How GenAI Can Transform Healthcare

As the CTO of CirrusLabs, a leading tech company based in Atlanta, Shiboo Varughese is passionate about leveraging AI to drive innovation.
Diabetic retinopathy is the largest cause of blindness among adults, but blindness can be prevented if the condition is diagnosed early and treated correctly. The diagnosis, however, is tedious. Treatment requires specialized medical professionals.
This is one of the reasons that the healthcare industry is turning to generative AI (GenAI). A recent study shows that a deep learning model can accurately identify diabetic retinopathy from static retina images. During the trial, this AI model achieved a diagnostic accuracy equivalent to that of a specialized ophthalmologist.
The above is just one of many examples of how AI is driving innovation in healthcare. AI, for instance, is also being used to improve personalized medicine, drug discovery, medical imaging and administrative efficiency. In fact, McKinsey found that GenAI has $1 trillion off unrealized improvement potential for the healthcare industry.
Healthcare’s Generative AI Imperative
In recent years, GenAI has evolved significantly due to advancements in computing power, data accessibility and algorithm design. These models excel at analyzing vast datasets to identify patterns, learn from them and generate new and realistic outputs
One of GenAI’s core capabilities is understanding and generating natural language, giving it the ability to improve tasks like translation, summarization and creative writing. In healthcare, this allows GenAI, for example, to summarize each doctor’s visit, saving the doctors a lot of time spent doing manual paperwork. The creative aspects of GenAI can also generate customized daily routines and schedules for terminally ill patients.
With the ability to create realistic and high-quality images, audio and videos, GenAI can be also used in medical imaging and cancer diagnoses.
Challenges like aging populations, rising costs and increasing complexities have made the healthcare industry ripe for disruption. GenAI offers a pathway to address these challenges by providing:
1. Administrative Efficiency: With GenAI, tasks can be automated when patients register at the hospital. Additionally, tasks like claims processing, appointment scheduling and patient eligibility verification can be automated.
2. Medical Imaging: GenAI can enhance medical image analysis by creating synthetic images for training algorithms, improving the accuracy of diagnostic tools. Furthermore, these tools can detect cancer cells.
3. Diagnostics And Personalized Medicine: By analyzing patient data reports, GenAI can assist in creating tailored treatment plans, optimizing drug regimens and predicting disease progression.
4. Drug Discovery And Development: Discovering new drugs is usually time-consuming and expensive. GenAI can accelerate this process by generating novel molecular structures, predicting drug efficacy and optimizing drug delivery systems, all while considering previous patient data in the repository to identify patterns.
Overcoming Challenges
While the potential is immense, challenges with GenAI must also be addressed. For healthcare, in particular, building trust in AI systems requires a delicate balance between innovation and ethical considerations, such as:
1. Data Privacy Bias And Security: Protecting sensitive patient information is paramount, especially since hospitals cater to a global clientele. Hospitals can comply with global and international standards by adopting robust data anonymization and encryption techniques.
2. Model Bias: Ensuring that GenAI models are unbiased is critical to avoid perpetuating existing disparities in healthcare. Techniques like fair representation and bias detection can help mitigate this issue.
3. Explainability: Understanding how GenAI models arrive at their decisions is crucial for building trust and ensuring regulatory compliance. This understanding can help train AI models efficiently using model interpretability techniques, such as LIME or SHAP.
4. Talent Acquisition: Finding and retaining skilled AI professionals with domain expertise in healthcare is a significant challenge. Collaborations with academic institutions and online training platforms can help address this gap.
Best Practices To Implement With AI
Likewise, though AI is well-researched, the lines between the black, white and grey areas are extremely blurred, leaving little space for speculation. This is why those working in this space must be vigilant while treading these unchartered waters. Here are a few key best practices:
1. Iterative Development: This iterative process allows for gradual improvements and ensures the model meets specific requirements and objectives. For instance, A/B testing during clinical trials can help identify areas for improvement in medical research and development. Based on the data collected, GenAI can go one step further and generate data with similar patterns for improved results.
2. Human-In-The-Loop: This refers to involving human experts in the GenAI process. Humans can provide feedback, guidance and corrections, improving the model’s output and preventing biases. Companies should always incorporate human feedback mechanisms to refine AI-generated content.
3. Ethical Considerations: As generative AI becomes more powerful, it is essential to consider the ethical implications of its use. Establishing clear ethical guidelines for GenAI and conducting regular bias audits is essential. Companies should collaborate with ethicists and legal experts to ensure the company’s compliance with the regional regulatory agencies.
4. Robust Testing: Robust testing is essential to ensure the reliability and quality of GenAI models. Thoroughly test models in various scenarios to identify and address potential issues. Also, implement a robust testing framework that includes unit, integration and user acceptance testing.
Building a skilled workforce capable of developing and deploying these technologies is crucial. Additionally, establishing clear regulatory frameworks and ethical guidelines will be essential for responsible AI adoption.
By addressing challenges and capitalizing on opportunities, the healthcare industry has the opportunity to harness the power of GenAI to create a healthier future for all.
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