Generating Better Health Outcomes: Harnessing Generative ML Models in Digital Medicine
The public has been enraptured by artistic images created using generative models like DALLE-2 and Stable Diffusion and have been amazed by conversations with the seemingly omniscient Chat-GPT. Use cases for these generative models are being explored in spaces from fashion to copy editing.
As a digital health data scientist at Edge Analytics, I am always on the lookout for the best machine learning methods to solve healthcare problems. In this article, I delve into the applications of generative ML models in digital medicine. With the potential to generate synthetic data for algorithm training, support analysis of existing health data, personalize medicine through simulation, and enhance patient education, these models hold great promise. I am particularly interested in exploring the use cases of generative models in mental health and will provide an in-depth analysis of this application.
Overall, this article highlights potential future use cases for generative models in digital medicine.
Improving models through data generation
Training machine learning models for medical applications is challenging. One of the primary reasons for this is a lack of available training data. Machine learning models require large amounts of training data to make accurate predictions. Medical data is particularly challenging to obtain and use for a number of reasons. First, data should be procured with patient permission to ensure patient privacy, a process that is often very time-consuming and resource-intensive. Second, medical data is often highly imbalanced, and poor data coverage makes modeling rare conditions difficult. For example, gathering data on a specific type of cancer might yield just a few cases amidst hundreds or thousands of records. Lastly, available medical data is highly biased. Machine learning models exhibit the same biases as the data used to train them, so critical to minimize bias in training datasets.
Generative models can be used to synthetically create balanced realistic data with minimal bias and no privacy restrictions. A few examples for how generative synthetic data could be used in digital medicine:
- Generative models can be used to create synthetic medical images similar to their real counterparts, but with a known ground truth. This can help in the development and evaluation of medical image analysis algorithms. For example, generative adversarial networks (GANs), have already been used to produce synthetic X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography as well as retinal, dermoscopic, and ultrasound images [Source, Source].
Caption: Synthetic chest X-rays using NVIDIA’s StyleGAN2-ADA model [Source]
- Generative models can be used to synthesize patient data for use in research and development while maintaining patient privacy. For example, privacy regulations often limit access to electronic health record (EHR) data. Using synthetic EHR data generated using GAN or GAN-style models can eliminate this risk [Source, Source].
- To help researchers and clinicians better understand disease progression and effective treatments, generative models can be used to predict the progression of diseases by generating synthetic data that simulates the disease process. A recent study used a generative Markov-Bayesian-based model to generate 5,000 synthetic illness trajectories to infer the progression of complications in type 2 diabetes patients [Source].
In addition to synthetic data generation, generative models can be used to augment existing data. Augmenting existing datasets with additional data points may be helpful in cases where the available data is limited, or when the model needs more training examples. A recent study uses GANs to augment the Cleveland dataset, a small and imbalanced dataset for heart disease prediction [Source]. Another recent study proposes a longitudinal-diagnostic generative adversarial network (LDGAN) to predict multiple clinical scores at future time points using incomplete longitudinal MRI data [Source].
Data imputation is often done when data points are missing due to measurement error or patient noncompliance. While traditional data imputation methods are simple (i.e. mean or median imputation), they are often not representative of the actual data and can have significant impacts on the resulting model. In some instances, generative models can be used for data imputation. Imputation using generative models has been shown to have better performance than traditional methods in clinical research applications [Source].
Supporting analyses of existing health data
The incredible power of generative large language models could be harnessed for analysis of existing datasets in digital medicine. For example, a model can scan through hundreds of pages of medical records to extract key information for personalized treatment or health plans. The development of a large language model for EHRs has been used for clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference, and medical question answering [Source]. Models fine-tuned on biomedical data, like BioGPT (fine-tuned on GPT-2) or AWS Comprehend Medical, will play a central role in language modeling applied to medicine.
Generative models could also be used to aid healthcare providers through clinical decision support, like identifying potential drug interactions or recommending medication dosages. Early stage research shows great promise in predicting drug-drug interactions using generative models [Source]. Research using generative models to design drug combinations showed that the generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel pharmacology strategies [Source].
Research in digital medicine could also greatly benefit from generative models: by analyzing large amounts of data and identifying patterns and trends, generative models could aid in the development of new insights into the causes of diseases, methods for early detection of disease, and novel treatments. Early research shows promising results using GANs to assess progression of Alzheimer’s disease [Source].
Personalized medicine with a digital twin
A digital twin is a virtual model designed to accurately reflect a physical object. Used in applications from modeling wind turbines to space capsules, digital twins are extremely useful for providing insights into a variety of sensor systems and their environment.
The human body is incredibly complex and thus challenging to simulate. With generative models, we may get one step closer to having our own health digital twin. Health digital twins are virtual representations of patients that are generated from multimodal patient data, population data, and real-time updates on patient and environmental variables [Source]. For example, generative models could be used to predict the outcomes of different treatment options and predict an individual’s response to different treatment options, personalizing medicine. The startup world is already investing in this idea: Unlearn AI has raised $50 million to build their platform that simulates potential health outcomes for individual patients using generative models.
Patient education
Generative models could also be used for patient education and to help patients manage their health and wellness. Imagine an empathetic WebMD, interactive educational modules to explain medical concepts, or a disease management system to help patients manage chronic conditions, such as diabetes or hypertension. While not reliable enough to provide healthcare management education yet, generative models hold significant promise to enable personalization of education and health management.
Applying generative models to mental health
The mental health subfield of digital medicine is one area with numerous applications for generative models. Therapies could be enhanced greatly using generative modeling.
- Art is a common mode of therapy. Image generative models can be used to generate art in collaboration with patients. Patients providing inputs to generate unique artwork could be used as a form of creative expression and therapy. At the time of writing this article, I could only find one app claiming to use generative AI art for art therapy, NightCafe.
- Generative models could be used to generate images representing a patient’s emotions to serve as a starting point for discussion during traditional therapy sessions.
- Image generative models can be used to create realistic virtual environments for patients to experience. These virtual environments can be used in exposure therapy to help patients confront their fears or anxieties in a controlled setting.
- “Bot therapists” built on large language generative models could be used for 24/7 therapy sessions. People who are worried about judgment or biased care may be more willing to share their concerns with a bot. The Chat GPT subreddit is filled with individuals using Chat GPT as therapy [Source]. It is important to note, however, that, according to OpenAI’s policies, Chat GPT is not fine-tuned to provide medical information and should not be used to provide diagnostic or treatment services for health conditions. In the future, one could imagine a world where a Chat GPT-like model is fine-tuned or trained with medical data to provide therapy.
Potential challenges
There are a number of challenges with using generative models in digital medicine that will need to be addressed as these technologies transition from R&D to clinical settings. In addition to technical challenges [Source, Source], there are ethical considerations that I would be remiss to not note:
Bias
Bias in the training dataset of generative models will result in biased model outputs. Models should be trained using representative data from the population they intend to be used on to mitigate downstream bias and potential harm.
Misuse
Misuse of these models in healthcare is another potential challenge. These models have incredible potential to augment healthcare, but these models should never be intended to replace a healthcare provider or be used for nefarious purposes.
Conclusions
In conclusion, generative models hold immense potential in digital medicine. They can augment models with synthetic data, aid in the analysis of existing data, and personalize medicine through the creation of a “digital twin”. Additionally, generative AI can improve patient education and therapy, particularly in the domain of mental health. While there are challenges to overcome, I believe we are only just beginning to see the benefits to healthcare generative models will provide.
What are you planning to use generative models for?
We want to hear from you! How do you plan to use generative models? Leave us a comment or send us an email at info@edgeanalytics.io. While you’re here, check out our blog series on Getting the Most Out of GPT-3-based Text Classifiers: Part 1, Part 2, Part 3 or check out our recent blog on GPT-Edit.
Generative AI at Edge Analytics
Edge Analytics has helped multiple companies build solutions that leverage generative models. More broadly, we specialize in data science, machine learning, and algorithm development both on the edge and in the cloud. We provide end-to-end support throughout a product’s lifecycle, from quick exploratory prototypes to production-level AI/ML algorithms. We partner with our clients, who range from Fortune 500 companies to innovative startups, to turn their ideas into reality. Have a hard problem in mind? Get in touch at info@edgeanalytics.io.
