Cracking the Code: How Generative AI Can Solve the Rare Disease Puzzle
Rare diseases, in the healthcare landscape, come with unique hurdles. Diagnosing and treating the some 7,000 known rare diseases (Figure) that afflict millions of people around the world can be like trying to put together a puzzle with just half its pieces. Too often these stories stretch on for years as the patient bounces between doctors searching for a proper diagnosis and effective therapy. But latest technologies such as generative AI are providing a silver lining.
In the most recent episode of Digital Health Talks, Shweta Maniar, Director of Healthcare and Life Sciences at Google Cloud dives into how generative AI may be able to accelerate the diagnostic process for rare diseases. Below are the highlights of our key takeaways from that conversation:
The Struggles Behind Rare Diseases
Consider, for example that while each rare disease affects a small percentage of the population, as a group they victimize millions worldwide. Since the symptoms vary so widely, and often do not manifest until later in life, patients are usually shuffled from doctor to doctor before winding up at a specialist who can return their disease with a corresponding analyzing gene. Which is why this condition gets left out and undiagnosed or misdiagnose for many years just because there has not been enough people working to find more about it.
To meet these challenges, generative AI is now coming onto the scene providing capabilities that might help in speeding up the identification of rare diseases and also to create personalized treatment.
Generative AI: A Game Changer in Healthcare
It is a type of artificial intelligence called generative AI, which learns patterns from data and can then make new things. And in the world of rare diseases, generative AI can serve as a:
Data-Driven Diagnostics: AI can study terrabytes of data, both genetic and clinical records on a real-time basis to find associations that are often missed by human researchers. Through this data-driven approach, AI helps making reasonably faster and more reliable diagnosis suggestions.
Personalized Treatment Plans: To have a generative AI system to predict possible results of the treatment based on patient’s genetic profile and condition, it can possibly recommend personalized plan. This is true in particular of rare diseases, for which there are generally no standard treatments or where available therapies seldom produce significant positive results.
Accelerating Research: Generative AI models can quickly process new medical research and incorporate these insights into everyday clinical care. AI can process thousands of paper in scientific literature, to allow researchers your patients and help keep an up-to- date method using novel discoveries towards patient care.
Shweta Maniar’s Vision for AI in Rare Diseases
Shweta Maniar, meanwhile, points to the advances AI has made in other parts of healthcare like leveraging medical imaging or drug discovery and thinks rare diseases are set to follow that path next. Generative AI 350 can see wide types of medical data and generate predictions that reinforce the image to resolve rare disease puzzles, according to her.
Hopefully most health researchers can take a page from that book, which shows the importance of partnership between AI developers and healthcare providers and research more widely. Chen elaborates, "Generative AI has the promise of extending healthcare access to remote regions and underserved populations by creating advanced diagnostic tools.
Overcoming Ethical and Data Privacy Concerns
The possibilities of generative AI are limitless, but the roundtable also addressed some ethical and privacy issues that we have to bear in mind when using AI for healthcare. Now, it becomes crucial to protect medical data as this type of information mainly involves any genetic distinction.
Shweta explain the need for well-documented rules and frameworks around data use, as well being open with patients on what they are agreeing to. Integrating AI in a way that benefits all will require trust between healthcare providers and patients
The Future of AI in Rare Disease Diagnosis
With the development of general AI, generative Ai will most likely play a sizeable role in increasing health care-related activities. This presents a unique opportunity for rare diseases, as the capacity to analyze and make sense of complex data can not only reduce time-to-diagnosis but more importantly improve patient outcomes.
Over the next few years, we are likely to witness generative AI become crucial in:
Improved pattern recognition for faster and more accurate diagnosis of rare diseases.
Individualized Treatment Based on Genetic Profiles & Conditions
Worldwide cooperation with a data-sharing effort, advancing the speed of research within rare diseases by permitting different investigations and already reported results to be combined. It would also improve transparency on processing unpublished information in order to avoid duplication of study efforts or misleading interpretations due to misinformation gaps.
Generative AI is ushering in a new era of healthcare innovation. By harnessing the power of AI to tackle the complexities of rare diseases, we are moving closer to a future where patients no longer have to wait years for a diagnosis or a viable treatment. As Shweta Maniar concludes in her conversation, the real potential of AI lies in its ability to personalize care, offering hope and solutions to those who have been historically underserved.
To hear more about how generative AI is transforming rare disease care, check out the full podcast episode of Digital Health Talks here.
Watch the Talk
Experts
Shweta Maniar
Director of Global Healthcare & Life Sciences Industry Strategy at Google Cloud
Shweta Maniar has an extensive background in healthcare, life sciences, and medical innovation. With over 18 years of experience, she has played a pivotal role in clinical research, biotechnology, and digital health. Before joining Google Cloud, where she leads the Healthcare & Life Sciences Industry Strategy, she worked at Genentech, where her expertise earned her multiple awards, including Innovation and MVP awards.
At Google Cloud, Shweta focuses on driving digital transformation in healthcare through AI, cloud solutions, and data analytics. She helps develop strategic partnerships with healthcare systems, pharmaceuticals, and life sciences companies, aiming to leverage AI for patient care and medical research.
Her academic background includes a degree in bioengineering from the University of California, Berkeley, and medical device certifications from Cleveland Clinic, where she developed skills in minimally invasive therapeutics