Professor (Dr.) Sanjay
In the next chapter of human health, medicine must evolve beyond the bedrock of randomized trials, systematic reviews, and the best available clinical evidence.
Evidence‑Based Medicine (EBM) has served us profoundly well—offering a structured, scientific basis for decisions that once relied heavily on intuition. But now, we stand at the threshold of a transformative paradigm: what I term Intelligence‑Based Medicine (IBM)—where precision medicine, real-time data, adaptive algorithms and human clinical wisdom converge to deliver individually optimized care
For decades, the practice of medicine has been anchored by a powerful, yet fundamentally retrospective, compass: Evidence-Based Medicine (EBM). We have looked backward to move forward, collating data from populations of the past to guide decisions for the patient of the present. This paradigm, for all its life-saving merits, is reaching its epistemological event horizon. The future of healing does not lie in the rearview mirror of aggregated trials; it lies in the forward-looking, predictive, and deeply personal synthesis of data, artificial intelligence, and human wisdom.
We are at the dawn of the era of Intelligence-Based Medicine (IBM).A revolutionary paradigm shift is underway in global healthcare. Evidence-Based Medicine (EBM), once the undisputed cornerstone of clinical practice, is quietly giving way to Intelligence-Based Medicine (IBM)—a dynamic, real-time, hyper-personalised framework powered by artificial intelligence, multi-omics, and digital twins. This is not evolution. This is transformation.
For three decades, EBM has served us brilliantly. Randomised controlled trials, meta-analyses, and rigorous statistical validation have slashed mortality from heart disease, cancer, and stroke. Yet, in 2025, we face a sobering truth: even the strongest evidence applies to populations, not individuals. A therapy that saves 70 % of patients leaves 30 % behind. Side-effects cripple thousands. Costs spiral. And rare diseases—those affecting fewer than one in 2,000 people—remain therapeutic orphans because trials are simply not feasible.
EBM asked, “What works for most?” IBM asks a more profound question: “What will work, precisely and preemptively, for you?”
This is not a semantic shift. It is a foundational transformation in our very philosophy of care. It moves us from practicing medicine as a stochastic art—educated guesswork based on probabilities—to wielding it as a deterministic science.
Why Move Beyond Evidence-Based Medicine?
Evidence-based medicine explicitly integrates clinical expertise, patient values, and the best available external evidence. Yet its focus has been largely on populations, averages, and cohorts rather than the unique individual patient sitting across from the clinician. Critics point out that EBM often struggles with heterogeneity of patients, rare conditions, atypical responses, and the fast-moving velocity of biomedical knowledge. Meanwhile, exponential growth in biomedical data—from genomics and metabolomics to wearable sensors and real-world clinical outcomes—has created opportunities and challenges that traditional EBM was not designed to address. The gap between what is known and what is personalized remains significant.
Enter Intelligence-Based Medicine:
.IBM does not discard evidence; it transcends it. Where EBM asks, “What worked best for similar patients in the past?” IBM answers, “What will work best for this unique patient, right now, and how will their biology respond over the next 72 hours?” The difference is profound.
Defining Intelligence-Based Medicine:
Intelligence-Based Medicine is the integration of three pillars:
1. Data-Driven Intelligence:
AI, machine learning, federated learning, digital twins, and adaptive analytics that capture vast and diverse biomedical and lifestyle data.
2. Clinical Wisdom & Patient Values:
No algorithm replaces the clinician’s judgement nor the patient’s individuality—their preferences, values, and context remain central. IBM honours that the human dimension is nonnegotiable.
3. Dynamic Evidence Synthesis:
Evidence no longer waits for decades; it evolves continuously. Systematic reviews, meta-analyses and real-time data streams merge, allowing decisions that are responsive, personalized and transparent.
The Pillars of Intelligence-Based Medicine:
IBM rests on three core pillars that distinguish it from its predecessor:
1. The Predictive Pillar: From Diagnosis to Prognostication
EBM reacts to manifested disease. IBM anticipates and intercepts pathological pathways before they crystallize into illness. We are no longer waiting for the myocardial infarction; we are modeling the individual’s endothelial inflammation, lipid dynamics, and hemodynamic stresses in silico, identifying the precise moment for a micro-dose, personalized intervention that averts the event entirely. This is the power of the Digital Twin—a dynamic, virtual replica of a patient’s physiology, continuously updated by a torrent of data from multi-omics (genomics, proteomics, metabolomics), wearable sensors, and environmental inputs. We will no longer say, “You have a 15% risk of a heart attack in ten years.” We will state, “My model indicates a 94% probability of a minor plaque rupture in the left anterior descending artery in 73 days. Let’s intervene next Tuesday.”
2. The Personalization Pillar: From Cohort to N-of-1
Precision Medicine was the first crack in the monolith of population-based care. IBM is its complete dissolution. Consider the recent breakthrough in regenerative neurology: an injectable, bio-scaffolding gel that I have been involved with in an advisory capacity. This isn’t a generic treatment. The gel’s composition—its signaling proteins, its structural matrix, its release kinetics—is bespoke, engineered in vitro for the specific biochemical and cellular microenvironment of the patient’s nerve injury. It doesn’t just encourage regeneration; it conducts it with the precision of a maestro, resulting in the full restoration of sensation, a feat once confined to science fiction. This is the archetype of IBM: therapies not merely selected, but architected for a biological context of one
.3. The Participatory Pillar: From Patient to Partner
In the IBM ecosystem, the individual is the central node—a co-pilot, not a passenger. Their data stream is the lifeblood of their care continuum. AI-driven algorithms act as cognitive prosthetics for physicians, synthesizing this data into actionable insights, freeing us from administrative drudgery and cognitive overload to focus on the quintessentially human aspects of care: empathy, complex judgment, and the therapeutic alliance. The physician’s role evolves from being the sole repository of knowledge to being the master interpreter of intelligence—the “sense-maker” for the patient-partner.
4.Continuous Learning Systems
Traditional guidelines update every 5–10 years. IBM updates in microseconds. Federated learning networks—secure, privacy-preserving pipelines—pull de-identified data from millions of electronic health records, wearable devices, and genomic sequencers worldwide. When a new mutation emerges in pancreatic cancer, IBM detects it across continents within hours, recalibrates treatment algorithms, and pushes silent updates to every oncologist’s decision-support interface. No committee meetings. No delays.
5.Digital Twins at Cellular Resolution
Imagine a perfect virtual replica of your heart, liver, or brain—updated in real time by smart tattoos, ingestible sensors, and liquid-biopsy microfluidics. Siemens Healthineers and Dassault Systèmes have already deployed cardiac digital twins in over 400 hospitals. By 2030, every ICU patient will have a twin predicting sepsis 18 hours before current biomarkers twitch. In oncology, twins simulate 1,200 drug combinations overnight, selecting the single regimen most likely to eradicate tumours while preserving fertility and cognition.
6. Causal AI, Not Just Correlation
Deep neural networks now infer causality using counterfactual reasoning and do-calculus. At Stanford’s AI Lab, a model recently outperformed 27 senior neurologists in predicting stroke recurrence—not by pattern-matching, but by reconstructing the exact biomechanical chain from atrial fibrillation to embolus to penumbra. The implications for preventive pharmacology are staggering: we can now prescribe interventions before the disease pathway even ignites.
7. Human-AI Symbiosis in the Operating Theatre
Last month in Zurich, a robotic surgeon guided by IBM completed a spinal nerve repair using the new injectable regenerative gel developed at EPFL. The AI did not merely assist; it anticipated micro-bleeds 40 milliseconds before they occurred, adjusted suction pressure dynamically, and selected the precise concentration of nerve-guidance peptides in real time. Post-operative sensation returned in 11 days—three times faster than the best human-only outcomes. The patient, a 34-year-old violinist, played Bach again last week.
From Theory to Practice: Key Domains of Transformation :
Diagnostics & Imaging: AI-augmented imaging and predictive models flag early disease phenotypes missed by the human eye or standard table of risk. Rather than waiting for high-dose contrast or late-stage symptoms, we capture subtle biomarkers, integrating them with clinical context to initiate therapy earlier.
Therapeutics & Precision Treatment:
Pharmacogenomics, immunotherapy, gene editing, and regenerative medicine are no longer experimental tangents—they stand ready for deployment within an intelligence-based framework, where therapy is matched not just to disease but to who the patient is.
Surgical & Interventional Innovation:
Using augmented reality, robotic assistance, intraoperative analytics and real-time outcome feedback loops, surgeries become adaptive, tailored, and continuously improved—treatments learn from each case, and each case teaches the system.
Patient Monitoring & Outcomes:
Wearables, implantables, sensors and connected health platforms feed continuous real-world data. Intelligence-based systems use this stream to adjust care, flag deviations, and predict complications—shifting from reactive medicine to proactive health management.
Regulation & Ethics:
As we advance, intelligence-based medicine demands transparent, adaptive regulatory frameworks that guard privacy, manage bias, ensure equitable access and validate algorithmic decisions. It’s not only about the technology—it’s about trust, fairness, and global collaboration.
The New Therapeutic Arsenal: Beyond the Pill:
The tools of IBM are as revolutionary as the philosophy itself. We are moving beyond the blunt instrument of the systemic drug to an era of exquisite, microscopic interventions.
· Theragnostic Nanobots:
Imagine sub-micron devices that simultaneously diagnose a nascent cancer cluster through specific surface markers and deliver a lethal, localized payload of therapy, then confirm apoptosis—all within a single, non-invasive procedure. This is not futurology; the foundational science exists in labs from Zurich to Singapore.·
AI-Enhanced Surgical Symbiosis:
The future of surgery is not robotic; it is cognitive. The “cognitive scalpel” I refer to in the title is an AI-guided instrument that provides real-time, intra-operative tissue analysis, warns of micro-vascular structures invisible to the human eye, and suggests the optimal resection path based on the patient’s unique digital twin simulation. It is a symbiosis of human skill and machine precision, minimizing collateral damage and maximizing functional preservation.·
Dynamic Pharmacogenomics:
Instead of a static genetic test, we will have living, evolving models of a patient’s liver metabolism. Before a new drug is even prescribed, we will simulate its journey through their unique system, predicting efficacy and pre-empting adverse reactions with near-perfect accuracy, rendering the term “side effect” nearly obsolete.
From Bench to Bedside: Three Breakthroughs Already Saving Lives
Pancreatic Cancer Liquid Biopsy Panel (Johns Hopkins + Freenome)
Detects five cancer-specific methylation patterns with 94 % sensitivity at stage I—when surgery still offers cure rates above 80 %. IBM integrates these results with radiomics, germline genetics, and gut-microbiome data to deliver a personalised adjuvant protocol within six minutes of biopsy.
CRISPR 2.0 Base-Editing for Sickle-Cell Disease (Beam Therapeutics + Broad Institute)
In vivo editing now achieves 96 % correction in haematopoietic stem cells. IBM predicts which patients will develop neutralizing antibodies to the viral vector and switches seamlessly to non-viral lipid nanoparticles—eliminating treatment failure before it happens.
Closed-Loop Parkinson’s Therapy (Medtronic + Rune Labs)
Subthalamic deep-brain stimulators now adjust 1,000 times per second using AI trained on 3.2 million hours of patient brain-activity data. Tremor reduction exceeds 90 %, and battery life has tripled. Patients report “feeling normal for the first time in 20 years.”
The Roadmap Ahead :
To transition from evidence-based to intelligence-based medicine, healthcare systems must embrace five key strategic shifts:
1. Infrastructure for Data & Compute: Secure, interoperable health data systems, high-performance analytics and federated models for global collaboration
2. Adaptive Clinical Trials & N-of-1 Frameworks: Move beyond group averages to designs that capture individual variability, rare conditions and personalized trajectories.
3. Human-AI Collaboration: Design systems where AI augments—but does not override—clinical judgement. Models must be explainable, auditable, and aligned with patient values.
4. Continuous Learning Systems: Care delivery becomes a feedback loop—real-world outcomes feed back into analytics, evidence is continuously refined, models evolve, and protocols adapt in near real-time.
5. Global Equity & Ethical Governance: Intelligence-based medicine must avoid creating new disparities. Accessibility, affordability, diversity of populations and algorithmic fairness must be built in from day one.
The Inevitable Challenges and Our Ethical Imperative:
This brave new world is not without its perils. The datafication of life creates unprecedented vulnerabilities for privacy and security. The algorithms that power our predictions can perpetuate and even amplify societal biases if not curated with rigorous, ethical oversight. The cost of these technologies threatens to create a new kind of health disparity—a chasm between the computationally-rich and the computationally-poor.
Therefore, the development of IBM must be paralleled by the development of a robust “Bio-Ethical Framework 2.0.” We need global standards for data sovereignty, algorithmic transparency, and equitable access. As scientists and physicians, our first oath is to primum non nocere—first, do no harm. In the 21st century, this oath must extend to the digital realm and the societal structures our innovations will inevitably impact.
The Economic Imperative:
McKinsey projects IBM will generate $450 billion in annual value by 2035 through reduced hospital readmissions, optimised drug development, and prevention of chronic-disease complications. In low-resource settings, IBM-powered smartphone diagnostics—already achieving 97 % accuracy for diabetic retinopathy in rural India—democratise world-class care without billion-dollar infrastructure.
Why This Matters Globally :
The burden of disease is evolving: multi-morbidities, aging populations, precision demands, and resource constraints challenge old models. By shifting to intelligence-based medicine, we unlock three profound benefits:
Scalability of Precision: Individual-level tailoring once reserved for niche care becomes broadly deployable.Efficiency and Value: By optimising diagnostics, reducing redundant interventions, and targeting treatments, we lower cost and maximize value for patients and systems.
Transformative Outcomes: Moving from a “treat when sick” paradigm to “predict, prevent, personalise” we change the arc of healthcare—shifting from disease management to health creation.
Ethical Guardrails: Because Power Without Principle Is PerilWe must confront bias, transparency, and consent head-on. IBM systems now embed “explainability by design.” Every recommendation arrives with a human-readable causal map: “This chemotherapy dose was reduced by 18 % because your digital twin predicted a 43 % risk of grade-3 cardiomyopathy given your specific BRCA1 variant and cardiac MRI strain patterns.” Patients own their twins. Data never leaves encrypted enclaves without explicit, granular permission.
The Next Decade: A Vision:
By 2035, no child will die of sepsis undiagnosed. No elder will fracture a hip because an AI missed early sarcopenia signals. No cancer patient will endure toxic chemotherapy that was doomed from the start. Intelligence-Based Medicine will turn reactors of data into rivers of insight, flowing directly into the art of healing.Evidence gave us the map. Intelligence gives us the compass—continuously recalibrating as the terrain of human biology shifts beneath our feet.We stand at the threshold of a new golden age of medicine. The question is no longer whether IBM will replace EBM, but how swiftly we can deploy it with wisdom, equity, and unrelenting focus on the person at the centre of every algorithm.
End Note: The Symphony of Synthesis evidence-based Medicine was a monumental solo. Intelligence-Based Medicine is a symphony. It is the harmonious integration of human biology, data science, material science, and human compassion. It is the final rejection of the notion that disease is an inevitable force to be battled, and the embrace of a new reality where health is a state to be actively engineered and maintained.The cognitive scalpels are being sharpened. The digital twins are being born. The intelligent gels are ready to mend our broken pathways. We stand at the precipice of the greatest transformation in the history of healing.
It is our collective duty—as researchers, clinicians, technologists, and citizens—to ensure we step forward with wisdom, courage, and an unwavering commitment to a healthier, more intelligent future for all.Medicine stands at an inflection point. We must honour the hard-won legacy of evidence-based care while transcending it with intelligence-based systems—where data, technology, and human care converge to deliver individualized health.
The future is not distant. It is being built today. As clinicians, scientists, engineers and regulators, we are architects of that future. When we embrace intelligence-based medicine, we unlock the possibility of healthcare that is smarter, more adaptive, and truly personal. The next horizon is not just about treating disease—it is about enabling human flourishing.
The future is not waiting. It is already learning.
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