Evidence-based Health Data for AI Longevity Apps

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Presentation
Dominik Tilman
Project Owner

Evidence-based Health Data for AI Longevity Apps

Funding Requested

$5,000 USD

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Overview

Data and knowledge used in AI-driven health and longevity apps is often inconsistent, contradictory or based on subjective theories. Our goal is to improve the integrity of this data and revolutionise healthcare by providing each individual with an evidence-based, personalised health protocol for wellbeing, longevity and disease prevention. For this ideation proposal, we will combine TrustLevel's work in data integrity and Lennart's expertise in biohacking and focus on market research, concept development and feasibility study for our new approach.

Proposal Description

Our Team

Our team combines experience in AI, data reliability, health optimization, and biohacking.

This mix ensures that we can effectively tackle the challenges of AI-driven healthcare applications and bring innovative and reliable solutions to the market.

View Team

Company Name (if applicable)

TrustLevel

Please explain how this future proposal will help our decentralized AI platform grow and how this ideation phase will contribute to that proposal.

This future proposal can add value to SNET's AI Marketplace by:

  • Expanding the service offering by adding a new service.
  • Attracting new users and partners who use our service and can thus also become aware of other services. 
  • Generate new revenue for SNET through our planned revenue-sharing model. 

Clarify what outcomes (if any) will stop you from submitting a complete proposal in the next round.

n/a

The core problem we are aiming to solve

In the rapidly growing field of AI-driven health and longevity applications, the reliability and credibility of data sources are paramount. However, the current landscape is plagued by inconsistent data quality, lack of transparency, and difficulty in assessing the credibility of data sources. Furthermore, AI models and apps are often based on contradicting and subjective theories, protocols, and opinions of longevity and health experts. This leads to suboptimal AI models, unreliable health recommendations, and potential risks for users relying on these applications for critical health decisions.

Our specific solution to this problem

Expected Outcomes of Ideation Phase:

  1. Validated Problem and Solution Concept: A good understanding of the core problem and validation of the proposed solution through research and stakeholder engagement.
  2. Detailed Use Cases and Framework: Clear use cases and a conceptual framework outlining how TrustLevel's protocols and AI can improve data reliability in health and longevity applications.
  3. A Follow-Up Proposal: This ideation proposal will build a solid foundation for a follow-up proposal to develop a proof of concept (PoC) in the next funding round, including defined objectives, milestones, and resource requirements.

Project details

Ideal Scenario:

Imagine a world where every individual has access to a personal AI-driven longevity doctor, dedicated to optimizing their health, extending their health span, and preventing diseases. 

This vision could be made possible through the democratization of biomarker measurement via new technologies. With access to this data, an AI-based doctor and application tailors a personalized protocol that aligns with an individual's preferences and goals. This protocol could encompass nutrition, exercise, supplementation, and specific treatments such as detoxification. The recommendations are verified against reliable data, comparing outcomes from similar protocols to refine and improve health strategies continually.

The AI-driven system would provide specific product and brand suggestions based on both price and quality, ensuring that only effective, evidence-based products are recommended. This approach eliminates the market for low-quality, overpriced products, as only those that demonstrably improve health metrics are endorsed.

Through this innovative application of AI, we can revolutionize healthcare, making it more personalized, data-driven, and effective in promoting long-term wellness and disease prevention for everyone. 

Of course this vision sounds exciting, but how do we get there?

Our approach to ideation:

With our expertise and experience in data reliability (Dominik with TrustLevel), longevity and biohacking (Lennart), we will use the resources of this ideation proposal to investigate and identify market potentials and gaps:

1. Research and Analysis:

We will conduct comprehensive research on the current landscape of AI-driven health and longevity applications:

  • to identify key challenges related to data reliability and credibility.
  • to review existing solutions and technologies used to address similar issues.

2. Stakeholder Engagement:

We will engage with experts in AI, health, and decentralized ecosystems:

  • to conduct interviews to gather insights and understand user needs and pain points.
  • to validate the core problem and proposed solution.

3. Concept Development:

  • We will develop use cases and scenarios where TrustLevel's reputation protocols can be applied to enhance data credibility in health and longevity applications.
  • We will outline the technical requirements and architecture for integrating AI and TrustLevel’s protocols.
  • We will create a conceptual framework how to integrate this concept into SNET’s AI Marketplace

4. Feasibility Study:

  • We will assess the technical feasibility of the proposed solution, including potential challenges and mitigation strategies and estimate the resources, time, and budget required for a proof of concept (PoC) in the next phase.
  • We will try to build a network of supporters and collaborators who can contribute to the follow-up proposal and PoC development

Proposal Video

DF Spotlight Day - DFR4 - Lennart Van Der Ziel - Evidence-based Health Data for AI Longevity Apps

3 June 2024
  • Total Milestones

    3

  • Total Budget

    $5,000 USD

  • Last Updated

    3 Jun 2024

Milestone 1 - Market Research

Description

Comprehensive research on the current landscape of AI-driven health and longevity applications to identify key challenges related to data reliability and credibility. Review of existing solutions and technologies addressing similar issues.

Deliverables

Research report outlining the key challenges existing solutions and gaps in the current landscape of AI-driven health applications.

Budget

$2,000 USD

Milestone 2 - Concept Development

Description

Develop use cases and scenarios where TrustLevel’s reputation protocols can enhance data credibility in health and longevity applications. Outline the technical requirements and architecture for integrating AI and TrustLevel's protocols.

Deliverables

Conceptual framework document detailing use cases and technical requirements.

Budget

$2,000 USD

Milestone 3 - Feasibility Study

Description

Assessment of the technical feasibility of the proposed solution including potential challenges and mitigation strategies. Estimation of the resources time and budget required for developing a proof of concept (PoC).

Deliverables

Feasibility report with a detailed assessment of technical feasibility and a roadmap for PoC development in the next phase.

Budget

$1,000 USD

Join the Discussion (4)

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4 Comments
  • 0
    commentator-avatar
    HenriqC
    Jun 8, 2024 | 3:07 PM

     What data exactly are we talking about ? I mean, do you refer to claims about causations/science? Or do you refer to the validity of products sold on the markets? Or do you refer to single data points provided by individuals (such as blood biomarkers)? Or do you refer to all data in general? At least, regarding the authenticity of data from individuals there is a real issue to be solved and actions towards a solution are (what I’ve heard of) currently mostly about verifying service providers. The problem is that inexpensive local or self-made measurements are in a disadvantageous position compared to the big established corporations, which means that the power concentrates and the most efficient solutions can´t be used optimally. So do you think there could be ways to dynamically estimate how reliable an individual person, action, sample or claim truly is at a given moment of time? Maybe something like the AI based consistency analysis/other dynamic measurements of how genuine the data delivered could be assumed to be at a given moment of time. If you can imagine any tools to tackle the issue it comes with great value.       Anyway, in more general sense, I like the philosophy of TrustLevel and applying the system to biodata and health applications would be super valuable.

  • 1
    commentator-avatar
    CLEMENT
    Jun 1, 2024 | 3:35 PM

    Hi Dominik. Great job ideating this. You may want to check out on one of the SNET spin offs, Rejuve.ai. I guess they do something close to what you intend to do. You may wamna look up what they already have done and use it to refine your project approach. Goodluck !

    Reply
    Upvoted by Project Owner
    • 0
      commentator-avatar
      Dominik Tilman
      Jun 3, 2024 | 1:21 PM

      Hi Clement, thank a lot for your comment! Yes, we are aware of what Rejuve is doing and see our approach as complementary. Jasmine (CEO of Rejuve) joined one of my DeepFunding/SNET Reputation Workshops earlier this year, so I had the chance to learn about their approach and roadmap.

      • 0
        commentator-avatar
        CLEMENT
        Jun 3, 2024 | 8:58 PM

        Nice. I wish you all the best. Kind regards ! Also, you are also welcomed to make comments on our team proposal as well https://deepfunding.ai/proposal/4757/  - AI4M (Enhancing Malaria Predictability using AI) https://deepfunding.ai/proposal/biotek-nexus-next-gen-biodiversity-conservation/  - BIOTEK NEXUS (Blockchain Biodiversity Conservation)

Reviews & Rating

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5 ratings
  • 0
    user-icon
    Max1524
    Jun 10, 2024 | 3:40 AM

    Overall

    4

    • Feasibility 3
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    Future impact

    Uneven and even inaccurate input data quality is always a problem for applications integrating high-tech AI. This proposal, as presented, can solve that problem (we will see later). The issue here is how the applied technology of this proposal will have a specific impact in the future. Because it seems to me that some opinions about this are still ambiguous.

  • 0
    user-icon
    Gombilla
    Jun 9, 2024 | 3:55 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Potential to reduce preventable disease occurence

    Integrating diverse sources of health data and ensuring their compatibility and accuracy requires sophisticated systems. I would recommend the team to always ensure oversight of this project as they continuously ideate on this. Also, achieving and maintaining high user engagement and adherence to personalized health protocols can be difficult, especially without direct supervision from healthcare professionals. The team should try to integrate a section that would allow supervision from health professionals. 

    Generally, I like this proposal because it has the potential to revolutionize healthcare by shifting towards data-driven, personalized health management, potentially reducing the incidence of preventable diseases. Goodluck with this !

  • 0
    user-icon
    CLEMENT
    Jun 1, 2024 | 3:41 PM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    Can improve overall health outcomes and longevity

    In an era where health information can often be inconsistent or contradictory, providing users with personalized health protocols grounded in solid evidence can have a profound impact on improving overall health outcomes, longevity, and disease prevention. I believe is the stand out point of this idea.

    This project can as well contribute to the SNET AI Marketplace, by offering a unique and valuable service, leveraging TrustLevel's expertise in data integrity and Lennart's knowledge in biohacking. Users can access evidence-based health protocols tailored to their individual needs, enhancing the marketplace's offerings in the healthcare sector.

  • 1
    user-icon
    Tu Nguyen
    May 31, 2024 | 9:49 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Evidence-Based Health Data For AI Longevity Apps

    This proposal will address inconsistent data quality, lack of transparency, and difficulty assessing the reliability of data sources in the rapidly growing field of AI-driven health and longevity applications . This is a very real problem.
    There is one piece of information that they should add. Those are the start and end times of the milestones.

  • 0
    user-icon
    Joseph Gastoni
    May 22, 2024 | 9:14 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    an ideation phase for combining TrustLevel\'s data.

    This proposal outlines an ideation phase for combining TrustLevel's data integrity protocols with AI for personalized health and longevity apps. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Moderate: The core activities (market research, concept development) are feasible with a well-defined plan.
      • Strengths: Leverages existing expertise in data reliability and biohacking.
      • Weaknesses: Technical integration of TrustLevel protocols with AI for health applications might be complex.

    Viability:

    • Moderate: Success depends on the strength of the market research, the feasibility of the technical approach, and the development of a sustainable business model.
      • Strengths: The proposal addresses a growing concern about data quality in AI-driven health apps.
      • Weaknesses: The proposal lacks details on the target market, pricing strategy, and competition in the personalized health app space.

    Desirability:

    • Moderate-High: For users seeking personalized and evidence-based health recommendations, this could be desirable.
      • Strengths: The proposal offers a potentially valuable tool for improving the reliability of AI-driven health apps.
      • Weaknesses: The proposal needs to address concerns about privacy and data ownership in the context of health information.

    Usefulness:

    • Moderate-High: The project has the potential to improve the quality of AI-driven health apps, but its impact depends on the effectiveness of the final product and its accessibility to users.
      • Strengths: The proposal offers a way to increase trust in AI-driven health recommendations.
      • Weaknesses: The proposal lacks details on how the effectiveness of the solution will be measured and how it will address potential regulatory hurdles in the health app market.

    Overall, this ideation phase has a promising approach, but focus on:

    • Market Research: Conducting thorough research on the current landscape of AI-driven health apps, user needs, and existing solutions.
    • Technical Feasibility Study: Evaluating the technical challenges of integrating TrustLevel protocols with AI for health applications.
    • Business Model Development: Defining a clear target market, pricing strategy, and plan for long-term sustainability.
    • Regulatory Considerations: Addressing potential regulatory hurdles associated with health data and personalized health recommendations.
    • Privacy and Ethics: Ensuring strong data privacy practices and addressing ethical concerns about AI-driven health advice.

    By addressing these considerations, this "TrustLevel + Longevity AI" ideation phase can increase its chances of success and lead to a strong proposal for the next Deep Funding round.

    Here are some strengths of this project:

    • Addresses a critical need for improving data quality and reliability in AI-driven health applications.
    • Combines expertise in data integrity with knowledge of biohacking and longevity trends.
    • Recognizes the limitations of current AI-driven health apps and emphasizes evidence-based recommendations.

Summary

Overall Community

4

from 5 reviews
  • 5
    0
  • 4
    5
  • 3
    0
  • 2
    0
  • 1
    0

Feasibility

3.8

from 5 reviews

Viability

3.6

from 5 reviews

Desirabilty

4

from 5 reviews

Usefulness

4

from 5 reviews

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