Medical Datasets for AI Model: Strategies for Access and Efficiency

The buzz surrounding AI’s integration into various industries, especially healthcare, is undeniable. AI’s potential advantages in healthcare, benefiting patients, providers, and insurers, have fueled a thriving healthtech sector and forecast a remarkable 38.4% CAGR growth in the AI healthcare market by 2030.

This exponential growth is tied to the pivotal role of data in AI solutions for diagnosing and treating diseases, ultimately enhancing precision and outcomes. However, healthtech companies commonly face a daunting challenge: accessing high-quality medical datasets.

Acquiring and using medical datasets for machine learning is a complex and costly task, riddled with technical and legal obstacles. This article aims to shed light on this issue and explore strategies to overcome these challenges. We’ll introduce you to three approaches for obtaining medical datasets and provide insights on overcoming the associated hurdles.

But first, let’s address a fundamental question:

What stands between healthcare ML and AI models and big data?

The shortest answer is data precision and its availability, but there’s more to the story. Let’s delve into these challenges in greater detail.

Data precision

In healthcare, data precision refers to the accuracy, reliability, and quality of the data used to train and test ML and AI models. Healthcare data is notorious for being noisy, incomplete, or error-prone. Patient records often contain mistakes, missing information, or inconsistencies. For instance, data might include non-clinical factors like patient diet and lifestyle, potentially leading to biased data, which in turn results in biased ML and AI models.

Data volume and availability

Another critical aspect of healthcare ML and AI is the availability of usable datasets. While healthcare generates vast amounts of data, managing it at scale can be a significant challenge. Storing and processing large healthcare datasets require robust infrastructure and computational resources. Moreover, access to comprehensive healthcare data can be limited due to legal, ethical, and organizational restrictions.
However, as we’ve mentioned, these are not the only challenges.

Data protection regulations

According to GDPR and HIPAA rules, healthcare institutions can only provide third-party access to patient medical data if records are anonymized or de-identified. This means either encrypting or permanently deleting all sensitive data, such as names, dates, locations, contacts, and IDs.
These requirements make it harder to find medical datasets for machine learning projects. As a company, you need to persuade and assist healthcare institutions in anonymizing or de-identifying their data in accordance with regulations. If a hospital gains no advantage from your custom app development, it has no incentive to invest time and resources in sharing this data.
Even if you decide to take this path, as a software developer, you need to:

• Study local data privacy regulations
• Find a collaborative way to acceptably de-identify records but remove the weight of the procedure from the data provider
• Spend hours and invest funds in de-identifying and storing the acquired data

As a result, data collection is still costly and resource-intensive.

Data labeling and interoperability

Even if you’re fortunate enough to find and access standardized and anonymized data, you still need to label data for use with an AI and ML learning model.
To provide this data, software development companies hire healthcare experts to manually label every image or record according to specific requirements. For example, suppose you’re developing AI-based image screening software to detect lung disease. In that case, you pay specialists to label thousands of X-ray images of healthy lungs and lungs affected by different conditions, such as tumors or tuberculosis.
Unsurprisingly, specialist healthcare services aren’t cheap due to their specific knowledge and expertise. With the WHO predicting a worldwide shortage of 18 million healthcare workers by 2030, the cost of such services will likely increase.
Furthermore, healthcare systems often employ disparate data standards and technologies, complicating data sharing and exchange among institutions. Achieving interoperability is paramount but entails yet another time-consuming and costly effort to harness the full potential of big data in healthcare AI.

Fortunately, there are strategies available to navigate these obstacles and obtain the necessary data for training and validating models.

Where to get big data for AI projects

When you need medical datasets for a machine learning project and want to avoid the time-consuming and expensive task of collecting data from scratch, you have three main options:

• Use synthetic data
• Use open data sources
• Use data from a third-party big data provider

So, let’s explore each of these strategies.

Synthetic medical data

Synthetic data is artificially generated data that simulates real-world patient information when access to large, diverse, or sensitive medical datasets is limited. This data typically consists of fictitious patient records, imaging, or other healthcare-related information.
Specialists use different approaches to synthesize data. One is to use random or rule-based data generation that lacks real data characteristics and statistical patterns to create an AI prototype or proof of concept. Alternatively, developers can use AI-based synthetic data generation to create entirely new data points while preserving the traits of the original dataset to create viable products.
Regardless of the method, synthetic data enables healthcare AI developers to train and fine-tune their algorithms effectively without compromising patient privacy or data security.
But this isn’t the only benefit of synthetic data: there are several more.

Benefits of synthetic medical data

Among the most prominent advantages of synthetic data are the following.

Improved ML model accuracy

By synthesizing data, developers can expand and diversify training datasets and avoid the challenges of working with real data.

• Synthetic data is high-quality and standardized, which helps models generalize better.
• By introducing new artificial data points, developers can avoid overfitting when a model performs well with training data but poorly on new data.
• Synthetic data helps to create and include diverse information, which allows models to better understand underlying patterns and relationships.

All of these things contribute to the accuracy and usefulness of AI-based apps.

Access to rare data

Data on rare diseases is naturally limited. However, synthetic data based on limited studies and real examples can provide a dataset big enough to train AI models.

In one example study, researchers generated synthetic data related to a rare ophthalmic disease, uveitis. More than 55% of randomly selected data samples were assessed as good or excellent, and AI diagnosis prediction accuracy was 80%.

Easy labeling and control

Fully synthetic data makes labeling easy, as it’s generated by pre-defined rules and can, therefore, be identified and labeled automatically. What’s more, engineers can easily control and adjust data by tweaking its generative rules.

Cost and time efficiency

As we’ve already seen, collecting and preparing real-world data is expensive and time-consuming. Synthesizing and processing data dramatically cuts the time and costs spent waiting for suitable cases, record anonymization, and standardization.

Still, synthetic data isn’t a magic bullet for every project, as it has some significant drawbacks.

Disadvantages of synthetic data

When using synthetic data, you need to be ready for the flaws that can hinder AI model training results.

A possible lack of realism

Synthetic data can miss essential nuances that exist in real-world data, particularly when the data generation model is poorly calibrated. This makes it less reliable in some cases.

Stricter validation requirements

As synthetic data reflects common patterns but can overlook subtle nuances, it’s hard to guarantee that an AI model trained on such data will perform well in the real world. This results in higher costs for companies at the testing and validation stage.

Limited data complexity

All data generation models and tools work on the same principle — the simpler the rules or patterns describing desired output, the more quality and accurate results they provide. This limits reliable synthetic data to simpler datasets: if an AI project needs more complex data, synthetic results can be disappointing.
Still, if synthetic data fits your project needs, you can use some of these resources for your projects.

Resources to use

Consider these websites when looking for synthetic data:

• Synthea is a repository that generates synthetic patient medical histories.
• The Office of the National Coordinator for Health Information Technology (ONC) is an open-source synthetic data search engine that provides high-quality data for pediatrics, care use cases, and opioid addiction.
• The US Department of Veteran Affairs provides synthetic medical data on veteran health.
• Simulacrum is a synthetic database on population cancer cases.

You can find more repositories online or use synthetic data engine tools to generate your own data according to your project requirements.

Open data platforms

Another way to acquire big data for AI healthcare projects is to use openly available sources. Open data platforms collect extensive medical data from hospitals, clinics, healthcare institutions, and research studies and remove personally identifiable information before making the data available to all. These sources have significant advantages.

Benefits of open data platforms

The following advantages speak in favor of open data platforms:

Fast access to data

Since open data is already aggregated and de-identified, half of the data preparation is done, and you don’t need to spend months collecting and anonymizing patient records.

Real-life data

Unlike synthetic data, open data reflects the real facts, nuances, and patterns of researched healthcare issues. Many platforms, such as the Healthcare Cost and Utilization Project (HCUP), collect records nationwide, so the size and diversity of datasets are enough for training ML and AI models on common healthcare issues.

Free access

Platform data is publicly available, which means you pay nothing for the collecting and anonymizing stages. This advantage can significantly reduce the cost of your ML and AI projects.

Drawbacks of open data platforms

Despite the above-mentioned key advantages, open-data platforms also have drawbacks that can make them less useful in some cases.

Limited data on rare issues

Finding consistent and comprehensive datasets on rare diseases, conditions, and healthcare issues can be difficult. In this case, you may still need to synthesize data to create a sufficient dataset.

Unsystematized raw data

Even though you skip the task of data anonymization, you still need to process and label data to fit your project requirements. This task can be especially challenging if you combine data in different formats from multiple platforms.

Platforms to use

If you’re prepared to work with these challenges, you can start looking for open data on the following platforms:

• Sci2sci is a platform with an advanced dataset search engine that finds open datasets on the Internet.
• The Global Health Observatory (GHO) is the World Health Organization’s collection of health statistics on topics ranging from child health and immunization to mental health and nutrition.
• The Cancer Imaging Archive (TCIA) is an open-access hub of radiology and histopathology images founded by the US National Cancer Institute (NCI).
• The Healthcare Cost and Utilization Project (HCUP), the US’s largest publicly available collection of inpatient care data, is suitable for training models to predict things such as the risk of disease, length of hospital stay, chance of rare disease, and healthcare costs.
• The UK Biobank is a large-scale study that collects health and genetic data from half a million participants in the United Kingdom.
• The National Health and Nutrition Examination Survey (NHANES) is a US government-supported program that collects health and nutrition data from the US population.

If neither synthetic nor open data fits your needs, you can try a third option.

Big data providers

One of the easiest ways to acquire medical datasets for machine learning projects is to purchase them from specialized data providers that collect, aggregate, and process healthcare-related information.
These providers collect datasets from claims data, clinical records, pharmacy data, and patient surveys and then de-identify, standardize, integrate, and systematize them. Opting for a third-party data provider has several major benefits.

Benefits of big data providers

Data providers’ advantages manifest themselves in the following:

Processed and systemized datasets

Big data providers make it easy to buy a standard set of processed data from their base. In many cases, such sets are ready-to-use, and you need minimum time and resources to label and adjust them for your project.

Flexibility

If you need additional data processing, such as reformatting or labeling, some providers can help you with it to save time. This means you get everything necessary to start AI model training ASAP.
But there is also a flaw in this way of collecting data.

A drawback of big data providers

The only drawback of opting for a data provider is the cost of their service. If your budget is tight, you might find that accessing raw data from open sources and processing it is more cost-effective. Alternatively, if you’re working on a non-profit or publicly beneficial project, you can also find non-profit providers offering datasets for free.

The big data providers you might choose

Here are some of the big data providers to consider:

• Data4Life is a non-profit organization offering health data ready for research in the areas of public health and personalized medicine.
• Optum, a subsidiary of UnitedHealth Group, provides access to its claims, clinical, and pharmacy data for research and development purposes.
• Merative (formerly IBM Watson Health) helps users make sense of their own data and offers healthcare data sets covering enterprise imaging, healthcare analytics, and fully adjudicated claims data.

Big data giants such as IBM, Google, and Microsoft Azure might also be helpful in your search for medical data or its processing.

Conclusion

Big data is the lifeblood of any successful AI-driven healthcare project. However, procuring medical datasets for machine learning is a major challenge due to strict privacy regulations and the time and resources required for data preparation. Indeed, finding reliable data for AI & ML projects is always about balancing speed, process complexity, and expenses.
Fortunately, there are practical ways to overcome these hurdles — synthetic data, open data platforms, and specialized data providers. Each option has advantages and drawbacks, which you’ll need to weigh up carefully according to your project’s goals and constraints. Depending on your needs, you can choose to use one of these methods solely or combine them for optimal results.
At Unicsoft, we’re here to make this process easier. As an experienced provider of healthcare and AI-based solutions, we can help you choose the most suitable approach and focus on what really matters — developing a reliable and trusted healthcare solution for your market.

Just contact us to start a conversation.