Can machines make healthcare affordable?
Doing more with less, and faster, by harnessing machine intelligence in biology and health
Biology is an elegant operating system. It has been driving the machinery of living organisms for a long time and doing so seemingly autonomously. Rather than using bits, information is represented by atoms, and it can execute programmes written in an obscure yet highly performant language known as DNA. The execution of unique source code within each living thing gives rise to the infinite diversity in their structure and function.
Human health is predicated upon understanding the inner workings of this operating system –– how it performs instructions, handles I/O, and maintains state. The challenge has always been that it resembles a black box –– incredibly complicated and has no documentation. Since the early days of civilisation, humans have sought to decode it by way of observation and experimentation, one unit of endeavour at a time. Recording of findings and more systematic investigation led to continuous accumulation of a shared knowledge over time about its underlying intricacies, which today forms the foundation of modern medicine. Despite remarkable achievements in healthcare owed to this increased understanding, there is much left to discover about the system.
Augmented intelligence –– 20 experts for the price of 1
The complexity of biology and the need for various human experts to interpret, explore, perturb, and consult on it are core drivers of what makes healthcare expensive today –– often prohibitively so. New tools and technology have advanced the field –– allowing for reading source codes, interpreting stack traces, writing new programmes, and analysing output –– and produced more data than could ever be obtained before. Yet, a tangible reduction in cost of care for patients remains elusive.
This, however, may be changing. The confluence of deep learning models, powerful computers, cloud infrastructure, and data has put artificial intelligence front and centre in the evolution of several industries –– of which healthcare is one of the most promising. By supercharging human efforts, AI has the potential to radically optimise cost across the healthcare value chain –– particularly in medical research, drug development, and patient care.
Weaving a tapestry of biology, one petaflop at a time
AI has the potential to accelerate scientific discovery in medicine, saving hours of work and expensive resources.
It can aggregate existing knowledge about biology and piece together a more thorough blueprint of its underlying mechanics –– akin to a form of documentation. By interacting with this documentation, scientists can more quickly extrapolate new knowledge –– e.g. deciphering the semantics of biology’s programming language, elucidating disease origin and mechanism of action, discovering novel biomarkers, and identifying areas for further work in the wet lab.
These AI systems can be built by training deep learning models on diverse biological datasets, including omics data, clinical data, and scientific literature, with the potential for specialisation or generality and the ability to process uni- or multi-modal information.
A notable example of such models is DeepMind’s AlphaFold. Trained on a proteomics dataset, AlphaFold learned to predict the 3D conformation that a newly manufactured protein comes to adopt within the body of a living thing –– critical for understanding disease and how to treat it. It can do this for any of over 200 million known proteins within minutes and with a high degree of accuracy. Structures had been typically studied manually and could take a researcher an entire PhD programme to determine for a single protein.
Therapeutics 2.0 –– from empiricism to engineering
AI has the potential to bring more new medicines to market, making the development process quicker and less expensive.
The drug development process is effectively a search problem –– one in which the goal is finding a chemical or biological asset (often a molecule) that can successfully modulate a specific pathway within the body that is involved in a given disease, and thereby elicit a therapeutic effect that is consistent across a broad sample of patients, all while not triggering adverse side effects.
The search space is immense, and navigating it is a long, risky, multi-stage undertaking that typically involves a combination of trial and error, heuristics, and serendipity. This inflates the price of medicine and limits resource allocation to only disease areas with high economic potential.
AI can transform the process by accelerating design-build-test-learn cycles, optimising search efforts to only those likely to succeed, and shifting search workloads to the more efficient realm of silicon. Not only can this optimise process cost, but also lower the barrier to entry in terms of tackling disease and broaden the universe of those that can be tackled economically.
Seeking a fourtieth opinion?
AI has the potential to make clinical care more efficient, boosting the productivity of care providers.
It can support provider decision making, helping them extrapolate more insight from patient data –– whether genotypic or phenotypic. It can synthesise and distil complex medical information (e.g. patient records), pick up on hard-to-discern patterns (e.g. in medical images), interpret raw health data in real-time (e.g. from remote sensors), and make “educated” recommendations (e.g. screening based on genetic risk factors). This can enable more accurate and timely diagnoses, treatments, and interventions.
AI can also streamline their workflow, eliminating administrative burden. It can transcribe patient interactions during consultations and serve as a repository for patient records that can be interacted with in natural language –– akin to a super assistant.
Patients can, in turn, receive maximised value –– more effective and personalised care, and an overall better experience.
A new day for biotech
Such intelligent capabilities will find their way into the bio and health industry as next generation software products –– ones with seamless interfaces (as APIs or other UIs) and built by a new breed of entrepreneur. These new biotech founders are multilingual –– fluent across data, life, and computer sciences –– and of a tech-first mindset.
Indeed, new AI-native business models are emerging within biotech. Therapeutics companies, rather than being built around the development and commercialisation of a single lab-derived hypothesis or asset, are architected as data-driven discovery platforms, off which multiple assets can be engineered. Their proprietary data, which is generated at scale via high throughput biology, is their core competitive advantage versus single IP. An early wave of such companies emerged over the past decade, with currently several of their assets undergoing clinical trials.
The space is nascent and there is still a lot of ground to cover. The bio and health value chain is becoming more unbundled and traditional demarcations increasingly blurred. Advances in life science, machine learning, biotech tools, medical devices, manufacturing processes, software stacks, and open sourcing will dictate how the sector will evolve. Barriers to entry are likely to be lowered, exerting more competitive pressure on incumbents.
Move carefully and don’t break things
In contrast to mainstream tech, missteps in bio are serious. New products should be built with care, caution, and rigorous testing. While hallucination is passable in many current applications of generative AI, it is not in healthcare. Explainability is also critical, allowing machine predictions to be validated by experts. There are nuanced legal and ethical considerations at play.
There are also questions of privacy, protection, and bias when it comes to patient data, as well as questions about wellbeing –– specifically biosafety and biosecurity. There is arguably more need today for talent in the global regulatory sector than ever before, to address these issues and pave the way for the technology to be deployed responsibly.
The future hasn’t been written yet –– but all the ingredients are there to make this a giant leap forward for human health.
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