By Rahul Yadav, CTO at Milestone Systems
Synthetic data, which refers to artificially generated data containing computer-generated information, offers a promising solution to address the training data scarcity issue. It allows researchers and developers to create diverse datasets that closely mimic real-world scenarios. Synthetic Data is a method used to generate data that retains the characteristics of real-world details without compromising the privacy of individuals in the sample videos.
In recent years, the healthcare sector has witnessed remarkable advancements in artificial intelligence (AI) and machine learning (ML) technologies. These technologies can potentially revolutionise patient care, diagnostics, and treatment planning. One challenge that impedes the progress of AI and ML projects in healthcare is the need for large amounts of accurately labelled data to train these solutions. This is where synthetic data has an important role to play.
Moreover, synthetic data provides complete control over the amount of data, scenarios, and environments included in the dataset. This eliminates the reliance on too few sources, which can result in biased information and inaccurate results. With synthetic data, researchers can generate vast datasets by altering various elements such as camera angles, lighting conditions, or even the physical characteristics of objects or patients. This versatility ensures a reduction in potential biases, making them more reliable in real-world applications.
Advanced tools, such as the Unreal Engine, a video game graphics tool recognised for producing realistic 3D visuals, can generate synthetic data for training healthcare solutions. The Unreal Engine uses graphics and physics engines to mimic scenes and real-world object interactions. Developers can use this technology to construct incredibly realistic environments where animated 3D objects and characters carry out diverse tasks. This makes it possible to create an almost endless synthetic dataset, each catering to specific use cases and circumstances.
The use of synthetic data offers significant cost savings. Traditional methods of acquiring and labelling real-world data can be expensive and time-consuming, requiring domain experts. Synthetic data eliminates the need for costly experts by automating the labelling process. As everything is simulated within the Unreal Engine, researchers can generate labelled datasets efficiently.
In the healthcare sector, synthetic data holds tremendous potential for various applications. It can be employed to train AI models, patient monitoring systems, predictive analytics, and more. By leveraging synthetic data, companies developing solutions for healthcare organisations can overcome the limitations of data scarcity, privacy concerns, and cost constraints, thereby accelerating the development and deployment of AI solutions for improved patient outcomes.
Ensuring AI solutions’ effectiveness, accuracy, and fairness requires a comprehensive strategy. This involves incorporating artificial data and implementing Model Operations (ModelOps) to enable ongoing monitoring throughout the entire life cycle of an AI solution. Adopting the ModelOps approach involved steering away from the traditional mindset of developing, deploying, and leaving the solution. Instead, ModelOps emphasises continuous monitoring, retraining, or replacement of the AI solution as needed. This process allows us to identify and address any unintended outcomes or side effects that may surface during real-world implementation. Healthcare organisations can leverage ModelOps to enhance the precision and fairness of the model, instilling greater confidence and dependability in AI-driven healthcare systems.
Synthetic data emerges as a crucial enabler of progress in the healthcare sector’s AI and ML applications. Healthcare organisations can effectively address data scarcity, privacy concerns, and cost limitations by leveraging synthetic data while training AI models. Combining synthetic data and ModelOps ensures improved performance, accuracy, and fairness of AI solutions throughout their lifecycle. As the healthcare industry continues to embrace the potential of AI, synthetic data will play a pivotal role in driving innovation, enhancing patient care, and advancing medical research.
