How Generative AI Can Help Accelerate Healthcare Insights Using Multi-Modal Data

By Phane Mane and Brian Peet

When you think about organizations in the broader healthcare industry, especially the companies that serve in the “Life Science” vertical a couple of things stand out i) Everyone is subjected to significant regulation and ii) they manage tons of data, often in various formats.

Naturally, all such firms look to leverage the power of Generative AI to either gain competitive advantage through investments in R&D, product development, etc., or improve operational efficiency for cost cuttings and an obvious approach to do that is to look at the data in their possession to derive insights that can help them make informed decisions and ultimately improve outcomes for patients.

LLM Challenges with Healthcare Data

Two key challenges with using Large Language Models (LLMs) to unearth insights are i) data privacy and/or protection and ii) confidence in the quality of “generated” insights because most often such "insights?" can be misleading due to the underlying model’s hallucinations.

Fortunately, there are many Machine Learning (ML) techniques such as Parameter-Efficient Fine-Tuning (PEFT), Reinforcement Learning from Human Feedback (RLHF), Retrieval Augmented Generation (RAG), Chain-of-Thought (CoT) Reasoning etc. to increase the quality of insights – similarly data privacy can be achieved through anonymization, minimization, tokenization, masking and obfuscation and hosting the AI models within the organization's infrastructure to protect PII/PHI data for cyberattacks and ensure regulatory compliance.

Given the large landscape (volume, variety, and variance) of data in healthcare companies that could include patient records medical images, and sensor data; although offering immense potential for improving patient care, and advancing medical research, the complexity poses significant challenges for traditional analytical approaches that have been around for decades.

Tackling Data Modalities

Coupled with this, healthcare data comes in various forms, charts, publications, codes, electronic health records (EHR), medical imaging, genomic sequences, RWE, and wearable device data – in other words, it is truly “multi-modal” which essentially means data could include text, images, audio, and video, sensor data, or any other form of information.

Each modality provides unique insights into patient health, but analyzing these data streams in isolation can limit the depth of understanding. Multi-modal data fusion, the integration of information from different sources, has emerged as a powerful strategy for unlocking comprehensive insights into complex healthcare challenges.

Traditionally, analyzing multi-modal healthcare data required manual intervention and extensive computational resources, often resulting in lengthy turnaround times and limited scalability.

Generative AI to the Rescue

While LLMs have already proven their versatility with handling data challenges in other verticals, their ability to do the same with healthcare data is still being debated – this is because most open-source models weren’t exposed to such data (private, voluminous, multi-modal, etc.) given privacy implications.?

The application of Generative AI in healthcare extends beyond clinical practice to encompass a wide range of use cases, from drug discovery and development to disease modeling and even population health management. By leveraging multi-modal data sets and advanced AI techniques, researchers can uncover novel biomarkers, identify disease risk factors, and optimize treatment protocols with unprecedented precision. Some additional examples could include medical image synthesis, data augmentation, synthetic data generation for clinical research, anomaly detection, and healthcare simulation for personalized medicine, etc.

Let's delve into some practical examples from our industry experience, showcasing how Generative AI has been pivotal in deriving effective insights from multimodal data within the Healthcare sector. Understanding the essence of any problem is the initial step toward finding a solution. It's crucial to pinpoint the focal issue, assess the value of a potential solution, and determine the indicators that suggest your analysis is on the right track.

Machine Learning (ML) and Deep Learning (DL) techniques are remarkably suited for this analytical approach. Each method within AI follows a foundational process involving data pipelines, data labeling and organization, comparison with reliable sources, and the generation of plausible outcomes. This process hinges on the principle that the proposed solution is likely to be accurate.

Based on our experience working with Biotech companies and as Generative AI practitioners, we recommend an approach that integrates the best of LLMs including text processing, natural language understanding, and image recognition, thereby extracting more valuable insights.

Below is an overview of the streamlined process we've adopted:

1. Identify the Problem: Pinpoint the core issue that needs resolution.
2. Formulate a Hypothesis: Propose a potential explanation or solution to the problem.
3. Determine Data Sources: Identify which datasets can be utilized. Which includes charts, text, and diverse datasets.
4. Acquisition and Cleansing Tools: Select tools for data gathering and purification.
5. Data Transformation: Modify the data into a usable format.
6. Model Selection or Development: Choose or develop models that can interpret the transformed data effectively (We believe that the synthesis of charts, graphs, and text has been critical to improving our understanding of the data and the results)
7. Results Review: Decide who will evaluate the outcomes and help to improve the process
8. Iterate Rapidly: Embrace swift failures, making adjustments until the results are convincingly probable.

The remarkable aspect of this approach is how understanding the problem, along with the appropriate tools and methods, can expediently direct you to the most suitable solutions, faster than ever before. While AI is not a panacea or a flawless fix, it significantly boosts the productivity of its users. By adhering to a structured process, you can ensure that your results meet your needs, benefiting both patients and companies.?

Determining if you need to use traditional analysis or multimodal advanced models, is based on a foundational need to understand the problem and if the solution derived is plausible.

Conclusion:

In conclusion, despite challenges with protecting data privacy and dealing with large amounts of data in all imaginable modalities, ML techniques can help enhance precision while ensuring regulatory compliance. Healthcare organizations can leverage the power of Generative AI to accelerate insights that could benefit drug discovery, unlock novel biomarkers, and provide treatment protocols.

Structured analysis, with the right tools, can accelerate iteration, quality of insights and meet organizational needs ultimately leading to improved patient outcomes everywhere.

I hope you find this blog helpful, please comment to share your thoughts or suggest other topics you would like us to discuss in the future.