze genomic data and clinical records, predicting patient responses to specific treatments.
  • Potential solution: AI-driven precision medicine that enables personalized drug selection and dosage optimization.

Drug discovery and development: Drug discovery is time-consuming and costly, often resulting in high failure rates during clinical trials.

  • Potential solution: AI algorithms to analyze biological data, predict drug-target interactions and simulate compound behaviors.
  • Potential solution: AI to accelerate drug discovery, shortening research timelines and reducing costs.

Remote patient monitoring and engagement: Monitoring patients with chronic conditions and providing health care in remote areas are challenging.

  • Potential solution: Internet of Things devices equipped with AI analyze patient data in real time.
  • Potential solution: AI-driven applications that engage patients, provide personalized health information and collect behavioral data.
  • Potential solution: data analysis to offer tailored interventions and support.

Early and accurate diagnosis: Rare diseases often present with diverse and atypical symptoms, making early and accurate diagnosis challenging.

  • Potential solution: advanced diagnostic tools, including genetic testing and omics technologies.
  • Potential solution: AI-driven algorithms to identify rare diseases in their early stages.

Genomic and molecular understanding: Understanding the genetic and molecular basis of rare diseases is crucial for targeted therapies, but the complexity of theses diseases often hampers research progress.

  • Potential solution: advanced genomic, proteomic and metabolomic techniques to unravel molecular mechanisms.
  • Potential solution: AI for data analysis and pattern recognition that hastens the identification of potential therapeutic targets.

Personalized treatment formulation: Developing personalized treatments tailored to the unique genetic makeup of each patient is challenging.

  • Potential solution: AI and computational modeling to predict drug interactions, optimize dosages and design customized treatments.
  • Potential solution: high-throughput screening techniques to identify potential drug candidates specific to rare disease mutations.

Manufacturing and administration of personalized therapies: Manufacturing personalized drugs on a small scale and ensuring efficient administration present logistical challenges.

  • Potential solution: advanced manufacturing technologies such as 3D printing for personalized drug delivery systems.
  • Potential solution: automated manufacturing processes to reduce costs and enhance scalability.
  • Potential solution: novel routes of drug administration for patient convenience.