MedAI Network

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MedAI Network ICO details

Start Date: 2018-4-2

End date: 2018-7-31

    • Category: Health
    • Token: MedAI
    • Platform: Ethereum
    • Type: ERC20
    • Initial price: 1 MedAI = 0.10 USD
    • Accepting: BTC ETH
    • Soft cap: 4100000 USD
    • Hard cap: 34500000 USD

MedAI Network – The first Medical Imaging Storage Network Powered by Blockchain

The MedAI Network is a full service platform provider for telemedicine on the blockchain. Our platform, unlike any other, permits and supports the transfer, transmission, storage, interpretation, second opinion, and evaluation by artificial intelligence of medical data and medical images across all subtherapeutic areas and medical specialties. The MedAI network is not limited to one medical specialty or subspecialty. The network can support any remote consultation between patient, physician, institution or sponsor. The internet has matured to enabling patients to seek online consultations at their convenience from a geographic and timing perspective. The advent of over-the-counter tests to analyse your blood, sequence your genome or check on the bacteria in your gut is shifting the ability to obtain care into the patient’s hands. The development of technology is causing a shift. The growth of various technologies such as the smartphone and wearables are allowing patients to monitor and treat their own health. These technologies if coupled with access to your own medical records and the ability to share this information with those you trust amplifies and exponentially increases empowerment of the patient and provider. Technology allows a patient to reduce inefficiencies in their treatment and also provide data to help train medical algorithms.

A patient concerned their heart can now buy a watch which contains a medical-grade monitor that is able to detect an arrhythmias. There are apps that can diagnose skin cancer to assess for concussion to Parkinson’s disease. There is significant medical data being generated minute by minute which needs to be transferred, stored, and analyzed at medical grade. Although health records and imaging are evolving to being electronic, they are still generally inaccessible and trapped. In addition, most health records are in a format that machines cannot read. The MedAI network empowers future AI technology to, for example , provide automated medical evaluation and diagnosis from medical imagery and data provided through the network


Telemedicine also known as Telediagnostics, or remote diagnostic services, is the future of healthcare. This is especially evident in medical specialties that rely heavily on imaging such as radiology, dermatology, ophthalmology and pathology where diagnosis of illness is based on quantitative and qualitative interpretation of imaging. The shortage of doctors has resulted in the growth of technology providing services to telemedicine or remote care. The manpower shortage and the need for technology has seen the consolidation of groups, for example, in the U.S. with the largest groups having over 1000 physicians on staff. These groups have the ability to invest in technology to provide remote telemedicine as well as staffing to support the endeavor. As the volume of care has increased, providers are seeking the interpretation of images both by outsourced low-cost, distributed workers around the world as well as by artificial intelligence. In contrast, patients are looking for second opinions or opportunities to get evaluation of their conditions via a more secure, rapid and cheaper methodology. This trend isn’t in imaging based medical specialties alone, but in the general care of patients as the combination of progression of information technology and modern medical imaging has transformed the management of nearly all significant medical specialties.

MedAI Network


With the decentralization of diagnostic services,  there is an opportunity and a true need for a new kind of telemedicine platform – one which focuses on trust, speed, and cost-efficiencies for patients and providers. The current systems are slow, inefficient and expensive for patients. Blockchain technology provides a highly secure, decentralized framework for sharing medical images. Smart contracts enable us to run automated computer programs to enforce the validity of those medical images and provide an audit trail with no third-party intermediary involved. In typical telemedicine, clinical trial transfer or second opinion services, when patients share their medical images with a physician online, the process requires the involvement of a telemedicine or other intermediary company whose job is to transfer the medical imaging data, verify the transaction, and keep a register in their servers. This adds cost, insecurity and unnecessary overhead. With the use of the blockchain, there is no requirement to reimburse a third party intermediary company a percentage of  transaction fees for essentially only transmitting images. Thanks to cryptography, all medical imaging data transfers can be secured and signed while smart contracts can provide additional verification and analysis.


The use of blockchain for medical records provides significant security benefits that can serve to reduce data breaches as well as aid in sharing health care records between providers and patients. Currently, the way health records are stored and shared leaves much to be desired. The system is not efficient, fully secure,  nor user friendly. There are many issues that prevent the sharing of data primarily centered around the lack of secure centralized storage. The patient’s data is typically spread out over multiple providers’ systems. This leads to limited access for both caregivers as well as patients, but also leads to significant wastage in healthcare costs as imaging studies are often repeated. From a security standpoint, when data is disparate across multiple providers – there is ample opportunity for security breaches. The Health Insurance Portability and Accountability Act (HIPAA) was developed for security of health records and mandates all HIPAA covered entities maintain technical safeguards to ensure the confidentiality, integrity, and availability of protected health information. However, in the current iteration, each healthcare entity implements their own security controls which may vary from vendor to vendor.

By employing the blockchain for medical data storage, MedNetwork does not store the data in a single location. The blockchain keeps data in an encrypted ledger, which is distributed across redundant, replicated and synchronized databases. The decentralization of the data ensures security.  With blockchain, each data block in the chain is encrypted via public cryptography which is unlocked with the use of private keys or password which are held by the patient in the MedNetwork.

With the MedNetwork blockchain, any healthcare provider or sponsor is able to access the patient’s imaging data as the patient grants each access via their private key. The security in the key means that without the key, the data is secure and inaccessible. Each time new data is obtained, the MedNetwork validates the data and adds the data to the blockchain in chronological order with the blockchain ultimately comprising the patient’s entire medical history.

The use of the blockchain allows both benefits for the patient as well as the  provider. There is no necessity for providers to fax or send records between consultants or when a patient moves to a new physician. The new office could simply access the patient’s medical records from the MedNetwork blockchain. From the patient’s perspective, the blockchain makes it easier to access healthcare records. Instead of submitting an electronic or written request for copies of their health data across a set of different healthcare providers, the patient needs only to make one request and the full healthcare record is accessible. As any patient can attest to, the current process is complicated, time-consuming, and often very costly for the patient as  each provider is permitted under HIPAA to charge a fee for providing copies of data to the patient.

Platform demo video


Medicine has seen an explosive growth in imaging across multiple disciplines including Radiology, Dermatology, Ophthalmology and Pathology. As these specialties grow, there is a natural increasing need for more quantitative parameters in the care of the patient. The ability to implement these evaluations on imaging is well suited due to the data driven nature of this analysis method. The methodologies in some segments, however, are approaching the limits of human interpretation. The ability to create man-machine symbiosis an initial phase with a long term evolution to complete computer based evaluation is the current state of the environment. The benefits of computer based interpretation as both an adjunct to the human evaluation as well as for more quantitative tasks is significant. With both a reduction in error rate as well as the introduction of novel findings not humanly possible. The reduction in error is important in current health care systems as error is often cited as a leading cause of morbidity and mortality.

Recently, artificial intelligence with the advancements in deep neural networks have allowed the progression of computer vision to approach and in some cases surpass human capabilities in complex image based recognition tasks. The current iteration of computer aided diagnosis (CAD)  involves machines doing image analysis and identifying potential abnormalities in images (for example, lung cancer on an xray study) for the physician (Radiologist). In the past five years, we have seen the advent of machine learning and deep learning with convolutional neural nets and other sophisticated algorithms that are pushing past CAD and now providing insights that go beyond human capabilities. This is seen for example in the ability of algorithms to predict the probability of breast cancer on a mammogram.

Platform demo video

MedNetwork was established to apply this revolutionary technology across all visual based medical specialties. We have assembled a team of physicians, medical imaging AI experts, blockchain experts and healthcare industry leaders. Over the past 4 years,  MedNetwork has developed and demonstrated repeated excellence in machine vision, deep learning and algorithms for image recognition and diagnosis. Our algorithms surpass accuracy, specificity and sensitivity of other available offerings on the market. We have demonstrated our technology across various medical imaging applications. This more broad excellence allows MedNetwork to develop algorithms that span across precision medicine including genetics, radiomics and other novel diagnostics. By coupling these diagnostics with imaging and machine learning, MedNetwork is able to provide exceptional care for patients not only on diagnosis, but managing the longitudinal span of disease.

Technology developed in the MedNetwork labs currently is able to provide assistance to physicians in imaging such as Computed Tomography (CT), Magnetic resonance imaging (MRI), X-ray, Ultrasound, Mammography, Fundoscopy, Skin images, endoscopy and Nuclear medicine. Applications include detecting breast cancer, diabetic retinopathy, lung nodules, reading chest X-rays, and detecting malaria. Our products are deployed to an international base across the globe. These AI powered diagnostics are applied automatically to all the data on the MedNetwork by hosting algorithms in our smart contracts.

Telemedicine and blockchain share a common philosophy of empowering the individual. MedNetwork, by combining two transformative technologies – deep learning and blockchain, we empower patients, providers and sponsors by enhancing workflows all while reducing costs and providing better quantitative and qualitative analysis by leveraging AI diagnostics.

6.3 Total Score

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Santosh Bhavani


Qianyu Zhou

Deep Learning Scientist

Salem Karani

Deep Learning Scientist

Raghav Behl

Blockchain Developer

Andrey Men

Blockchain Developer

Adam Gradzki

System Architect

Indra Djackova


Amit Mehta


Tejas Mathai

Deep Learning Scientist

MedAI Network Advisors

Ravi Govindan


Concept validation and establishing industry partners
Q1 2018
MedAI Telemedicine MVP for telediagnostics Recruitment of physicians and imaging centers Token Pre-Sale for MedAI’s community whitelist
Q2 2018
Expand development team Integrate AI diagnostics into smart contract Public sale phase 1
Q3 2018
Product security audits Opening sales office in Houston, USA (Texas Medical Center) Paid pilot projects with providers for MedAI PACS/Viewer Integrate third party AI diagnostics Planned product releases: - MedAI Data Market
Q4 2018
MedAI Network public beta release Private blockchains for use in enterprise systems. Planned product releases: - MedAI PACS/Viewer - MedAI Diagnose - MedAI Telemedicine
Q1 2019
Additional 2FA integrations such as Whatsapp and WeChat to make telemedicine more consumer friendly Public testing - at this stage anyone can sign up to become a verified data host. Explore regulatory approvals for new diagnostics Planned product releases: - MedAI Trials
Q2 2019
Beta release of mobile app based on ​Toshi Opening sales office in Singapore: expand to Southeast Asia where there is a large need for AI diagnostics
Q3 2019
Javascript-based client libraries for medical imaging companies and web applications to leverage the MedAI Network for storage, AI diagnostics, and telemedicine
Q4 2019
Expand telemedicine partnerships in Southeast Asia
Q4 2019
Expand telemedicine partnerships in Southeast Asia

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