How AI will make us live longer and healthier lives
From reducing medical errors to personalizing cancer treatment, AI will transform health care for the better.
'AI Reality Bites' - Every day, new advancements in AI are announced - but what do they mean in practice?
Among the areas where we can apply AI, healthcare is the one people generally get the most excited about. For good reason, considering the obvious benefits we can reap. Everybody is on board with curing diseases. Most people have a very personal relationship with health, whether that’s because of an individual health crisis or one experienced by a family member or close friend. Naturally, the AI community is very eager to do its part in minimizing suffering and helping people live happy and healthy lives.
Furthermore, health costs are immense. Healthcare expenditure in the United States is rising by over $3 trillion annually. It's a staggering figure, reflecting a system struggling with inefficiencies and errors. This is not just a financial issue; it's a human one, affecting real lives daily. Still, many cases of chronic diseases get diagnosed too late and managed inappropriately. Many errors are being made. The situation is similar in other developed countries.
People in less developed countries still suffer from diseases we already learned to handle decades ago. Despite our best efforts, healthcare remains a multi-layered tragedy
Where does AI fit in? Thanks to new technological developments, here's now an opportunity to improve the accuracy and speed of disease diagnosis, especially in chronic conditions where early detection is critical. Through Natural Language Processing, we can make sense of vast amounts of medical data more efficiently, aiding in faster and more informed decision-making. Computer Vision's role in analyzing medical images can support doctors in identifying issues that might be missed by the human eye. Additionally, techniques like semi-and unsupervised learning have the potential to unearth new insights from medical data, enhancing our understanding of various health conditions. These AI technologies are not just theoretical possibilities; they are tools beginning to make tangible improvements in healthcare, although their full potential is yet to be realized.
Over the past few years, I have worked with many people trying to bring healthcare to the next level by implementing AI. It is easy to get lost when thinking about this topic, so I wrote this piece as a primer for those looking for an overview of the AI healthcare landscape.Â
The Big Data Breakthrough
There have been attempts at revolutionizing medicine through AI since the 1970s. However, these early systems' lack of advanced machine learning made them difficult to maintain, and they were never fully integrated into clinical practice. Since then, the landscape has dramatically changed. Not only have there been significant algorithmic advancements, such as the development of Convolutional Neural Networks (CNNs) and the transformer architecture, but we also now have access to greater computational power and an abundance of data.
A notable development in recent years has been the emergence of large clinical datasets that have been de-identified, addressing privacy concerns while providing a rich resource for medical research and AI development (e.g., MIMIC, Truven Market Scan, All of Us). These datasets, encompassing a wide range of health-related information, are proving invaluable in training AI systems more effectively and comprehensively.
The kinds of data available now are diverse and extensive. We are seeing a surge in the collection of unstructured data, such as clinical notes, lab tests, imaging, and vital signs. This data holds a wealth of information that AI can leverage for deeper insights. Furthermore, genomics and proteomics advancements provide detailed biological data that can revolutionize personalized medicine. Additionally, an often-overlooked source of health data comes from social media, where people share information about their physical activities, diets, and mental health. These varied data sources, from clinical records to social media posts, offer a comprehensive view of health and lifestyle, presenting new opportunities for AI applications in healthcare.
What the future could look like
For many applications in healthcare, the relevant data is not labeled, which makes unsupervised learning an essential approach here. Moreover, we are often interested in causal relationships when it comes to diagnosis and treatment. That means, we need more than naive supervised learning and correlation. Through more sophisticated techniques, like Deep Reinforcement Learning, we can identify causal relationships. I will now walk you through a few examples to give an overview of how AI can and partly already is being deployed in healthcare.
A New Era of Precision and Efficiency
First, we can improve how healthcare providers operate, increasing quality and access to care. Healthcare providers often face challenging working conditions, including limited resources and the need to make critical decisions. AI can significantly alleviate this pressure by supporting medical staff
One of the key areas where AI can assist is in the analysis and interpretation of patient information from electronic health records (EHRs). By extracting and processing data, AI systems can optimize medical workflows and help operators attend to patients more effectively.
AI can also be helpful in resource allocation and triage in ERs and clinics. By evaluating the severity and urgency of patients' conditions, AI can aid in prioritizing treatment, ensuring that those who need immediate attention receive it promptly. This not only optimizes the use of limited resources but also helps in managing patient flow more efficiently.
Another significant benefit of AI is its potential to reduce medical errors. Through continuous monitoring and analysis of patient data, AI systems can identify potential adverse events or complications early on. This early detection is vital in preventing escalation and ensuring timely intervention.
Integrating AI with a database of clinical guidelines and best practices can streamline the treatment process. Once a diagnosis is made, the system can immediately provide healthcare professionals with recommended treatment protocols. This speeds up the response time and minimizes the chances of errors, ensuring that care is consistent with the latest medical standards and research.
Fully realizing the benefits of AI in healthcare might require new organizational models in ERs and clinics. While increasingly common, the focus on digitized data-driven workflows is not yet a standard practice across the healthcare industry.Â
There are already companies out there that are pioneering a new approach to a data-driven clinic. For example, our Merantix venture Ovom transforms fertility medicine by setting up clinics based on data-driven workflows, allowing hyper-personalized care.
Examples: Ovom: Personalized and data driven fertility medicine. Memora Health: Automating care workflows.
The New Diagnostics in Healthcare's Digital Age
The advent of AI in medical diagnostics has opened new avenues for early and accurate disease detection. AI, particularly computer vision, is increasingly being utilized to analyze medical images such as mammograms, X-rays, and other scans, enhancing the accuracy and efficiency of diagnoses.
For instance, companies like Vara, another Merantix venture, are leveraging computer vision technology to detect breast cancer. By analyzing mammogram images, their models can help doctors identify potential signs of cancer. This accelerates the diagnostic process and increases the chances of detecting cancer at an early, more treatable stage
.Beyond image data, AI can also be applied in other areas of diagnosis. A prime example is wearable technology, like smartwatches, which can measure an individual's electrocardiogram (EKG) data. Traditionally, obtaining an EKG required a visit to the doctor. However, integrating sensors and AI algorithms in wearable devices makes it possible to continuously monitor heart rhythms in real time. We could detect abnormalities such as arrhythmias by analyzing the time-series data of a user's heart rate.Â
Examples: Vara: Breast cancer screening with AI. Freenome: Early cancer detection based on blood tests. InVision Medical Technology: Heart disease detection with AI.Â
Transforming Chronic Disease Care from Cancer to Diabetes
We have become much better at treating infectious diseases in the past few decades. However, chronic conditions like cancer, heart disease, diabetes, and dementia are still significant problems. Unlike acute illnesses, many chronic diseases exhibit varying trajectories, making treatment and management a nuanced and individualized process.
Take cancer, for instance. Its progression is rarely linear and can vary significantly from patient to patient. For certain types of cancer, there are multiple treatment options available, and choosing the most effective one can be akin to educated guesswork. The response to treatment varies widely among individuals and is influenced by many factors, including genetics, lifestyle, and the specific characteristics of the cancer.
In the future, ML models could be crucial in personalizing cancer treatment. We could analyze patient data - encompassing genetic information, treatment history, and tumor characteristics - to predict how different treatments might affect the disease's trajectory. This isn't just a theoretical possibility; efforts are underway to gather the necessary data. For example, the Multiple Myeloma Research Foundation (MMRF) is compiling detailed records on thousands of patients with Multiple Myeloma, tracking their symptoms, treatment responses, and biological data. Such comprehensive datasets are invaluable for training models to predict disease progression and treatment responses more accurately.
Other conditions like kidney disease follow a more predictable, albeit gradual, decline in organ function. It's crucial to determine a patient's current state and how quickly the condition is progressing for these diseases. However, accurately gauging this progression is challenging. AI can assist in this regard by analyzing trends in lab results, symptoms, and other health indicators to provide a clearer picture of the disease's trajectory.
Beyond diagnosis and treatment, AI also has the potential to revolutionize disease management, especially for conditions like Type-1 diabetes. Managing this disease requires constant vigilance over blood sugar levels, which can be stressful and complex. Machine learning can be employed to predict future insulin requirements more accurately. For instance, consider an AI algorithm tailored to a patient's unique metabolic data. This algorithm could estimate the impact of different foods on insulin levels. If a patient takes a photo of their meal, the algorithm could predict how that specific meal would affect their blood sugar, thereby aiding in more precise insulin dosing.
In summary, AI has the potential to significantly enhance our understanding and management of chronic diseases. By providing more personalized and accurate predictions and recommendations, AI can support clinicians in offering better care and empower patients to manage their health more effectively. As we continue to advance in this field, we can transform the landscape of chronic disease treatment and management, making it more proactive, personalized, and patient-centric.
Examples: Instalab: At home blood testing for biomarkers to optimize log-term health.
Shaping a Future of Personalized, Efficient Care
There are many more use cases for AI in healthcare, such as drug discovery or uncovering new disease subtypes through clustering. Integrating AI in healthcare and leveraging vast data sets holds immense potential. However, it is essential to note that this potential hinges on developing robust, fair, and accountable AI systems, particularly in areas where unsupervised learning plays a crucial role
At its core, the future of healthcare with AI is about prevention, personalization, and optimization. AI promises a healthcare system that not only anticipates and prevents diseases but also tailors treatments to individual patient needs while enhancing the efficiency of healthcare delivery. As we embrace this data-driven approach, we are poised to revolutionize healthcare, making it more effective, equitable, and accessible for all.
Thanks for reading, and please let me know what you think, what further opportunities in the market you see or what technological trends on the horizon you believe will make an impact. A special thanks goes out to Eduard HĂ¼bner, who was my great co-author for this piece.
- Rasmus
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