The technology developed by Humans.ai can help bring synthetic media into the realm of healthcare to address some of the major friction points in this industry. Humans.ai can facilitate the creation of artificially generated avatars that could act as test subjects in healthcare trials, streamlining the discovery of new treatments and the accuracy of disease detection.
The first thing that usually comes to mind when discussing about synthetic media and its effects on our lives revolves around the liberalization of content creation, namely how it can reduce the gap between idea and implementation by giving anyone the possibility to channel their creative spirit and express themselves in new artistic ways.
For the most part, you wouldn’t be wrong to think this way. But this would be a somewhat narrow view, as synthetic media can be used to serve a considerable number of industries with its ability to generate synthetic data that can stimulate new research and development. The healthcare sector, in particular, encounters numerous bottlenecks related to the amount of data available for research and drug testing, as data privacy concerns and reduced data sample sizes are some of the thornier issues that slow down developments in this area.
Positioning itself as a pioneer in the synthetic media sector, Humans.ai puts artificial intelligence and blockchain together to create a ripple effect that will lead to the democratization of content generation through the power of its AI technology. Our approach is to turn artificial intelligence from a technology that was reserved only for tech heads and large corporations who could afford the necessary resources to run and maintain their own AI into a technology that can be owned and put to use by everyone. With the technology developed by Humans.ai, people can use their biometric data to generate AI models that can bring real-world value without needing to worry about all the technological overhead and resources necessary to run their AI.
Synthetic data, as the name implies, is data that is generated artificially rather than being created by real events. One major benefit of synthetic data is the fact that it isn’t tied to a specific individual, which removes any privacy concerns, as is usually the case with patient health records that fall under the protection of regulations like The Health Insurance Portability and Accountability Act (HIPAA).
Synthetic data is created with the help of algorithms, and it is utilized in different industries and businesses for a wide range of activities, including test data for new products, model validation and AI training. Often used to generate data that imitates real-life scenarios, synthetic data opens up a seemingly limitless number of possibilities for testing and development.
Synthetic data vs real data
Although synthetic data started to be used in the early ’90s, it has witnessed a surge in popularity starting with the 2010s when the computing power and storage capabilities of computers reached a new level.
Some of the benefits of synthetic data over real data are:
- Fewer restrictions on data usage — real data is often accompanied by an abundance of constraints that stem from privacy rules and other regulations. In turn, synthetic data overcomes all these issues by managing to replicate all the important statistical properties of real data without breaching privacy and ownership.
- Supplementing the shortage of real data — data is frequently used for testing new products or treatments, but more often than not, industries are faced with small data samples. Synthetic data can address this information shortage while also providing data rarely encountered in real-life scenarios like in the case of rare diseases.
- Generating data for machine learning — machine learning algorithms require vast quantities of specialized data to process to detect patterns and learn. For example, the data needed to train the AI behind self-driving cars is very expensive to generate in real life. In this case, artificially generated data can decrease costs and generate enough information to facilitate the learning process.
Patient health records are essential for conducting patient-centred research, which focuses on enhancing the effectiveness of prevention as well as providing the best treatment options for each patient. The problem faced by healthcare research institutes is that accessing patient records is a difficult and costly endeavour as it raises several privacy concerns. Also, the lack of health data it difficult to research new treatment options, meaning that development in this area moves at makes a sluggish pace. Synthetic health data can help mitigate some of these pain points while assisting in the refinement of existing testing techniques, helping propagate new developments into the healthcare sphere.
Synthetic health data can mirror the particularities of a sample population of interest, people who suffer from diabetes, for example, or people who suffer from a rare condition and provide the necessary information samples for healthcare researchers to conduct their studies, test new ideas and hypotheses.
Synthetic health records are not the same as de-identified data or censored data, which are based on patient records that had their private information removed, instead, they are created from scratch using technology and confirmed based on real-world data to make it realistic. This means data generated this way can never be synced back to a specific individual or their health record.
Enhance diagnostics and machine learning accuracy
Machine learning and deep learning are subsets of artificial intelligence that have been utilized in the past years in numerous healthcare applications to improve medical imaging, patient data analytics, drug discovery, and so on. These mechanisms work by detecting patterns in health records to make accurate predictions regarding, for example, how patients would respond to treatment or how a disease would progress given a set of initial symptoms. Synthetic data can be used to generate an extensive data sample that can be fed to machine learning algorithms to enhance their accuracy, enabling physicians to make better decisions that don’t leave room for interpretation.
Enable the detection and analysis of rare diseases
Probably the best thing about rare diseases is the fact that not many people suffer from them. At the same time, this means that researchers have very little information concerning what causes these diseases, how they evolve, how they can be prevented or treated. Synthetic media technology can be utilized to analyze existing cases and generate new data for researchers to analyze, giving them ample opportunities to test different theories and information concerning how a patient would react to a particular treatment. Paired with machine learning, synthetic media can help decrease the time necessary to detect these diseases.
Streamlined drug discovery
The development of new medication and treatment is a time-consuming process that, more often than not, is based to some degree on trial and error and on test subjects who are willing to participate in laboratory trials to determine their effectiveness. This makes the creation of new medicine a costly and time-consuming process. Synthetic media can be used to generate synthetic patient data that faithfully recreate the data of real patients and even reproduce synthetic patients that suffer from an illness and use them as test subjects to determine how a real human being could react to a particular treatment. If a company would need a sample of ten’s thousands of people that suffer from a particular disease to determine the effectiveness of a treatment, synthetic media could provide these numbers.
Facilitate reproducibility in medical research
In any research field, results matter only if they can be successfully reproduced time and time again. This is especially valid in healthcare, where the same treatment and medication need to always produce the same results. In most cases, patient data privacy regulations can hamper reproducibility in clinical research. Synthetic media enhance data sharing and help clinical researchers ensure that their results are reproducible.