One of the biggest roadblocks in medical AI development is the lack of high-quality, diverse data for these technologies to train on.
What Is the Issue with Data Access?
Artificial Intelligence (AI) has emerged as a game-changer in the realm of medical imaging, with immense potential to revolutionize clinical practices. AI-powered medical imaging can efficiently identify intricate patterns within data and provide quantitative assessments of disease biomarkers. This technology not only enhances the accuracy of diagnosis but can also significantly speed up the diagnostic process, ultimately improving patient outcomes.
While the landscape is promising, medical innovators grapple with challenges in accessing high-quality, diverse, and timely data, which is vital for training AI and driving progress.
A 2019 study from the Massachusetts Institute of Technology found that over half of medical AI studies predominantly relied on databases from high-income countries, particularly the United States and China. If models trained on homogenous data are used clinically in diverse populations, then it could pose a risk to patients and worsen health inequalities experienced by underrepresented groups. In the United States, If the Food and Drug Administration deems these risks to be too high, then they could even reject a product’s application for approval.
In trying to get hold of the best training data, AI developers, particularly startups and individual researchers, face a web of complexities, including legal, ethical, and technical considerations. Issues like data privacy, security, interoperability, and data quality compound these challenges, all of which are crucial in the effective and responsible utilization of healthcare data.
One company working to overcome these hurdles in hope of accelerated and high-quality innovations is Gradient Health.
Gradient Health’s Approach
Gradient Health offers AI developers instant access to one of the world’s largest libraries of anonymized medical images, sourced from hundreds of global hospitals, clinics, and research centers. This data is meticulously de-identified for compliance and can be tailored by vendors to suit their project’s needs and exported in machine learning-ready DICOM + JSON formats.
By partnering with Gradient Health, innovators can use these extensive, diverse datasets to train and validate their AI algorithms, mitigating bias in medical AI and advancing the development of precise, high-quality medical solutions.
Gaining access to top-tier data at the outset of the development process promises long-term benefits. Here’s how:
- Expand Market Presence: Access the latest cross-vendor datasets to develop medical innovations, expanding your market share.
- Global Expansion: Enter new regions swiftly with locally sourced data from your target markets, accelerating your global reach.
- Competitive Edge: Obtain on-demand training data for imaging modalities and disease areas, facilitating product portfolio expansion.
- Speed to Market: Quickly acquire data for product training and validation, reducing sourcing time and expediting regulatory clearances for faster patient delivery.
“After looking for a data provider for many weeks, I was not able to get even a sample delivery within one month. I was immensely glad to work with Gradient and go from first contact to final delivery within one week!” said Julien Schmidt, chief operations officer and co-founder at Mango Medical.
In recent years, medical AI has experienced significant growth. Innovations in medical imaging in particular have played a pivotal role in enabling healthcare professionals to identify diseases earlier and more accurately in patients with a range of conditions.
Gradient Health offers a data-compliant, intuitive platform for AI developers, facilitating access to the essential data required to train these critical technologies. This approach holds the potential to save time, resources, and, most importantly, lives.
More information about Gradient Health is available on the company’s website. They will also be exhibiting at RSNA 2023 in booth #5149 in the South Hall.