AI in Healthcare: 5 Use Cases and 1 challenge

The accelerated developments in machine learning and artificial intelligence in healthcare have set the stage for some interesting transformations. By enabling better care for patients, optimizing processes, and generating new opportunities for medical research and treatment, these technologies are indeed going to change the healthcare industry radically. According to a study conducted by the Berkeley Research Group, it’s estimated that AI will be able to save the US healthcare system approximately $360 billion annually as well as boost diagnostic accuracy by an average of 15 to 20%. 

In this post, I will give you insights into how AI and healthcare are working hand in hand, together with real-world examples. We will also examine the main barrier to AI adoption in healthcare – security and compliance.

Sponsored

class=”wp-block-heading”>1. Predictive analytics for enhanced patient care

Let’s start by exploring predictive analytics. Predictive analytics leverages AI to analyze historical and real-time data to forecast future events. In healthcare, this means predicting patient outcomes, potential complications, and possible disease outbreaks.

• Disease Prediction and Prevention: By analyzing patient data, AI can help identify patterns that indicate a high risk of certain diseases. For example, IBM Watson Health has been used to predict heart disease by analyzing electronic health records and identifying at-risk patients early.
• Hospital Readmission Reduction: AI models can predict which patients are at risk of readmission, allowing healthcare providers to implement preventive measures. For instance, Penn Medicine developed an AI system that forecasts the likelihood of patient readmissions, allowing for more targeted or aggressive interventions.

2. AI diagnosis using medical imaging

Another area that AI has had an impact on is medical imaging and diagnostic capabilities. AI-powered medical imaging and diagnostics are enhancing the speed and accuracy of our disease detection abilities. Advanced image recognition algorithms are able to analyze medical images such as X-rays, MRIs, and CT scans to detect abnormalities with high precision. 

• Early Detection of Diseases: Google’s DeepMind developed an AI algorithm that can detect over 50 eye diseases from retinal scans with accuracy comparable to physicians. This early detection increases the chances for prompt treatment and better patient outcomes.
• Automated Diagnostics: Zebra Medical Vision provides AI-driven solutions that can automatically analyze medical imaging data to detect conditions such as breast cancer, liver disease, and cardiovascular issues, significantly reducing the workload on radiologists.

3. Personalized medicine

The third point I’d like to touch on is in the realm of proactive medical approaches; personalized medicine. Personalized medicine tailors medical treatment to the individual characteristics of each patient. AI enables the analysis of large amounts of data, which includes genetic information, to create personalized treatment plans.

• Genomic Analysis: Foundation Medicine uses AI to analyze genomic data from cancer patients, identifying genetic mutations and suggesting targeted therapies. This approach allows for highly personalized treatment plans that improve patient outcomes.
• Predictive Modeling: Tempus uses AI to analyze clinical and molecular data to predict how patients will respond to specific cancer treatments, enabling more precise and effective therapies.

4. Drug discovery and development

Drug discovery is an important aspect of healthcare. It is essentially the process of identifying and developing new medications in order to treat different diseases and conditions. It often involves a series of different stages, which include the initial discovery of possible drug candidates, preclinical testing, and clinical trials. This process is to ensure that the drug is both safe and efficient for patients. This traditional drug discovery process is lengthy and expensive. Instead, AI can accelerate the process by analyzing large datasets to identify potential drug candidates and predict their efficacy at a quicker pace. 

• Compound Screening: Atomwise uses AI to screen millions of chemical compounds to identify those with the potential to treat diseases like Ebola and multiple sclerosis. Their AI technology helps narrow down potential drug candidates much faster than traditional methods.
• Clinical Trial Optimisation: Insilico Medicine employs AI to design and simulate clinical trials, identifying the most promising drug candidates and optimal patient populations. This approach reduces the time and cost of bringing new drugs to market.

5. Virtual medical assistants

AI-powered virtual assistants and chatbots are finding a place in healthcare, especially in the fast and convenient access to information and support for patients. For example, Cleveland Clinic has applied AI in supporting jobs, scheduling, handling formerly laborious tasks, and making operations more efficient to be able to concentrate on direct patient care. 

To enhance the large language models (LLMs) used in their virtual health assistants, organizations can take advantage of a database like Charmed OpenSearch that supports Retrieval Augmented Generation (RAG). This integration allows the chatbots to deliver real-time information, within context by gathering pertinent data from an extensive knowledge base. Using vector databases along with OpenSearch’s k-nearest neighbors (KNNs) functions empowers healthcare organizations to develop chatbots that increase patient interaction quality and operational effectiveness.   

The elephant in the room: Security and compliance

While AI in healthcare holds a lot of power and benefit, security and compliance remain some of the most critical concerns, and the most significant barrier to AI adoption. ML models require large sets of patient data, which ultimately increases the risk of privacy breaches or data exploitation. In the BRG report, it states that both data privacy and security are top priorities for healthcare organizations. This is unsurprising given the wealth of sensitive data these organizations handle – such as EHRs, genomic data, and much more – as well as the extensive regulation that the industry is subject to.

AI solutions in healthcare also often use large amounts of open source packages, which means the possible introduction of vulnerabilities. This is further heightened by the complexities of maintaining security throughout the entire ML lifecycle, starting from data ingestion all the way to model deployment. 

Lastly, ML holds certain risks such as data poisoning that could potentially undermine the integrity of the AI models. This can result in the manipulation of either the data, or the model, to generate incorrect outcomes – which can be a big issue in healthcare. Organizations need to apply systems that are capable of monitoring and detecting anomalies as well as those capable of retraining pipelines. 

All of these challenges can make it difficult to safely implement AI and take advantage of the innovations and use cases outlined above. But you can mitigate these challenges with the right technologies. At Canonical, we’ve designed our end-to-end enterprise AI solution to maximize security at every layer of the stack.

Canonical’s Ubuntu Pro offers vulnerability fixes for over 30,000 open source packages, including critical ML tools and frameworks like Python, MLflow, TensorFlow and PyTorch. This helps minimize the risk and complexity of utilizing these open source tools. 

As mentioned before, data privacy is of utmost concern, particularly as models process and infer highly sensitive data. Confidential AI on Ubuntu uses confidential computing to protect data during inference. This can make it possible to use sensitive data while remaining compliant with industry regulations. 

Last but not least, Charmed Kubeflow can integrate security features like authentication and network isolation into AI workflows. By ensuring that there is end to end protection across the whole ML lifecycle, you can protect the integrity of your models and minimize the risk of data poisoning.

The AI revolution in healthcare is making a significant difference in patient care, operational efficiency, and the cycle of medical innovations. As AI/ML advances, it allows for more innovation and better health outcomes by both bending the cost curve and pushing medical research boundaries further. Canonical’s AI stack offers a path to securely maximizing the impact of AI on the healthcare industry from the base OS, all the way to the application layer.

Further Reading

• BeWell Case Study: Scaling up patient self-testing services with Ubuntu Core
• Guide to MLOps
• MLOps on Highly Sensitive Data
• Run AI at Scale
• Machine learning security risks
• Ubuntu for confidential AI