What if healthcare becomes not a universal right but a personal upgrade? Asks Paul Armstrong
The healthcare industry is entering its most radical transformation since the antibiotic era – and possibly its most dangerous. AI is no longer just scanning X-rays or arranging appointments. Robotic systems are beginning to perform surgery with levels of precision humans cannot sustain. Generative models are designing drugs from molecular blueprints. Exoskeletons promising longer, stronger working lives are moving from prototype to pilot. Elon Musk talks about a future where AI surgeons operate endlessly and where machine reasoning supports every clinical decision. A future like that could expand access, prolong healthy years and reduce suffering – and a future like that could just as easily produce an Elysium-style divide where optimisation is reserved for the wealthy and the rest of society receives the algorithmic, or, gulp, genetic, minimum.
Healthcare providers, insurers and governments are betting heavily on the optimistic version. Predictive diagnostics can identify cancers years earlier. DeepMind’s AlphaFold has mapped protein structures that accelerate drug discovery timelines from decades to months. Machine learning triage systems process clinical data at speeds no human team could match. McKinsey estimates AI could deliver more than one trillion dollars of annual savings for global healthcare systems by 2030. The NHS is already investing in AI radiology tools and automated triage models in an attempt to ease chronic pressure. A healthier population and a lighter budget is an appealing equation for any administration.
A sharp divide emerges once deployment begins. Data shapes every AI medical system, and most datasets come from affluent, urban, Western patients. Minority groups and lower income communities appear under-represented in the very material that trains models. A review in Nature Medicine found that most commercial medical AI tools showed significant performance gaps across ethnicity, gender and socioeconomic background. A world in which early detection becomes normal for some groups and optional for others is not a world that closes health inequalities. A system like that deepens them.
Private sector involvement accelerates the divergence. Neuralink, Figure and a wave of robotics firms are developing brain-machine interfaces, humanoid care assistants and rehabilitation technologies that will reach premium markets first. Boston Dynamics is exploring logistics automation for hospitals. Pharmaceutical companies now operate AI-first discovery pipelines where models predict the success of drug candidates and recommend lab experiments without human design. Investors are pouring billions into these ventures because the commercial logic is straightforward. Enhancement may just be more lucrative than equity, and optimisation will likely sell better than access.
Healthcare and business is going to change
A new narrative is emerging from the corporate side. Healthcare becomes not a universal right but a personal upgrade. Exoskeletons for ageing workers are being marketed as productivity equipment. Wearable medical models promise real-time behavioural nudges for peak performance. Employers will start subsidising enhancement technology long before national systems do like they did with wearables and telemetrics. Access will undoubtedly drift away from the most vulnerable and towards the most valuable.
Regulation cannot keep pace. The UK’s MHRA and the US FDA are drafting frameworks for AI medical devices, yet the boundary between therapy, augmentation and surveillance is already blurring. A robot performing a procedure after ingesting millions of prior surgeries raises an immediate question about liability. A clinician cannot meaningfully supervise a system that learns continuously from global data. A hospital cannot absorb responsibility for errors generated by a model built in another jurisdiction. Traditional medical accountability does not map onto distributed intelligence.
Why businesses need a healthcare strategy for the next ten years
Strategic preparation requires more than optimism. Healthcare is becoming a data industry that happens to treat patients. Insurers, pharmaceutical giants and providers are increasingly selling prediction rather than treatment. Whoever owns the patient model will dominate the next decade of healthcare economics. A serious national strategy should prioritise governance over automation. Every dataset should be audited for demographic balance. Diversity thresholds should be mandated for clinical training material. Public research institutions should partner with private firms to ensure the national population is represented in the training process, not just the wealthiest corners of it. Equity will be programmed long before it is legislated.
Boards face a new class of risk. Algorithmic ethics must carry the same weight as financial audits. Bias detection, model transparency and data provenance should be treated as competitive advantages. LinkedIn might just be about to find this out. Confidence in digital healthcare will collapse as soon as the first major misdiagnosis is traced to an opaque model. Firms that disclose training data and clinical validation will attract public trust that closed systems cannot match.
A deeper question sits behind all of this. Medical AI often frames itself as life-saving technology. Every major breakthrough comes with celebratory headlines. Hidden inside that narrative is a harder truth. AI systems optimise for objectives, not ethics. A predictive model may conclude that prevention for one population is less profitable than treatment for another. A personalised drug algorithm may recommend development pathways that favour wealthy markets. A triage system may ration by efficiency rather than equity. None of these outcomes represent medicine. All represent policy expressed as code. In other words, we’ve learned nothing from the racist hand soap dispenser. In short, we need to be better before any algorithm will be.
So what can businesses do now to prepare?
Healthcare will be transformed beyond recognition in the next decade. Progress does not guarantee fairness. Systems being built today will decide who receives early intervention, who receives enhancement and who is left with baseline care. Leaders who ignore this now will inherit consequences they cannot control. The real choice is not between human doctors and machine doctors. The real choice is between automated empathy and engineered inequality.
Businesses now need to treat AI in healthcare as both a supply chain shift and a reputational risk multiplier. Boards should map every point where employee health, customer data or product safety intersects with AI driven decisions. Governance should be raised to the same level as financial oversight. Firms should pressure test suppliers, demand transparency on training datasets, and model second order effects such as rising insurance premiums, workforce stratification or productivity gaps created by unequal access to augmentation tools. Companies that wait for regulators or vendors to define safe practice will fall behind. Strategic advantage comes from acting early, publishing clear ethical commitments, investing in literacy across teams and making equity part of product design rather than a retrospective patch.The first prescription for every executive, policymaker and investor should be a big dose of humility. Machine intelligence will save lives, but only if the system around it is designed to distribute those benefits fairly from the group up. We’ve seen what happens if the same people adapt the same swiped code. Elysium by default isn’t a given, it’s a choice we’ll need to fight Elon and co to achieve. A society that treats health as structural design rather than charity has a chance to use AI to close gaps rather than cement them. Britain has a head start and could lead the charge in this arena. Let’s hope Streeting and co are up for the challenge, and some quick pivots.