When infants are developing we are encouraged to give them tummy time, as it helps to strengthen their neck, shoulder, and arm muscles, promoting motor skills to prepare them for upcoming milestones like crawling and sitting up. More specifically, we as humans were designed to crawl before walking for one simple reason: to gain proprioceptive input to train our brains, our neural networks, about how the physics of the outside world works. Similarly, AI has an instrumental role in shaping the expertise and situational awareness of clinicians professionally as they begin to adopt it more widely in clinical practice.
When driving, if my car drifts over the solid line on the pavement I get an audible beep, as if to say, “Are you sure you want to do that?” AI can give real-time feedback to aid in auxiliary awareness and decision making as we navigate the world. In the same way, the way that clinicians operate is about to radically change. Clinical decision support will be present and pervasive in many applications, from complex instrumentation for surgical interventions down to simple more routine instrumentation like the stethoscope. The extraordinary thing about the AI of the future is that it will move beyond the function of a sanity-checker. It will function as a second pair of eyes, and even offer an unseen perspective. It will connect the dots with data points the human clinician cannot recall. It will bring new information to their attention from the latest research, enriching the corpus of input available to trigger faster, more precise clinical decision making.
And while AI will not have all the answers and deterministically say “here’s the exact diagnosis”, it will at least provide a contextualized probabilistic framework, rooted in sophisticated reasoning, to say “here are the options of what it might be and why”. In other words, it is not a validation mechanism. With humans in the loop, models are being trained to give suggestions for differential diagnoses along with the reasoning behind their suspicion. You see, AI works in probabilities using patterns learned from the data. It does not work in absolutes. Healthcare data is complex and thus is best suited to augment, and not replace, clinical decision making with humans and their nuanced understanding of the world. It is hardly feasible to identify practical scenarios where the human clinician is not the final arbiter of patient care. This also means that as clinicians begin to rely more on AI, they will also need to cultivate new skills, particularly around critical interpretation of AI-generated insights, to fully leverage its potential in patient care.
The silent hum of AI in the background can be the vestibular input our nervous system needs to be increasingly more aware of the environment around us. What’s difficult for people to acknowledge is that human-led clinical decision making and navigation is already deeply biased and flawed by being incredibly subjective, with a bias towards that clinician’s experience. What’s exciting about AI is that it will enable a single human clinician to have access to the experience and wisdom of a multitude of experienced clinicians. It will be more in tune with clinical outcome rather than practice pattern. It will be savvy to which therapies are uniquely suited to be efficacious for patients like the one in front of them, and those which are not. As AI brings more insights to the point of care, it will widen the aperture of clinicians’ minds to consider greater possibilities and be even more capable to make more comprehensive, evidence-driven decisions. In a sense, AI creates an opportunity for human adaptive intelligence.
The modus operandi of clinicians is about to radically change. Just as our physical senses extend our cognition in the real-world and allow for sensory integration and cognitive mapping, so too will AI foundationally function bi-directionally, as part of a broader sensory system, to allow clinicians to adapt and refine their decisions accordingly. AI will be that nudge to say, “Are you sure? Have you considered X?” which dynamically integrates clinicians with richer medical insights for more precise and individualized care. More sophisticated clinical workflows will facilitate synergistic human-AI collaboration leveraging carefully synthesized collective intelligence to bring clinicians greater interpretive acuity and certitude. They will be more attuned to the broader clinical landscape which can enhance the standard of care. What’s more, as AI has the ability to analyze health data on both the population and individual levels, it can enable a dual-layered decision-making paradigm that strengthens the clinicians’ ability to make both broad and specific recommendations and decisions. Acting as a conduit between theoretical knowledge and practical application, AI will foster a more adaptive and resilient healthcare delivery model as standards of medical excellence continue to evolve.
Photo: metamorworks, Getty Images
Emily Lewis, MS, CPDHTS, is an accomplished leader in digital health and AI. With nearly two decades of experience, she has made significant contributions to clinical decision support systems, using machine learning to accelerate patient treatment times. Emily has spearheaded the integration of large language models into electronic health records to reduce clinical workloads, and led the development of a software as a medical device (SaMD) tool now recognized by the FDA and EMA, with widespread implementation across multiple health systems.
A strong advocate for AI interpretability and transparency, Emily has developed frameworks and best practices that ensure compliance with international regulations. She has contributed to the advancement of post-market surveillance standards, laying out systems for continuous AI performance monitoring. Known for fostering cross-industry collaborations, Emily has driven partnerships between pharmaceutical companies, healthcare providers, and AI startups. She has also established educational programs to enhance healthcare AI literacy among clinicians and industry professionals. She has led multi-disciplinary teams and shaped corporate AI strategy and data governance. Through her thought leadership, speaking engagements, and publications, Emily remains at the forefront of healthcare innovation.
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