Participate in the Challenge:
Recoding Medicine

YOUR PERSONAL INFORMATION

Anrede

Titel

Nachname

Vorname

Telefonnummer

Ihre Position (e.g. project manager, CEO)

Institution

Institution (andere) (Hier Institution eintragen, falls diese nicht in der Liste ist)

Rechtsform

Rechtsform (andere) (Hier Rechtsform eintragen, falls diese nicht in der Liste ist)

Straße, Hausnummer

Adresszeile 2

Postleitzahl

Stadt

Land

Land (anderes) (Hier Land eintragen, falls dieses nicht in der Liste ist)

Where did you first learn that SPRIND exists?

Please specify where you heard about us.

What made you decide to actually apply to this Challenge?

Please specify what made you apply for this challenge.

YOUR SOLUTION

Project Title

0/50

Short Description

0/500

LINK TO RELEVANT ARTIFACTS

e.g., peer-reviewed articles, GitHub projects, dataset documentation, data-use agreements, etc.

0/500

CORE IDEA & VALUE

Describe your overall approach: the health dataset at the core of your project and how you will curate it into an AI-ready resource; the concrete application(s) – e.g. biomarkers, diagnostics, clinical decision support, or drug repurposing – through which you will demonstrate value; the translational or commercial value you expect; and the clinical area or industry in which you expect the highest impact.

0/2500

APPLICATION

How is your solution going to be used by researchers, clinicians or other medical professionals after the challenge has ended and your results have successfully made it to the market and into clinical/medical practice?

0/1000

NOVELTY OF MEDICAL USE-CASE

Describe the medical use-case you want to tackle with your AI solution; which part of the medical process you want to tackle, which diseases/conditions you want to work on.
Explain why this field is in need of a data-driven AI solution to generate a breakthrough for patients and how the clinical or scientific practice in the field should change.
Please include citations.

0/1500

TECHNICAL NOVELTY & ADVANTAGE OVER THE STATE OF THE ART

Which methods or techniques incl. AI methods and algorithms (cite the most recent papers or pre-prints where relevant) does your approach build on, and why do they give a measurable advantage over the current state of the art?
Why do you consider these algorithms to be best-suited for the goal you want to reach with your system?
Which relevant, previously unsolved bottleneck in the interplay between health data and AI does your project address?
Please include citations.

0/2000

MATURITY & RISKS

What prototypes, code bases, data pipelines, curated data samples, or models are already working (attach links or short demos if possible)? Rate the Technology Readiness Level (TRL) of the overall system and of each major sub-component (dataset, curation/ETL pipeline, model), and identify the parts that are still research-grade, including data-related risks (e.g. access, quality, generalization).

0/1000

KPIs & BENCHMARKS

Which KPIs or benchmarks (existing or newly established) would you use to track your progress, and how would you assess success?

0/500

PLANNED PUBLIC CONTRIBUTION

Describe the substantial, documented public contribution you plan to make available, and its intended access model. Examples: curated datasets, benchmark suites, annotation tools, ETL or methods pipelines, tools, open model weights, reference implementations, or contributions to existing open ecosystems.

0/1000

DATASET: DESCRIPTION, RELEVANCE & UNIQUENESS

Describe the dataset(s) at the core of your project – modality, size, depth, and provenance – and their clinical or societal relevance. What distinguishes them from publicly available alternatives, and why does making them usable enable a novel AI use or training that was previously precluded by data availability? Note that mere further development of existing datasets, marginal enrichment of large public datasets, or purely synthetic data without a real-world basis are not in the focus of this challenge.

0/2000

DATA ACCESS, LEGAL BASIS & ACCESS MODEL

How robust is your access to the data at the start of Stage 1? Describe the legal and ethical basis (consent, ethics approval, data protection) and the commercial usage/exploitation rights, and name the necessary steps and stakeholders (e.g. data holders, data protection officers). Which access model do you intend to use, and which alternative access models (federated learning, on-site training, synthetic data) have you examined and assessed?

0/1500

REQUIRED COMPUTE

Which compute resources will be required for the training of your AI system (including test- and validation runs or hyperparameter tuning) and how will they be procured?Which requirements must be fulfilled by the hardware in order to process the envisaged data (e.g. location, Secure Processing Environment etc.)?
On what kind of hardware will your solution run after the challenge? Remember to put sufficient resources for compute in your cost estimate.

0/500

WORK PLAN

The Plan should:

Cover milestones for the entire duration of the three stages, with specific information on what will be achieved after Stage 1.
Indicate where collaboration and subcontracting work packages will be required.
Contain cost information, including contributions by the team or the team’s institution (e.g. personnel, licenses, compute, infrastructure) for the three stages of the Challenge.

0/4000

FINANCIAL COST ESTIMATE FOR STAGE 1

Max 480,000 EUR (plus VAT, where applicable), in EUR.

0/100

TEAM

Adress:

Why is your team best suited to achieve the goal of the challenge?
What relevant field-specific expertise and experience do you already have, and with whom? Does the team cover the necessary interdisciplinary breadth (data science, clinical domain, data governance)?
Does the team have demonstrable access to the described data, and are the institutional relationships with the data holders robust?
What expertise is the team missing, and how will you address it?
Have you taken first steps toward a spin-off or industry cooperation?
Attach the CVs of the key people in the Attachment section.

0/2000

TEAM MEMBER 1

Name, role on the project (e.g., PI, Technical Lead, Research Engineer) and Percent FTE committed.

0/250

TEAM MEMBER 1

Links to demonstrate track record (Industry, Entrepreneurship, Google-Scholar, GitHub-Project, etc.)

0/250

TEAM MEMBER 2

Name, role on the project (e.g., PI, Technical Lead, Research Engineer) and Percent FTE committed.

0/250

TEAM MEMBER 2

Links to demonstrate track record (Industry, Entrepreneurship, Google-Scholar, GitHub-Project, etc.)

0/250

TEAM MEMBER 3

Name, role on the project (e.g., PI, Technical Lead, Research Engineer) and Percent FTE committed.

0/250

TEAM MEMBER 3

Links to demonstrate track record (Industry, Entrepreneurship, Google-Scholar, GitHub-Project, etc.)

0/250

ATTACHMENTS

Dokument auswählen (.PDF)

DATA PROTECTION POLICY

PRIVACY NOTICE

I/we declare that I/we agree to the processing of my/our data in accordance with the SPRIND GmbH data protection declaration dated 12 April 2022.

Reasons for Exclusion

I/we agree with the statement on reasons for exclusion:JaNein

Declaration of Infringements

I/we agree with the statement on infringements: Yes/ NoJaNein

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Participate in the Challenge:Recoding Medicine

YOUR PERSONAL INFORMATION

YOUR SOLUTION

ATTACHMENTS

DATA PROTECTION POLICY

Reasons for Exclusion

Declaration of Infringements

SPAM PROTECTION

Participate in the Challenge:
Recoding Medicine