Over the next five years, clinical AI will continue automating repetitive diagnostic tasks, especially within medical imaging.
Look a little further ahead, five to 10 years out, and the technology will regularly tap multimodal datasets by way of driving precision therapeutics. Ambient intelligence, synthetic biology immunomics and robot-assisted treatments should begin to abound.
And beginning around 2031, AI ought to be helping provider organizations “achieve a state of precision medicine through AI-augmented healthcare and connected care.”
In the latter window, autonomous virtual assistants could deliver precision preventive medicine while networked provider orgs offer closely connected care via single, shared digital infrastructures.
The forecasts come from researchers in the U.K. who lay out an educated projection in the July edition of Future Healthcare Journal, a publication of the Royal College of Physicians.
Senior author Bryan Williams, MD, chair of medicine and director of biomedical research at University College London in the U.K., and colleagues flesh out a few particulars they expect to see in the three consecutive time windows.
Among their forecasts:
1. AI today and in the near future. While advances in AI for precision diagnostics have made their mark for various discrete applications—diabetic retinopathy, lung-cancer screening—today’s systems are not “reasoning engines” with common sense or intuition, Williams and colleagues point out.
Today’s AI “resembles a signal translator, translating patterns from datasets,” they observe.
But by 2026, AI will drive broadening adoption of healthcare-specific Internet of Things technologies, virtual assistants, augmented telehealth and personalized mental healthcare, Williams and co-authors state.
2. AI in the medium term (the next five to 10 years). In this period, provider organizations and medical practices will progress from merely adopting healthcare AI to proactively pushing tech vendors to develop novel AI systems for solving specific clinical challenges.
“[W]e propose that there will be significant progress in the development of powerful algorithms that are efficient (e.g., require less data to train), able to use unlabeled data, and can combine disparate structured and unstructured data including imaging, electronic health data, multi-omic, behavioral and pharmacological data,” the authors write.
3. AI in the long term (10 years out and beyond). Williams and colleagues anticipate healthcare AI to become still more intelligent, allowing provider organizations to tailor precision medicine on a patient-by-patient basis.
“Healthcare will shift from the traditional one-size-fits-all form of medicine to a preventative, personalized, data-driven disease management model that achieves improved patient outcomes (improved patient and clinical experiences of care) in a more cost-effective delivery system,” they write.
“It is clear that we are at a turning point as it relates to the convergence of the practice of medicine and the application of technology, and although there are multiple opportunities, there are formidable challenges that need to be overcome as it relates to the real world and the scale of implementation of such innovation,” Williams et al. comment.
A key to getting AI to deliver on its formidable promise, they add,
... will be an expansion of translational research in the field of healthcare applications of artificial intelligence. Alongside this, we need investment into the upskilling of a healthcare workforce and future leaders [who] are digitally enabled, and understand and embrace, rather than being intimidated by, the potential of an AI-augmented healthcare system.”
The study’s lead author is Junaid Bajwa, MD, MBA, London-based chief medical scientist at Microsoft Research.
The paper is available in full for free.