Artificial intelligence correctly triaged 41 of the 50 patients in the study (82%). Surgeons had an accuracy triage rate of 70% (35 patients), intensivists 64% (32 patients), and anaesthesiologists 58% (29 patients). The number of incorrect triage decisions was lowest for AI (18%), followed by 30% for surgeons, 36% for intensivists, and 42% for anaesthesiologists.
These are the kinds of contexts where we’ll increasingly see the use of machine learning algorithms to “provide guidance” to clinicians: high stakes decision-making scenarios where the correct outcome relies on the integration of data from a wide variety of clinical domains that are not optimised for human cognition. It’s just not possible for a human being – or team of human beings – to track the high number of relevant and inter-related variables that influence these kinds of clinical outcomes.
It’s a bit content-heavy and not as graphic-y as I’d like but c’est la vie.
I’m quite proud of what I think is a novel innovation in poster design; the addition of the tl;dr column before the findings. In other words, if you only have 30 seconds to look at the poster then that’s the bit you want to focus on. Related to this, I’ve also moved the Background, Methods and Conclusion sections to the bottom and made them smaller so as to emphasise the Findings, which are placed first.
Here is the tl;dr version. Or, my poster in 8 tweets:
Aim: The aim of the study was to identify the ways in which machine learning algorithms are being used across the health sector that may impact physiotherapy practice.
Image recognition: Millions of patient scans can be analysed in seconds, and diagnoses made by non-specialists via mobile phones, with lower rates of error than humans are capable of.
Video analysis: Constant video surveillance of patients will alert providers of those at risk of falling, as well as make early diagnoses of movement-related disorders.
Natural language processing: Unstructured, freeform clinical notes will be converted into structured data that can be analysed, leading to increased accuracy in data capture and diagnosis.
Robotics: Autonomous robots will assist with physical tasks like patient transportation and possibly even take over manual therapy tasks from clinicians.
Expert systems: Knowing things about conditions will become less important than knowing when to trust outputs from clinical decision support systems.
Prediction: Clinicians should learn how to integrate the predictions of machine learning algorithms with human values in order to make better clinical decisions in partnership with AI-based systems.
Conclusion: The challenge we face is to bring together computers and humans in ways that enhance human well-being, augment human ability and expand human capacity.
Reference list (download this list as a Word document)
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The algorithm could handle this uncertainty by computing multiple solutions and then giving humans a menu of options with their associated trade-offs. Say the AI system was meant to help make medical decisions. Instead of recommending one treatment over another, it could present three possible options: one for maximizing patient life span, another for minimizing patient suffering, and a third for minimizing cost. “Have the system be explicitly unsure and hand the dilemma back to the humans.”
I think about clinical reasoning like this; it’s what we call the kind of probabilistic thinking where we take a bunch of – sometimes contradictory – data and try to make a decision that can have varying levels of confidence. For example, “If A, then probably D. But if A and B, then unlikely to be D. If C, then definitely not D”. Algorithms (and novice clinicians) are quite poor at this kind of reasoning, which is why they’ve traditionally not been used for clinical decision-making and ethical reasoning (and why novice clinicians tend not to handle clinical uncertainty very well). But if it turns out that machine learning algorithms are able to manage conditions of uncertainty and provide a range of options that humans can act on, given a wide variety of preferences and contexts, it may be that machines will be one step closer to doing our reasoning for us.