Shiboo Varughese, CTO of CirrusLabs, a leading Atlanta-based technology company, is passionate about using AI to drive innovation.
Diabetic retinopathy is the leading cause of blindness in adults, but if diagnosed early and treated correctly, blindness can be prevented. However, diagnosis can be tedious and treatment requires specialized medical professionals.
This is one of the reasons why the healthcare industry is turning to generative AI (GenAI): Recent studies have shown that deep learning models can accurately identify diabetic retinopathy from static retinal images. During testing, the AI models achieved diagnostic accuracy on par with expert ophthalmologists.
This is just one of many examples of how AI is driving innovation in healthcare. For example, AI is also being used in personalized medicine, drug discovery, medical imaging, and improved administrative efficiency. In fact, McKinsey found that GenAI has the potential to bring $1 trillion in unrealized improvements to the healthcare industry.
The Need for Generative AI in Healthcare
In recent years, GenAI has evolved significantly due to advances in computing power, data accessibility, and algorithm design. These models are becoming more adept at analyzing massive data sets to identify patterns, learn from them, and generate new, realistic outputs.
One of GenAI’s core capabilities is natural language understanding and generation, giving it the ability to improve tasks such as translation, summarization, and creative writing. For example, in healthcare, this allows GenAI to summarize each doctor’s visit, saving doctors a lot of time they would otherwise spend on manual paperwork. GenAI’s creative side also allows it to generate customized routines and schedules for terminally ill patients.
GenAI can also be used in medical imaging and cancer diagnosis because it can create realistic, high-quality images, audio, and video.
The healthcare industry is poised for disruption due to challenges such as an aging population, rising costs, and increasing complexity. GenAI offers a way to address these challenges by providing:
1. Streamlined administration: GenAI can be used to automate tasks such as patient registration with a hospital, billing, appointment scheduling, patient eligibility verification, and more.
2. Medical Imaging: GenAI can enhance medical image analysis by creating synthetic images for training algorithms, improving the accuracy of diagnostic tools. Moreover, these tools can detect cancer cells.
3. Diagnostics and personalized medicine: By analyzing patient data reports, GenAI can help create customized treatment plans, optimize drug therapy, and predict disease progression.
4. Drug discovery and development: Discovering new drugs is typically time-consuming and expensive. GenAI can accelerate this process by generating new molecular structures, predicting drug efficacy, and optimizing drug delivery systems while considering historical patient data in a repository to identify patterns.
Overcoming challenges
GenAI’s potential is enormous, but challenges must be addressed: building trust in AI systems, especially in healthcare, requires a delicate balance between innovation and ethical considerations.
1. Data Privacy and Security: As hospitals cater to customers across the globe, protecting sensitive patient information is crucial. By employing robust data anonymization and encryption techniques, hospitals can comply with global and international standards.
2. Model Bias: Ensuring that GenAI models are unbiased is important to avoid perpetuating existing disparities in healthcare. Techniques such as fair representation and bias detection can help mitigate this issue.
3. Explainability: Understanding how GenAI models make decisions is essential to building trust and ensuring regulatory compliance. This understanding helps train AI models efficiently using model interpretability techniques such as LIME and SHAP.
4. Talent Acquisition: Finding and retaining skilled AI professionals with healthcare domain expertise is a major challenge. Partnerships with academic institutions and online training platforms can help bridge this gap.
Best practices to implement with AI
Similarly, while AI is well-studied, the lines between black, white, and gray areas are very blurry and leave little room for speculation. This is why those working in this field need to be vigilant as they tread this uncharted territory. Here are some key best practices:
1. Iterative development: This iterative process allows for incremental improvements and ensures that the model meets specific requirements and objectives. For example, A/B testing during clinical trials helps identify areas of improvement in medical research and development. Based on the collected data, GenAI can go a step further and generate data with similar patterns to improve results.
2. Human-in-the-loop: This refers to involving human experts in the GenAI process. Humans can provide feedback, guidance, and corrections to improve the model output and prevent bias. Companies should constantly incorporate human feedback mechanisms to refine AI-generated content.
3. Ethical considerations: As generative AI becomes more powerful, it will be essential to consider the ethical implications of its use. Establishing clear ethical guidelines for generative AI and conducting regular bias audits is essential. Companies should work with ethicists and legal experts to ensure compliance with local regulators.
4. Robust testing: Robust testing is essential to ensure the reliability and quality of your GenAI models. Test your models thoroughly in a variety of scenarios to identify and address potential issues. Also, implement a robust testing framework that includes unit, integration, and user acceptance testing.
Developing a skilled workforce capable of developing and deploying these technologies is critical, and establishing clear regulatory frameworks and ethical guidelines is essential for responsible AI adoption.
By addressing the challenges and leveraging the opportunities, the healthcare industry has the opportunity to harness the power of GenAI to create a healthier future for all.
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