Artificial Intelligence and Machine Learning has already transformed industries, boosting productivity, automation, and efficiency. But its most profound impact isn’t in cost savings. It’s in medicine, where it can quite literally save lives.
Take Pulmonary Fibrosis (PF), a disease where lung tissue becomes scarred and stiff, making every breath harder. For patients, the most urgent questions are deeply human:
👉 “How fast will my disease get worse?”
👉 “Will I need aggressive treatment or even a transplant soon?”
Right now, those answers are incredibly difficult to predict.
That’s why my research focuses on building a smarter way forward. Using Bayesian AI models, we can not only forecast how lung function changes over time but also capture uncertainty, something black-box models fail to do.
💡 Instead of just numbers, doctors get probabilities.
💡 Instead of guesswork, patients get clarity.
And the future? Even more promising. By integrating this with Multimodal LLMs, we can build AI systems that reason across patient data, clinical notes, and imaging results, then:
✔️ Summarize risks for doctors
✔️ Suggest treatments and clinical trial opportunities
✔️ Explain conditions to patients in plain, compassionate language
From this Pulmonary Fibrosis analysis:
📊 26% of patients were identified as “rapid decliners”—losing >50 ml of lung capacity per checkup.
This is a red flag for disease severity. It means that about 1 in 4 patients are at higher risk of fast progression.
These patients may:
Require closer monitoring
Benefit from early or more aggressive treatment
Face higher risks of hospitalization or complications
The potential impact
20–30% better identification of high-risk patients
30–50% shorter diagnostic delays
Up to 50% slower lung decline with earlier treatment
Faster, more efficient clinical trials.
This is where technology meets humanity. Where algorithms aren’t just improving workflows but they’re giving patients a chance at more time, better care, and hope.
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