By Futurist Thomas Frey
The Question Nobody Thought to Ask
What if instead of analyzing your cancer, you could interview it? Not metaphorically—literally ask your tumor what it needs to survive, why it’s growing, what would kill it most effectively. What if your autoimmune disease could explain exactly why your immune system is attacking your body and what would make it stop?
This isn’t science fiction. It’s the logical endpoint of AI-powered diagnostic systems that can simulate complex biological processes and translate them into conversational interfaces. And it changes everything about how we diagnose, treat, and understand disease.
The concept comes from software engineering: “talking to the defect.” When code fails, instead of manually debugging thousands of lines, AI systems can simulate the defect’s behavior and explain—in plain language—what’s wrong, why it’s happening, and how to fix it. AI business developer Dave Blundin articulated this breakthrough in diagnostic methodology: give the problem a voice.
Now extend that concept to medicine. Your illness becomes your diagnostic partner. The defect in your body explains itself. The disease that’s killing you tells you how to kill it first.
Let me walk you through why this represents fundamental transformation in medical diagnosis, what becomes possible when diseases can explain themselves, and how quickly this shifts from theoretical to clinical reality.
How “Talking to the Defect” Actually Works
In software, the process is straightforward: AI analyzes the defective code, simulates its execution, identifies failure points, and generates natural language explanation of what’s wrong. Instead of developers hunting for bugs manually, the bug describes itself—its root cause, its propagation pattern, its fix.
In medicine, the process becomes more complex but follows the same logic:
Step 1: Complete biological modeling. AI systems analyze your specific tumor—its genetic mutations, metabolic pathways, growth patterns, response to environmental factors. Not generic cancer models—your exact cancer with all its unique characteristics.
Step 2: Simulation and prediction. The system simulates your cancer’s behavior: how it evades immune response, what nutrients it requires, which treatments it would resist, what vulnerabilities exist in its survival strategy.
Step 3: Conversational interface. Instead of presenting doctors with raw data and complex visualizations, the AI creates conversational agent representing your disease. You literally talk to your cancer.
The conversation might go:
Doctor: “Why are you resistant to chemotherapy?”
Cancer AI: “I’ve upregulated DNA repair mechanisms in response to previous treatment. Standard chemotherapy damages my DNA, but I repair it faster than it accumulates. You need combination therapy that overwhelms my repair capacity.”
Doctor: “What’s your metabolic weakness?”
Cancer AI: “I’m dependent on glutamine for rapid growth. Glutamine deprivation would slow my proliferation significantly, especially if combined with targeting my altered glucose metabolism.”
This isn’t the AI guessing—it’s the AI translating complex biological simulation into actionable conversation. The “defect” explains itself because the AI understands the defect completely.
What Becomes Possible
Personalized treatment optimization. Instead of trying standard protocols and hoping they work, you ask your specific disease what would kill it most effectively. The cancer that’s unique to you explains its unique vulnerabilities.
Preemptive resistance management. Ask your infection what antibiotics it would develop resistance to fastest. Ask your tumor what escape mutations it would likely develop under different treatment regimens. Design therapy that anticipates and prevents resistance before it emerges.
Patient understanding and agency. Imagine being able to ask your autoimmune disease: “Why are you attacking my joints?” and receiving comprehensible explanation of the immunological cascade causing your symptoms. Patients become informed participants rather than passive recipients of care they don’t understand.
Accelerated drug discovery. Pharmaceutical researchers can interview simulated diseases to understand what molecular interventions would be most effective. Instead of testing thousands of compounds blindly, ask the disease what would disrupt it and design molecules specifically for that purpose.
Preventive intervention. Talk to pre-cancerous cells about what conditions would trigger malignant transformation. Talk to your genetic predispositions about what lifestyle factors would activate or suppress disease risk. Intervene before illness manifests.
The Timeline We’re Looking At
This isn’t distant future speculation. The components already exist:
Current capability (2026): AI systems can analyze genomic data, predict protein structures, simulate metabolic pathways, and generate natural language explanations. We’re not yet “interviewing” diseases, but we’re close.
Near-term (2027-2030): First clinical applications of disease simulation with conversational interfaces. Oncology leads—asking tumors to explain their treatment resistance, identifying personalized vulnerabilities. Infectious disease follows—interviewing bacterial infections about antibiotic susceptibility.
Medium-term (2030-2035): Mainstream adoption for complex diseases. Autoimmune conditions, neurological disorders, chronic illnesses all get conversational diagnostic interfaces. Standard of care shifts from “analyze the disease” to “interview the disease.”
Long-term (2035-2040): Preventive medicine dominated by this approach. Regular health monitoring includes conversations with potential diseases before they manifest. “Your cardiovascular system is developing atherosclerosis—here’s what it says about its progression and how to reverse it.”
The technology trajectory is clear. The only question is how quickly medical practice adapts to treating diseases as entities that can explain themselves rather than mysteries to be decoded.
Why This Changes Everything
“Talking to the defect” transforms medicine from pattern recognition to direct interrogation. Instead of inferring what might be wrong from symptoms and tests, you ask what’s wrong and receive explicit answer from the biological process causing the problem.
This isn’t replacing doctors—it’s giving them unprecedented diagnostic power. The physician who can skillfully interview a patient’s disease extracts information impossible to obtain through traditional diagnostics.
It also democratizes medical understanding. When your disease can explain itself in plain language, you don’t need medical degree to understand what’s happening in your body. The information asymmetry between doctor and patient collapses when both can interview the illness directly.
Final Thoughts
The concept of giving defects a voice started in software engineering but applies to any complex system—including human biology. When AI can simulate your specific disease with sufficient fidelity, it can speak for that disease, explaining its behavior, vulnerabilities, and treatment response.
This means the future of diagnosis isn’t better imaging or more sensitive tests. It’s conversations with the illnesses trying to kill us, asking them directly how to kill them first.
Your cancer will tell you what would cure you. Your infection will explain what would eliminate it. Your chronic disease will describe what would reverse it. And medicine transforms from educated guessing to informed interrogation.
The defect gets a voice. And that changes everything.
Related Articles:
Talking to the Defect: Dave Blundin on AI-Powered Diagnostics https://www.youtube.com/watch?v=7GFKB0oKd9A
AI-Powered Protein Folding Breakthrough Revolutionizes Drug Discovery https://www.nature.com/articles/d41586-024-alphafold-protein-structures
The Last Economy: Why Our Current System Collapses When Intelligence Becomes Cheaper Than Labor https://www.impactlab.com/2026/01/02/last-economy-system-collapse-intelligence-cheaper-labor/
