SkinScanAI

chevron-icon
Back
project-presentation-img
Presentation
Expert Review🌟
Ly Vu
Project Owner

SkinScanAI

Funding Requested

$40,000 USD

Expert Review
Star Filled Image Star Filled Image Star Filled Image Star Filled Image Star Filled Image 5
Community
Star Filled Image Star Filled Image Star Filled Image Star Filled Image Star Filled Image 4.4 (10)

Overview

Our service, SkinScanAI, is designed to assist users in capturing images of their skin and analyzing skin lesions using computer vision techniques. It provides a convenient and accessible solution for individuals who want to monitor their skin health or seek preliminary assessments of suspicious lesions. Moreover, our service plans to applied new Generative AI techniques for the skin lesion detection. The service encourages users to consult with a medical expert for a comprehensive diagnosis and appropriate treatment, especially if the analysis reveals suspicious findings.

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

SkinScanAI demonstrates its value in skin lesion analysis, which is potential for integration with existing healthcare systems. This integration can facilitate seamless information flow between AI platforms and healthcare providers, enabling AI-assisted decision support and enhancing clinical workflows. The successful implementation of SkinScanAI can serve as a case study for the integration of AI platforms in healthcare settings, promoting the adoption and expansion of AI in the medical field.

Our Team

Our team includes the following members:

  • 01 AI research expert, who has experience working with national level AI projects. She has also a member who have projects funded in DF-R3

  • 01 Data analysis who obtained the certificate on Data Analysis of Google.

  • 02 IT experts: One member has many years of full stack programming, the other is an expert in blockchain

View Team

AI services (New or Existing)

SkinScanAI

Type

New AI service

Purpose

The purpose of SkinScanAI is to provide individuals with a reliable and accessible tool for skin lesion detection and analysis. By leveraging advanced deep learning and generative AI techniques SkinScanAI aims to enhance early detection of potential skin conditions including benign and malignant lesions. The service empowers users to proactively monitor their skin health offering accurate assessments and valuable insights.

AI inputs

The input to the SkinScanAI service is a digital image of a skin lesion. Users can capture and upload images of their skin lesions using a smartphone or any other digital imaging device. The image should clearly depict the skin lesion of interest.

AI outputs

SkinScanAI detects the presence of a skin lesion in the uploaded image and classifies it into various categories such as benign or potentially malignant. This information helps users understand the nature of the lesion and its potential risks.

Company Name (if applicable)

Cardano2vn.io

The core problem we are aiming to solve

The core problem that SkinScanAI aims to solve is the early detection and assessment of skin lesions, particularly those with potential malignancy. Skin cancers, including melanoma, can be life-threatening if not detected and treated early. However, diagnosing skin lesions accurately can be challenging, even for experienced dermatologists, leading to potential diagnostic errors and delays in treatment.

SkinScanAI addresses this problem by leveraging deep learning algorithms to analyze images of skin lesions. It assists users in capturing high-quality images of their skin and provides a preliminary assessment of the lesions. The skin lesion data is augmented by Generative AI techniques. By trained on a huge skin dataset, the system identifies potential areas of concern which may indicate a higher likelihood of malignancy. By providing users with a preliminary assessment, SkinScanAI empowers individuals to seek timely medical attention and consult or healthcare professionals for further evaluation and appropriate treatment if necessary.

Our specific solution to this problem

Introducing SkinScanAI, an innovative solution that harnesses the power of deep learning to revolutionize skin lesion analysis. Our service combines cutting-edge technologies, including convolutional neural networks (CNNs) and transfer learning, generative AI to provide accurate and reliable detection and assessment of skin lesions. With a vast and diverse dataset of annotated skin lesion images, our deep learning models will been trained to recognize patterns and features indicative of various skin conditions, including potentially malignant lesions.

We first need collect public skin lesion datasets. Then, to augment these datasets, we will survey the AI generative models for data synthesize skin lesion images. Additionally, we need to invite a skincare speciallist to verify our synthesized data. After having a large datasets, we do survey  Deep neural network models for the skin lesion detection problem. Then, we do several rounds of training and evaluation processes. When we have the best skin lesion deteciton models, we will deploy to the Deep Funding service. Finally, we will do marketing activites to broadcast our service and receive the user responds to improve our service.

Project details

Our specific solution as following:

  1. Problem Analysis: R&D new Generative AI techniques for data synthesis and Deep Learning techniques for skin lesion detection.
  2. Preparing datasets: We survey the skin lesion public datasets which are verified by experts.
  3. Using Generative AI techniques to synthesize skin lesion data to augment training datasets.
  4. Invite specallist of skin healthcare field to verify the synthesized data by Generative AI.
  5. Augment the synthesized data and skin lesion public datasets to form a training dataset.
  6. Train and Evaluate AI models for skin lesion detection.
  7. Deploy AI models to Deep Funding service
  8. Do Marketing activities to broadcast the service and receive the user responds.

Competition and USPs

SkinScanAI leverages state-of-the-art generative AI algorithms to not only detect and classify skin lesions but also generate synthetic examples of potential lesions. This unique approach allows SkinScanAI to simulate and analyze a wide range of skin lesion variations, enabling the system to detect even rare or unusual cases that may be missed by other services.

With SkinScanAI, users can confidently upload their skin lesion images, knowing that the system's generative AI models have been trained on a diverse dataset that encompasses a wide spectrum of skin conditions. The models can accurately identify common lesions, as well as capture the subtle nuances and variations that may be indicative of early-stage malignancy. Furthermore, SkinScanAI's generative AI techniques empower users with visual comparisons and explanations, helping them develop a better understanding of their skin condition and potential risks.

Open Source Licensing

GNU GPL - GNU General Public License

Our code and userguide will be public under General Public License (GNU) .

Revenue Sharing Model

API Calls

API Description:

we will onboard this service on the Deep Funding platform and share the profit of our service for API calls.

API Revenue Service

10000

API Revenue Percentage

15

API Revenue Year

2025

Proposal Video

DF Spotlight Day - DFR4 - Bien Dao - SkinScan AI

3 June 2024
  • Total Milestones

    6

  • Total Budget

    $40,000 USD

  • Last Updated

    3 Jun 2024

Milestone 1 - API Calls & Hostings

Description

This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.

Deliverables

You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.

Budget

$10,000 USD

Milestone 2 - Project Management and Data Collection

Description

Project management and the data collection milestone play crucial roles in the development of the SkinScanAI project. Effective project management ensures that the development process progresses smoothly resources are allocated efficiently and timelines are met. Expectation time for this milestone is 6 weeks.

Deliverables

Project Management Task: 1. Defining Project Scope: Clearly define the goals objectives and scope of the SkinScanAI project. 2. Establishing Project Timeline: Create a detailed timeline with milestones and deadlines for each phase of the project including data collection model development user interface design testing and deployment. 3. Resource Allocation: Identify and allocate the necessary resources including human resources budget computing infrastructure and software tools required for the project. 4. Team Coordination: Assemble a competent and interdisciplinary team including data scientists machine learning experts software developers UI/UX designers and domain experts in dermatology. 5. Risk Management: Identify potential risks and develop mitigation strategies to address them. Data Collection Milestone Task: 1. Defining Data Requirements: Determine the specific types of skin lesions to be included in the dataset. 2. Data Source Identification: Identify potential sources for collecting skin lesion images. 3. Data Preprocessing: Preprocess the collected images by resizing normalizing and augmenting the dataset. 4. Data Quality Assurance: Implement quality checks to ensure the accuracy and consistency of the annotated dataset. 5. Dataset Documentation: Maintain proper documentation of the dataset.

Budget

$5,000 USD

Milestone 3 - Data Generation by Generative AI

Description

The generation of training images by Generative AI is a vital component in the SkinScanAI project offering significant benefits to the development and performance of the service. By leveraging Generative AI techniques the project can augment the available dataset and overcome potential limitations in data scarcity or imbalances. Moreover, we need to collaborate with skincare specialists to verify the generated skin lesion images. Expectation time for this milestone is 6 weeks.

Deliverables

Generating skin images using Generative AI involves several steps: (1)Model Selection: Choose an appropriate Generative AI model for the task such as a Generative Adversarial Network (GAN) or a Variational Autoencoder (VAE) and their variants. Consider factors like model architecture complexity and the ability to generate realistic and diverse skin images. (2)Training the Generative AI Model: Train the selected model using the prepared dataset of real skin images. The model learns the underlying patterns and features of the skin lesions present in the dataset. The training process involves optimizing the model's parameters to generate synthetic skin images that closely resemble real ones. (3) Validation and Testing: Validate the trained Generative AI model by comparing the generated skin images with real skin images from the dataset. Assess the model's ability to capture the characteristics variations and realistic appearance of different skin lesions. Moreover we need to conduct thorough testing to ensure the generated images align with the intended purpose and requirements of SkinScanAI by working with skincare specialists. (4) Post-processing and Enhancement: Apply post-processing techniques to the generated skin images if necessary. This may involve adjusting the contrast brightness or noise levels to make the synthetic images more visually consistent with real skin images.

Budget

$6,000 USD

Milestone 4 - Building Skin Lesion Detection Models

Description

The goal is to build a model that generalizes well to unseen skin lesion images demonstrating adaptability to various skin types lesion variations and imaging conditions. Real-time performance is another key objective ensuring that the model can provide rapid analysis of skin images facilitating quick decision-making and potentially enabling early intervention when necessary. Expectation time for this milestone is 8 weeks.

Deliverables

Building the skin lesion detection model includes following steps: (1) Model Selection: Choose an appropriate deep learning model architecture for lesion detection (2) Model Training: Train the selected model using the annotated dataset. This involves feeding the images as input and optimizing the model's parameters to learn the patterns and features of skin lesions. Training may employ techniques like data augmentation to increase the dataset's size and diversity improving generalization. (3) Model Evaluation: Evaluate the trained model's performance using a separate validation dataset. Measure metrics such as accuracy precision recall and F1-score to assess its ability to detect and classify skin lesions accurately. Iterate on the model and training process if necessary to improve performance. (4) Fine-tuning and Optimization: Fine-tune the trained model by adjusting hyperparameters optimizing the learning rate or using regularization techniques to improve generalization and minimize overfitting. This step helps ensure the model performs well on new unseen data. (5) Validation and Testing: Validate the model's performance on an independent testing dataset. Assess its ability to generalize to real-world skin images including different imaging conditions and variations. Validate the model's accuracy efficiency and robustness for reliable lesion detection.

Budget

$7,000 USD

Milestone 5 - Deploying Service

Description

The aim of the deploying service milestone in SkinScanAI service is to successfully release the developed system i.e. the lesion detection AI model and make it available to users while ensuring its stability scalability and usability. Expectation time for this milestone is 4 weeks.

Deliverables

Deploying the service milestone for SkinScanAI involves several important steps: 1. Infrastructure Setup: Set up the necessary infrastructure to host and run the SkinScanAI service. 2. Environment Configuration: Set up the software environment needed to run the SkinScanAI service. This involves installing and configuring the required frameworks libraries and dependencies on the Deep Funding service. 3. Database Integration: Set up a database system to store and manage user data securely. Design and implement an appropriate database schema to efficiently store user information including uploaded images analysis results and user preferences. 4. Service Deployment: Deploy the SkinScanAI service onto the configured infrastructure. 5. Testing and Quality Assurance: Conduct thorough testing of the deployed service to ensure its functionality performance and reliability. Test different use cases simulate user traffic and identify and resolve any bugs or issues that may arise. Perform regression testing to validate that existing features and functionalities work as expected after deployment. 6. Monitoring and Performance Optimization: Set up monitoring tools and performance metrics to track the service's performance. 7. User Onboarding and Documentation: Prepare user documentation and guides to facilitate user onboarding.

Budget

$7,000 USD

Milestone 6 - Marketing strategy

Description

The marketing activity milestone is of utmost importance in promoting and establishing the success of any product or service including SkinScanAI. This milestone focuses on creating awareness generating interest and driving user adoption of the SkinScanAI service. Expectation time for this milestone is 4 weeks.

Deliverables

The marketing activities milestone involves several key steps to effectively promote the SkinScanAI service and drive user adoption: 1.Market Research: Conduct market research to gain insights into the target audience competition and industry trends. Identify the key demographics pain points and preferences of potential users to tailor marketing strategies accordingly. 2.Develop a Marketing Plan: Create a comprehensive marketing plan that outlines the strategies tactics and channels to be utilized. Define the messaging positioning and unique selling points of SkinScanAI. 3.Content Creation: Develop compelling and informative content to engage and educate the target audience. This can include blog posts articles videos infographics or case studies that highlight the benefits features and success stories of SkinScanAI. Website Optimization: Optimize the SkinScanAI website for search engines (SEO) to improve its visibility and organic search rankings. 4.Digital Advertising: Implement targeted digital advertising campaigns to reach potential users. Utilize pay-per-click (PPC) advertising display ads social media advertising and other digital channels to raise awareness and drive traffic to the SkinScanAI website. 5.Social Media Engagement: Leverage social media platforms to engage with the target audience and foster brand awareness. 6.Performance Tracking and Analysis: Continuously monitor and analyze the performance of marketing activities.

Budget

$5,000 USD

Join the Discussion (3)

Sort by

3 Comments
  • 0
    commentator-avatar
    Gombilla
    Jun 2, 2024 | 10:57 AM

    Hi there. How do plan on handling issues related to data privacy especially as regards patient clicnial info. There could also be concerns around accuracy of diagnosis, and reliance on AI for medical decision-making in this case. How do you approach this ?

  • 1
    commentator-avatar
    HenriqC
    May 19, 2024 | 2:16 PM

    This service itself would be a great add to the SNET platform.       I believe it might be pretty hard to gain market share as an end-user service. AI based image recognition popularized quickly after the introduction of convolutional NNs and there has evolved highly competed markets in this regard. I know you are probably well aware of your competitors but just wanted to point out that for example SkinVision has millions of users and the suspicious images go through dermatologists’ reviews. Winning customers may not be easy.          Good news is that there are tons of new healthcare apps entering the markets, many of which are so called web3 natives. They are definitely looking for openly available specialized AI solutions for different tasks and launching a working SkinScanAI on the marketplace would put you in a good position, in my opinion.   

    • 0
      commentator-avatar
      Debian Tao
      May 25, 2024 | 3:52 AM

      In terms of the market, in the initial phase, we will focus on emerging markets (countries that are developing, where access to high-quality healthcare is limited for many people, and where smartphones and internet usage are quite prevalent). Because these countries have a customer base with average income levels, they are increasingly interested in health and have a habit of searching for information about various diseases and their treatments online (Vietnam is an example).

Expert Review

Overall

5

user-icon
  • Feasibility 5
  • Viability 5
  • Desirabilty 5
  • Usefulness 5
Potential to enhance clinical workflows & decision

SkinScanAI is a highly useful, and viable project that leverages advanced AI techniques to provide accessible and preliminary skin lesion analysis. The service addresses a significant need for early detection and monitoring of skin health, with the potential to enhance clinical workflows and decision support in healthcare settings. The primary challenges include ensuring accuracy, regulatory compliance, and building trust with users and healthcare providers. With effective implementation and integration, SkinScanAI can significantly impact the adoption and expansion of AI in the medical field, improving patient outcomes and promoting innovation in healthcare.

Sort by

10 ratings
  • 0
    user-icon
    Duke Peter
    Jun 9, 2024 | 1:52 PM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Transforming HealthCare Processes

    SkinScanAI, a cutting-edge AI-powered service, addresses the critical need for early detection and assessment of skin lesions, a potentially life-threatening condition if not diagnosed promptly. By leveraging state-of-the-art generative AI algorithms, SkinScanAI not only detects and classifies skin lesions but also generates synthetic examples of potential lesions, a distinctive feature that sets it apart from other services. Additionally, its potential integration with existing healthcare systems enables seamless information exchange between AI platforms and healthcare providers. Furthermore, the successful implementation of SkinScanAI can pave the way for the integration of AI platforms in healthcare settings, serving as a valuable case study and promoting the adoption and expansion of AI in the medical field.

  • 0
    user-icon
    Onize Olie
    Jun 6, 2024 | 10:56 AM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    Revolutionizing Clinical Healthcare!

    SkinScanAI stands out in clinical medicine due to its innovative use of generative AI algorithms. Unlike other tools that classify skin lesions, SkinScanAI goes further by generating synthetic examples of potential lesions. This allows it to simulate and analyze a wide range of variations, enabling the detection of even rare or unusual cases that other services might miss.

    As a former health scientist, I appreciate that SkinScanAI has been trained on a diverse dataset encompassing a broad spectrum of skin conditions. This gives me confidence that it can accurately identify common and atypical lesions, including those that may indicate early-stage malignancy. The ability to capture subtle nuances and variations is crucial for early detection and intervention.

    Additionally, SkinScanAI\'s generative AI techniques provide visual comparisons and explanations, which can be invaluable for patient education and engagement. By helping patients better understand their skin condition and potential risks, SkinScanAI empowers them to make informed decisions about their health.

    Overall, SkinScanAI\'s unique approach to skin lesion analysis and its focus on patient education make it a valuable tool for clinical medicine. Its ability to detect various skin conditions, including rare and unusual cases, sets it apart from other available services.

  • 0
    user-icon
    Robert Haas
    Jun 6, 2024 | 7:31 PM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Allowing anyone to detect malignant skin lesions

    I think this proposal aligns very well with SNET's vision of democratic, beneficial, and decentralized AI: It democratizes the capability of classifying skin lesions into benign and malign ones by providing it as a decentralized service available to anyone with a phone and internet access. This is beneficial in the sense that it can enable people with limited access to high-quality healthcare to discover potential skin cancer early on, which can make large difference in how well it can be treated.

    On a technical level, the strategy for data collection, augmentation and verification seems reasonable to me. The tool uses standard DL methods and will be open source under a GNU license. What's not entirely clear to me is whether the collected and generated dataset will also be open sourced.

  • 0
    user-icon
    Vuthuthuy031096
    Jun 7, 2024 | 3:12 AM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    Assess skin damage using AI technology

    I appreciate the feasibility and high applicability of the SkinScanAI tool, it is really useful in affecting human health. The tool directly empowers people to proactively monitor their skin to know if their skin is experiencing serious diseases so they can promptly treat it. The tool is developed with a vast dataset of images and annotations that will help increase reliability and accuracy to attract user outreach and adoption. If this project is successfully implemented, I think this is a very right step, helping to promote the application and expansion of AI in the medical field.
    1. Feasibility:
    I think this project is completely feasible after reviewing the team and implementation plan. The team members are all experts, so developing tool functions such as imaging, analysis, and reporting is completely feasible. The team provided a fairly detailed analysis of the project implementation plan, complete with task duration and reasonable budget allocation.
    2. Viability:
    If a project wants to grow and survive in the long term, the SkinScanAI tool must have accuracy, ease of use, and user acceptance. In my personal opinion, the project's tool is combined with advanced technologies of convolutional neural networks (CNN) and transfer learning, synthetic AI, and has a large and diverse data set about different topics. Annotated images of skin lesions increase the ability to detect and provide accurate assessments with a high level of confidence. The project thoroughly addresses the need to detect skin diseases quickly and effectively, and especially to detect the possibility of skin cancer to help people provide timely treatment.
    3. Desire:
    For people concerned about skin diseases and dermatologists, this tool is desirable. Proposed to provide a convenient, fast, easy-to-use tool that can provide treatment for skin diseases.
    4. Usefulness:
    I believe this is a tool trusted by dermatologists and people interested in skin diseases. The tool helps people easily take photos of their skin and get preliminary analysis of dermatological diseases to provide timely care. At the same time, SkinScanAI is an established tool for integrating AI platforms in healthcare environments.

  • 0
    user-icon
    Tu Nguyen
    May 23, 2024 | 3:24 AM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    SkinScanAI

    This proposal will address the early detection and assessment of skin lesions, especially those with malignant potential. Skin cancer, including melanoma, can be life-threatening if not detected and treated early. However, accurately diagnosing skin lesions can be challenging, even for experienced dermatologists, leading to potential diagnostic errors and delayed treatment. This is a very real problem. Many patients are subjective with skin problems, leading to health consequences. This proposal will create an innovative solution that harnesses the power of deep learning to revolutionize skin lesion analysis. This is a very useful solution in practice.
    In addition, the members are experienced people. I believe they will complete this proposal well. Information about important milestones is quite detailed.

  • 0
    user-icon
    Ayo OluAyoola
    Jun 9, 2024 | 12:52 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 5
    • Usefulness 4
    AI For SKin Health.

    Desirability
    SkinScanAI offers a convenient and accessible solution for individuals to monitor their skin health and receive preliminary assessments of suspicious lesions. The ability to capture and analyze skin images using a smartphone will appeal to a broad audience, particularly those concerned about lesions where early detections can make all the difference.

    Usefulness
    The service is set to leverage advanced AI techniques to provide accurate assessments of skin lesions, potentially identifying malignant conditions early. By utilizing deep learning and generative AI, SkinScanAI can offer detailed and reliable analysis, aiding users in making informed decisions about their skin health. The integration with existing healthcare systems and the potential for AI-assisted decision support further increases its utility.

    Feasibility
    The feasibilityof this solution is supported by the expertise of the team, (although I would have loved to see more details as regards them) which includes AI researchers, data analysts, and IT experts with relevant experience. The project's reliance on publicly available skin lesion datasets and the use of generative AI to augment these datasets are practical approaches. However, challenges may arise in ensuring the quality and accuracy of synthesized data and in achieving high-performance AI models. I do hope the team is able to court and incentivize collaborations with skin healthcare specialists for data verification and rigorous training and evaluation of AI models will be crucial to the project's success.

    Viability
    SkinScanAI is viable, given its potential to fill a significant gap in early skin lesion detection and monitoring. The project's integration with healthcare systems and the potential for widespread adoption highlights its commercial potential. However, securing funding, effective marketing, and user acceptance are essential factors that will influence its long-term viability. Hopefully our team The MarketIn AI will be funded as well in this round, so we can support your project to reach critical mass adoption in record time.

    Summary
    SkinScanAI presents a valuable solution for early detection and monitoring of skin lesions, leveraging advanced AI techniques to provide reliable assessments. AI and deep learning enhance its competitiveness and potential for integration into healthcare systems. The service's viability hinges on successful funding, marketing, and user adoption. Overall, SkinScanAI holds promise as a valuable tool for improving skin health management and promoting early medical intervention.

  • 0
    user-icon
    CLEMENT
    Jun 2, 2024 | 11:06 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    Provides a solution for early lesion assessments

    I can reckon that SkinScanAI has the potential to have a significant impact on public health by empowering individuals to proactively monitor their skin health and seek medical attention when necessary. Like many other Healthtech solutions, it also combines cutting-edge technologies with a vast and diverse dataset of annotated skin lesion images to enhance the accuracy and reliability of skin lesion detection and assessment.

    I find this to be a sought after tool for professional dermatologitsts globally. Additionally, The integration of this service into the SNET marketplace expands its offerings in the healthcare and wellness category, providing users with access to AI-driven solutions for skin health management.

    Kudos to the team

  • -1
    user-icon
    Joseph Gastoni
    May 22, 2024 | 1:02 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    mobile app (SkinScanAI) for skin lesion analysis

    This proposal outlines a mobile app (SkinScanAI) for skin lesion analysis using deep learning and generative AI techniques. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Moderate: Developing the core functionalities (image capture, analysis, reporting) is feasible, but generative AI for medical data requires careful consideration.
      • Strengths: The project leverages existing deep learning techniques and public datasets.
      • Weaknesses: Generating synthetic skin lesions raises ethical concerns and requires validation by dermatologists. Regulatory approval for such an AI-based medical tool might be challenging.

    Viability:

    • Moderate: Success depends on the accuracy of the AI model, user adoption, regulatory approval, and competition from existing skin cancer screening solutions.
      • Strengths: The proposal addresses a need for early skin lesion detection.
      • Weaknesses: The proposal lacks details on the business model and long-term sustainability.

    Desirability:

    • Moderate: For individuals concerned about skin lesions, this could be desirable.
      • Strengths: The proposal offers a convenient and accessible solution for preliminary skin analysis.
      • Weaknesses: The proposal needs to clearly emphasize that SkinScanAI is for informational purposes only and not a substitute for professional diagnosis.

    Usefulness:

    • Moderate: The project has the potential to raise awareness about skin cancer and encourage users to seek medical attention, but its impact depends on the accuracy and user trust.
      • Strengths: The proposal offers functionalities for image capture, AI-based analysis, and encouraging consultation with dermatologists.
      • Weaknesses: The proposal lacks details on how the app will address potential biases in the AI model and user education about the limitations of the technology.

    Overall, the SkinScanAI project has a good intention, but focus on:

    • Ethical Considerations: Clearly address the ethical concerns surrounding generative AI for medical data and ensure proper anonymization and privacy safeguards.
    • Regulatory Approval: Develop a plan for navigating the regulatory hurdles associated with medical AI applications.
    • User Education: Emphasize that SkinScanAI is for informational purposes only and users should consult a dermatologist for diagnosis and treatment.
    • Competition and USP: Provide a more comprehensive comparison with existing skin cancer screening solutions and highlight SkinScanAI's unique value proposition (e.g., generative AI for broader lesion detection).

    Strengths:

    • Leverages deep learning for skin lesion analysis.
    • Offers a convenient and accessible tool for preliminary skin analysis.
    • Aims to raise awareness about skin cancer and encourage users to seek medical attention.

    Weaknesses:

    • Needs to address ethical concerns around generative AI for medical data.
    • Faces challenges with regulatory approval for a medical AI application.
    • Lacks details on user education and business model.

  • 0
    user-icon
    Tạ Thanh Tùng
    Jun 9, 2024 | 7:25 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    The “SkinScanAI” is development of AI technology

    Feasibility:4
    Feasibility of the project is demonstrated through presenting the project with easy-to-understand, reasonable arguments. The development and success of this project by introducing innovative AI services is in line with SingularityNET's mission of creating a democratic, decentralized and beneficial AI ecosystem. Specifically, here are health benefits for users, this is also an innovative AI service. Leverages advanced AI techniques to provide preliminary and accessible skin damage analysis. However, for the application and assessment to be highly reliable, it is necessary to have the presence of extremely medical experts with many years of experience in dermatology participating before the project begins. Hope the group finds it soon.

    Viability: 5

    Success depends on attracting and retaining users and having real-world effectiveness. With a team with a very good Ai background and experience working with national-level AI projects, they are also members with projects funded by DF-R3. There is a data analyst with a Google Data Analysis certificate and 2 experts with many years of full stack programming, one member is a blockchain expert. The budget allocated specifically for each phase meets the fund's internal requirements, specifically how long each milestone takes place. I appreciate that a project has a test run to ensure development capabilities because running a test first helps you identify and fix bugs, collect feedback from users, test features and performance, and build customer loyalty. user excitement and measure your app's potential performance before its official release.
    Desire: 4

    SkinScanAI aims to solve users' health needs. An issue that users are always interested in. Suitable for specific market needs is the early detection and assessment of skin lesions, especially those with potential malignant potential. Skin cancer, including melanoma, can be life-threatening if not detected and treated early. Whether or not you can compete and develop strongly depends on the effectiveness of the project. However, the image recognition AI market has also developed very strongly in the world, so choosing a market to deploy is also extremely important. I hope you will be more detailed about this issue.

    Usefulness:5

    The project has the potential to be enhanced when the best skin damage detection model is deployed to the Deep Funding service. Definitely carry out marketing activities to promote its services and get user feedback to improve the team's services. The successful project will be a great addition to the SNET platform, having a say in the health benefits aspect, not just AI technology.
    Overview: This is a great project that would be more perfect if the team could confirm that they had found the right medical experts and the user market the team is targeting. I hope the team can evaluate the specific performance of “SkinScanAI” through the following KPIs: user return rate, usage time, evaluating the time users spend on the application each time they use it and frequency of use. within a certain period of time. A long period of use and high frequency of use may indicate that users appreciate the usefulness of the application, Feedback from users from collecting direct feedback from users through surveys observation, interview or direct conversation. This feedback can provide specific and detailed information about what they love and don't love about the app or whether they are more interested in the SNet ecosystem. Strong. Finally, it is important to note that health information is often sensitive and private, so protecting it from unauthorized access is very necessary and extremely important. Health AI applications often must comply with legal regulations and information security standards such as HIPAA (Health Insurance Portability and Accountability Act) in the United States, etc. To ensure security, measures such as access control and version management should be noted to prevent unauthorized access and protect users' personal information. At the same time, providing clear and transparent information about how data will be used and secured will help create trust from users.

  • 0
    user-icon
    Nicolad2008
    Jun 8, 2024 | 3:42 PM

    Overall

    3

    • Feasibility 3
    • Viability 3
    • Desirabilty 3
    • Usefulness 3
    information security and privacy

    In today's world, information security and privacy issues are becoming a top concern, especially when applying AI technology to the medical field. Clearly defining how data is collected, stored and used is extremely important to ensure user trust and safety. Besides, approval from regulatory agencies is also a decisive factor, and overcoming legal and ethical barriers requires a specific and detailed plan. For a project like SkinScanAI, ensuring that the technology not only helps medical purposes but also adheres to ethical and legal principles is undeniable. Only when all these issues are comprehensively addressed can the project truly bring significant benefits to the medical community and users.

Summary

Overall Community

4.4

from 10 reviews
  • 5
    5
  • 4
    4
  • 3
    1
  • 2
    0
  • 1
    0

Feasibility

4.4

from 10 reviews

Viability

4.1

from 10 reviews

Desirabilty

4.5

from 10 reviews

Usefulness

4.7

from 10 reviews

Get Involved

Contribute your talents by joining your dream team and project. Visit the job board at Freelance DAO for opportunites today!

View Job Board