{"id":620,"date":"2020-02-11T15:12:10","date_gmt":"2020-02-11T15:12:10","guid":{"rendered":"https:\/\/nilg.ai\/?p=620"},"modified":"2022-09-14T12:37:35","modified_gmt":"2022-09-14T12:37:35","slug":"ai-in-healthcare-from-disease-to-society","status":"publish","type":"post","link":"https:\/\/nilg.ai\/en_us\/202002\/ai-in-healthcare-from-disease-to-society\/","title":{"rendered":"AI in healthcare: from disease to society"},"content":{"rendered":"

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Artificial Intelligence certainly has an impact on every industry and aspect of our daily lives. One of the hottest topics is the application of AI in healthcare though still mostly in a retrospective (<\/span>in silico<\/span><\/i>) validation scenario and not on actual large-scale randomized trials. <\/span>While we work on achieving General Artificial Intelligence (or at least as general as a whole knowledge area), the way we have been thinking about AI in healthcare is by addressing very specific tasks<\/strong>, one at a time. In the meantime, that\u2019s good enough to cause a major impact in the area while we discover how to learn more holistic representations<\/strong> (either deep or engineered) of the human body.<\/span><\/p>\n

As mentioned by Eric Topol<\/a> on his book \u201cDeep Medicine<\/a>\u201d [1], AI has the potential to make medicine human again, to empower doctors by building better information aggregation tools, better pattern detection techniques, better ways to register information from the visits so they can focus on their actual goal: caring about patients. In order to achieve this ultimate goal, we need to move from our very narrow approach of thinking about patients at an exam-level<\/strong> (e.g., predicting if a patient has cancer from a single image) to a wider approach<\/strong>, a patient-level approach that relies on the whole spectrum of data we have about each person. At the same time, we need to move from populational-driven findings to personalized medicine<\/strong>, where a person is no longer within normal limits (WNL) but on his personalized risk patterns.<\/span><\/p>\n

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Three levels of granularity<\/span><\/h2>\n

While our main focus nowadays is on targeting patient-level, or as I prefer to say, disease-level applications, there is a widely unexplored range of applications we could be tackling in AI in other levels of healthcare. Namely, there is an equivalent (if not higher) potential for optimization at a hospital (i.e., healthcare facility) level and at a government (i.e., policy-making and research) levels. So, while addressing topics such as cardiovascular diseases will prevent the death of 18 million patients per year worldwide [2]<\/span>, improving hospital management would directly impact the 3.5 billion people that have access to healthcare [3]<\/span>, and addressing global policy-making challenges will potentially impact the whole world population.<\/span><\/p>\n

As you might be thinking, tackling exam-level issues is way more actionable from an individual\/company perspective. This can be easily corroborated by inspecting the program of the top conferences in Machine Learning in Medicine (<\/span>ISBI<\/span><\/a>, <\/span>MICCAI<\/span><\/a>, <\/span>EMBC<\/span><\/a>, <\/span>MIDL<\/span><\/a>) whose sessions and papers are mostly in this direction. Why? It is easier to measure the impact, to move from decisions to actions, to get standardized data, to get to something usable. It\u2019s easier and has a higher impact in the short term. Period.<\/strong><\/span><\/p>\n

So, it is expected that most of the initiatives will address this applicational level. However, we need to keep in mind the full picture<\/strong> and keep asking ourselves what can we do at a lower level of the pyramid. While we will cover each one of these levels in detail in future posts in this blog, we wanted to briefly summarize the broad opportunities in each one of them.<\/span><\/p>\n

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Patients<\/span><\/h4>\n

AI applied to patients has monopolized the research attention in the last decade. While being the tip of the iceberg, it is a tip with a major impact on our daily lives<\/strong>. AI combined with mobile devices and telemedicine can promote equal access to high-quality diagnoses regardless of the patient\u2019s geographic location<\/strong>.<\/span><\/p>\n

Most use cases in this area focus on the triage\/screening\/diagnosis of specific diseases and sensing modalities. Areas with proven success are those where the decision process involves recognizing visual patterns<\/strong> (e.g., dermoscopy, radiology, among others). In this direction, we have been working closely<\/a> with MobileODT<\/a> to fight cervical cancer<\/strong> -a preventable major cause of deaths among women- using mobile devices and Deep Learning<\/a>. In the national\/Portuguese landscape, there are multiple references of AI-driven companies for specific health and well-being problems: Adapttech<\/a>, CardioID<\/a>, SWORD Health<\/a> and iLOF<\/a>.<\/span><\/p>\n

As we move to a more decentralized data acquisition setting with low-cost devices, tangential use cases come to the table to ensure equal quality of service<\/strong>. For instance, methodologies to automatically validate the compliance<\/strong> of the acquisition protocol (especially relevant with untrained personnel), measuring the quality<\/strong> of the data and interacting with the user to help him improve the procedure. In parallel, as we move from small datasets to massive flows of data, we need tools to summarize<\/strong> the most relevant data, both intra and inter-patient. Here, we can observe for instance the efforts on data summarization for capsule endoscopy which records hours of data from each patient.<\/span><\/p>\n

Finally, with the appearance of better treatments and procedures to fight each disease, the patient\u2019s adherence<\/strong> to the protocol is key. Thereby, predicting which patients aren\u2019t going to follow the treatment and what actions can we make to improve their adherence is something we need to focus on in AI. See the case of Keheala<\/a> on how we can impact treatment adherence.<\/span><\/p>\n

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Course<\/h6>

The Machine Learning Spectrum<\/h3>\n\t\t\t

Do you want to get insights into how we have built such models?<\/p>\n\t\t\tLearn More<\/a>\n\t\t<\/div>\n\t<\/div><\/span><\/p>\n

Hospitals<\/span><\/h4>\n

It\u2019s estimated that 20% of the healthcare costs are related to administrative tasks [1]<\/span>. These tasks involve among other things, finding the right billing codes<\/strong> for insurance reimbursement, a task that could be partially automated using RecSys to filter the most relevant codes (we will publish a blog<\/a> post covering this specific topic soon, stay tuned!)<\/em>.<\/span><\/p>\n

Resource management<\/strong> is an inexhaustible source of use cases for AI. Here, we can go from forecasting demand<\/strong> for supplies to optimizing resource allocation. Being each minute in a waiting room a life-threatening cause in multiple scenarios, removing each potential bottleneck from the hospital entry door to the patient\u2019s discharge is a must<\/strong>. Matching uncertain\/difficult cases with more skilled personnel while ensuring a pedagogic diversity of the cases analyzed by each doctor is also a relevant optimization problem from an AI perspective, where a trade-off exists between the short-term individual patient care and the long-term professional training of the staff.<\/span><\/p>\n

If we want to transform medicine, we need to focus on improving the overall experience<\/strong>, creating personal relationships between doctors and patients. Therefore, we need algorithms to match the right team of professionals (i.e., physicians, nurses, etc.) with the right patients moving from plain availability to affinity.<\/span><\/p>\n

Society\/Government<\/span><\/h4>\n

As it is the case for hospitals but on a larger scale, governments need to focus on resource allocation<\/strong> and forecasting<\/strong> to match populational needs with quality services. Furthermore, while hospitals tend to focus on daily incoming cases, governments need to look at the whole picture, diving into the appearance of epidemics<\/strong> on optimizing for improving the long-term healthcare system. <\/span>Governments can use AI to detect surplus or unmatching stocks with demand<\/strong>, proactively optimizing the sharing of resources between the centers in a national healthcare system to adjust to drifts in the requirements.<\/span><\/p>\n

Regulatory demands and policy-making are typically orchestrated by national organizations. Therefore, it is of their best interest to take advantage of AI to explore better screening programs, resource allocation, compliance verification of devices and facilities, among others. Accelerating these processes without compromising quality<\/strong>, will allow their citizens to get quick access to the latest discoveries in the field.<\/span><\/p>\n

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Information flow<\/span><\/h2>\n

As AI becomes pervasive across multiple decision processes, the potential for model-to-model interaction<\/strong> increases. Thus, we can expect AI predictions to enable other decision workflows in any direction of the pyramid: top-to-bottom, bottom-to-top and sidewise.<\/span><\/p>\n