By Sonny Iroche
In 2010, I lost my mother, Sybil, to heart-related complications. Twelve years later, MIT and Mass General Brigham unveiled “Sybil” — an AI model that can detect lung cancer up to six years in advance. While my mother’s illness was different, the coincidence of names is a poignant reminder of how far healthcare has come, and how AI might have given her more time.
A Mother Named Sybil
My mother, Sybil, was born on 14 May 1931, a rare name in Africa, often greeted with curiosity. She embodied wisdom, warmth, and grace, living a full life, often surrounded by family and friends. In January 2010, just months shy of her 79th birthday, she passed away from heart-related complications.
Her death, though unexpected for someone who had been in general good health, left a void that time has not fully healed. And yet, looking at the rapid advances in medical technology over the past decade, I can’t help but wonder: if the healthcare tools available today had existed back then, might she have lived longer?
In 2022, an AI tool named Sybil emerged, capable of predicting lung cancer years before symptoms appear. That name alone stopped me in my tracks. The original Sibyls of ancient Greece were oracles who foresaw the future. My mother’s name meant something similar to me, and here was a modern-day, life-saving “oracle” in the world of medicine.
Sybil the AI: A Modern Oracle for Lung Health
Developed by the MIT Jameel Clinic and Mass General Brigham, Sybil is an artificial intelligence model trained to analyze a single low-dose CT scan and predict a person’s risk of developing lung cancer up to six years in advance.
Unlike traditional radiology, which focuses on visible nodules or abnormalities, Sybil scans the entire lung image for subtle patterns invisible to the human eye. It works without needing extra patient information such as smoking history; an important feature since lung cancer affects non-smokers too.
In clinical validation studies, Sybil achieved high accuracy: it could predict one-year cancer risk with an AUC score above 0.90 and maintained strong predictive power over six years. It’s a glimpse into a new era of proactive, personalised healthcare, where diseases are anticipated rather than merely reacted to.
The Irony and the What-If
The irony is clear, in 2010, the year my mother passed, this kind of technology was unimaginable. Her condition was cardiac, not cancerous, but the principle remains: early detection saves lives.
If AI could predict lung cancer from a CT scan, it’s not far-fetched to imagine that similar AI systems trained on heart data, could flag cardiovascular risks years ahead. Indeed, those tools now exist.
AI in Cardiology: A Missed Opportunity in 2010
Heart disease remains the leading cause of death worldwide. In 2010, diagnosing heart conditions was far less precise, often relying on periodic check-ups, patient-reported symptoms, and basic imaging. By the time problems surfaced, damage was often irreversible.
Today, AI is changing that picture:
• ECG Pattern Recognition: AI algorithms can detect irregular heart rhythms (such as atrial fibrillation) from a simple ECG, even when the patient feels fine. Early treatment can prevent strokes and heart failure.
• Echocardiogram Analysis: Deep learning models can read ultrasound images of the heart with remarkable precision, spotting early signs of heart muscle weakness or valve disease.
• Risk Prediction from Wearables: Smartwatches with AI-driven heart monitoring can flag dangerous rhythms or trends in heart rate variability, prompting users to seek medical attention sooner.
• CT Calcium Scoring: AI can quantify calcium build-up in arteries from CT scans, predicting heart attack risk years in advance.
If tools like these had been widely available in 2010, perhaps they could have picked up early warning signs in my mother; signs which human doctors might have missed.
AI’s Broader Impact on Healthcare
The Sybil model is part of a larger movement where AI is tackling some of medicine’s biggest challenges:
• Breast Cancer: AI systems like MIT’s Mirai predict breast cancer risk up to five years ahead, helping personalise screening schedules.
• Skin Cancer: AI apps can now classify suspicious moles with dermatologist-level accuracy, even on a smartphone.
• Stroke Detection: In UK hospitals, AI reads brain scans to detect strokes faster and more accurately than human radiologists, shaving off critical minutes that can mean the difference between recovery and disability.
• Drug Discovery: AI models have designed entirely new antibiotics to fight resistant bacteria, cutting years off traditional research timelines.
Each of these breakthroughs echoes the same theme: see earlier, act earlier, save more lives.
Personal and Cultural Resonance
“Sybil” is not a name you often hear in Africa. My mother carried it with dignity, its meaning tied to wisdom, hard work and foresight. When I first read about the AI Sybil, I felt an odd mix of grief and pride, grief that such technology came too late for her, pride that the name is now associated with saving lives.
In both the human and technological forms, “Sybil” symbolises an ability to see ahead, whether in guiding a family or foreseeing a disease. The fact that both share this rare name makes the connection deeply personal for me.
The Future of AI in Medicine
Looking ahead, the challenge is not just creating powerful AI tools, but ensuring they are:
• Accessible: Many of the world’s patients live in African countries and other countries without advanced imaging or screening programmes. AI must be integrated into low-cost, widely available platforms.
• Ethically Governed: AI must work equally well for all populations, free from racial or gender bias.
• Integrated into Practice: Doctors must be trained to use AI effectively, understanding both its strengths and limitations.
If done right, AI could become as routine in healthcare as the stethoscope, silently running in the background, catching what human eyes and ears might miss.
A Legacy in Name and Purpose
I often imagine an alternate 2010 where my mother’s check-up included an AI-powered heart scan, quietly flagging an elevated risk score. Perhaps the doctor would have adjusted her treatment, caught a complication early, and bought us more time with her.
We cannot turn back the clock. But we can ensure that others do not lose loved ones simply because the tools to help them weren’t available. In that sense, the Sybil AI is not just a medical innovation, it’s part of a broader legacy of foresight and care that my mother embodied.
Conclusion: A Name, a Memory, and a Mission
The coincidence of “Sybil” the AI and Sybil my mother is more than sentimental. It’s a reminder that technology is at its best when it serves humanity, when it extends lives, preserves dignity, and spares families the grief of preventable loss.
My hope is that as these AI tools mature, they will become commonplace in clinics from Boston to Lagos, to Johannesburg and other cities; helping people live longer, healthier lives. And perhaps, when patients hear about Sybil in the future, they will think not just of an algorithm, but of the mothers, fathers, and children whose lives it helped save.
