AI can be really destructive and not know it. So the AIs that recommend new content in Facebook, in YouTube, they’re optimized to increase the number of clicks and views. And unfortunately, one way that they have found of doing this is to recommend the content of conspiracy theories or bigotry. The AIs themselves don’t have any concept of what this content actually is, and they don’t have any concept of what the consequences might be of recommending this content.
We don’t need to worry about AI that is conscious (yet), only that it is competent and that we’ve given it a poorly considered problem to solve. When we think about the solution space for AI-based systems we need to be aware that the “correct” solution for the algorithm is one that literally solves the problem, regardless of the method.
This matters in almost every context we care about. Consider the following scenario. ICUs are very expensive for a lot of good reasons; they have a very specialised workforce, a very low staff to patient ratio, the time spent with each patient is very high, and the medication is crazy expensive. We might reasonably ask an AI to reduce the cost of running an ICU, thinking that it could help to develop more efficient workflows, for example. But the algorithm might come to the conclusion that the most cost-effective solution is to kill all the patients. According to the problem we proposed, this isn’t incorrect but it’s clearly not what we were looking for, and any human being on earth, including small children, will understand why.
Before we can ask AI-based systems to help solve problems we care about, we’ll need to first develop a language for communicating with them. A language that includes the common sense parameters that inherently bound all human-human conversation. When I ask a taxi driver to take me to the airport “as quickly as possible”, I don’t also need to specify that we shouldn’t break any rules of driving, and that I’d like to arrive alive. We both understand the boundaries that define the limits of my request. As the video above shows, an AI doesn’t have any “common sense” and this is a major obstacle for progress towards having AI that can address real world problems beyond the narrow contexts where they are currently competent.
It’s a nice coincidence that my article on machine learning for clinicians has been published at around the same time that my poster on a similar topic was presented at WCPT. I’m quite happy with this paper and think it offers a useful overview of the topic of machine learning that is specific to clinical practice and which will help clinicians understand what is at times a confusing topic. The mainstream media (and, to be honest, many academics) conflate a wide variety of terms when they talk about artificial intelligence, and this paper goes some way towards providing some background information for anyone interested in how this will affect clinical work. You can download the preprint here.
The technology at the heart of the most innovative progress in health care artificial intelligence (AI) is in a sub-domain called machine learning (ML), which describes the use of software algorithms to identify patterns in very large data sets. ML has driven much of the progress of health care AI over the past five years, demonstrating impressive results in clinical decision support, patient monitoring and coaching, surgical assistance, patient care, and systems management. Clinicians in the near future will find themselves working with information networks on a scale well beyond the capacity of human beings to grasp, thereby necessitating the use of intelligent machines to analyze and interpret the complex interactions between data, patients, and clinical decision-makers. However, as this technology becomes more powerful it also becomes less transparent, and algorithmic decisions are therefore increasingly opaque. This is problematic because computers will increasingly be asked for answers to clinical questions that have no single right answer, are open-ended, subjective, and value-laden. As ML continues to make important contributions in a variety of clinical domains, clinicians will need to have a deeper understanding of the design, implementation, and evaluation of ML to ensure that current health care is not overly influenced by the agenda of technology entrepreneurs and venture capitalists. The aim of this article is to provide a non-technical introduction to the concept of ML in the context of health care, the challenges that arise, and the resulting implications for clinicians.
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.
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This has led companies interested in applying AI to healthcare to find different ways to scoop up as much data as possible. Google partnered with Stanford and Chicago university hospitals to collect 46 billion data points on patient visits. Verily, also owned by Google’s parent company Alphabet, is recruiting 10,000 people for its own long-term health studies. IBM has spent the last few years buying up health companies for their data, accumulating records on more than 300 million people.
I’ve pointed to this problem before; it’s important that we have patient data repositories that are secure and maintain patient privacy but we also need to use that data to make better decisions about patient care. Just like any research project needs carefully managed (and accurate) data, so too will AI-based systems. At the moment, this sees a huge competitive advantage accrue to companies like Google, that can afford to buy that data indirectly by acquiring smaller companies. But even that isn’t sustainable because there’s “no single place where all health data exists”.
This decision by the Ontario government seems to be a direct move against the current paradigm. By making patient data available to via an API, researchers will be able to access only the data approved for specific uses by patients, and it can remain anonymous. They get the benefit of access to enormous caches of health-related information while patient privacy is simultaneously protected. Of course, there are challenges that will need to be addressed including issues around security, governance, differing levels of access permissions.
And that’s just the technical issues (a big problem since medical software is often poorly designed). That doesn’t take into account the ethics of making decisions about individual patients based on aggregate data. For example, if an algorithm suggests that other patients who look like Bob tend not to follow medical advice and default on treatment, should medical insurers deny Bob coverage? These and many other issues will need to be resolved before AI in healthcare can really take off.
There are any number of reasons why people don’t get medical care or don’t follow up on a referral to a specialist. They might not think they have a serious problem. They might lack time off work, reliable transportation, or health insurance. And those are problems AI alone can’t solve.
There’s a good point to be made here; an algorithm may be 100% accurate in diagnosing a condition but the system can still fail for many reasons, one of which may be the all too human characteristic of ignoring medical advice. Of course, there are many good reasons for why we may not be able to follow the advice, which is mentioned in the article. However, the point is that, even if an algorithm gets it absolutely right, it may still not be the solution to the problem.
Note: I mentioned this story a few posts ago. It’s going to be interesting to follow it and see how the system fares in the uncertainty of real-world situations.
So if you think about it, wireless signals, they travel through space, they go through obstacles and walls and occlusions, and some of them, they reflect off our bodies, because our bodies are full of water, and some of these minute reflections, they come back. And if, just if, I had a device that can just sense these minute reflections, then I would be able to feel people that I cannot see. So I started working with my students on building such a device, and I want to show you some of our early results.
So here is our device, transmitting very low power wireless signal, analyzes the reflections using AI and spits out the sleep stages throughout the night. So we know, for example, when this person is dreaming. Not just that … we can even get your breathing while you are sitting like that, and without touching you. So he is sitting and reading and this is his inhales, exhales. We asked him to hold his breath, and you see the signal staying at a steady level because he exhaled. And I want to zoom in on the signal. These are the inhales, these are the exhales. And you see these blips on the signal? These are not noise. They are his heartbeats. And you can see them beat by beat.
Two weeks ago I presented some of my thoughts on the implications of AI and machine learning in clinical practice and health professions education at the 2018 SAAHE conference. Here are the slides I used (20 slides for 20 seconds each) with a very brief description of each slide. This presentation is based on a paper I submitted to OpenPhysio, called: “Artificial intelligence in clinical practice: Implications for physiotherapy education“.
As part of our commitment to continuing professional development (CPD) in South African healthcare, we’re required to accumulate 5 ethics credits every year. Yesterday I gave a presentation to the staff in our department in order to fulfill this requirement. It went quite well, although being my first time I felt pretty unprepared.
I learnt a lot from the experience and together with the feedback I got from my colleagues, will be able to refine the workshop for next year. One of the main suggestions was to add more interactivity to the session. I definitely agree that this is one area I could’ve improved on, especially with the view to making it more dynamic.
I’m going to split my blog posts up according to the different sessions, just for ease of reference i.e. a few posts, rather than one very long one. Here are my notes from the first keynote of the day, from Professor Bill Burdick.
If you don’t continue the momentum for change, you’re going to be left behind
We need to start system capacity building at the undergraduate level
It turns out that GDP isn’t the most important factor in determining life expectancy, nor is the number of doctors / 1000 population, nor is sanitation and literacy, although there is an increasing trend for each of these variables. Health spending as a % of GDP also isn’t the major factor. Changing each of these independent variables isn’t going to necessarily enhance life expectancy, but changing all of them will.
Fewer children per woman = greater life expectancy, also the younger a woman is at marriage, the earlier she dies
Taking these factors into account, what must we as health educators do to have an impact on improving health?
Academics have the skills to pull in, analyse and interpret data, and to disseminate the resultant new knowledge, which clinicians need to make evidence based decisions to enhance clinical care.
It is important for academics / health educators to integrate with the public sector by engaging with the community, training other health workers, incorporate health professionals in the management sector, and to engage with public policy makers