Artificial Intelligence (AI) and Machine Learning (ML) solutions are driving improvements and innovations in healthcare areas like disease diagnosis, clinical operations, drug development, and personalized treatment. These solutions enable healthcare providers, medical practitioners, and patients to make informed medical decisions.
The amount of healthcare data is growing at a fast pace owing to factors like a rising population, greater digitization, and a surge in the number of medical devices. To leverage the power of this data, businesses employ effective data storage and data management techniques such as building large-scale biomedical knowledge graphs that reflect relationships between different entities in the medical data.
In the context of the healthcare industry, knowledge graphs reflect the relationships between patient information, diagnoses, symptoms, etc. AI and ML solutions can use this structured data in knowledge graphs to develop predictive models that can enable healthcare providers to make better-informed decisions, provide personalized care, and improve patient outcomes.
Three Ways AI & ML Are Transforming Healthcare
In 2021, the global AI in healthcare sector was valued at $10.54 billion which went up to 13.82 billion in 2022. By 2029, it is forecasted to grow to a whopping $164.10 billion. This is not surprising, considering the transformative impact of AI and ML in healthcare. This growth is more evident than in the following three areas we will explore in this section: diagnosis, treatment outcomes, and administration.
1. Better Diagnosis
AI and ML solutions have the potential to help healthcare professionals in improving the accuracy and speed of medical diagnoses. Healthcare providers are using smart symptom detectors to recommend appropriate treatment plans to patients. Moreover, deep learning models can analyze large amounts of medical data and identify patterns and correlations that may be missed by doctors. Also, AI-powered imaging tools are being used to analyze X-rays, MRIs, or other radiology scans to improve the diagnostic process for clinical experts.
2. Improved Treatment & Patient Outcome
Clinical decision support systems using ML algorithms can use past and present patient data like medical histories, lifestyle factors, and genetic information to come up with customized treatment plans. Furthermore, the implementation of AI and ML techniques in healthcare can facilitate patient monitoring and adjust treatment procedures as and when required.
3. Enhanced Administration
Healthcare administration and operational workflows can also be streamlined using AI and ML solutions. These solutions can automate tasks such as scheduling appointments, managing and sifting through patient records, and processing insurance claims and medical bills among others. AI-powered medical chatbots can be used to provide basic medical assistance to patients, thereby reducing the burden on healthcare professionals.
4 Prominent Technologies Enabling AI & ML in Healthcare
Advanced AI technologies are having a transformative impact on the healthcare sector. In this section, we will look into the key technologies that help enable AI solutions in healthcare. These technologies help healthcare providers to process and analyze large amounts of medical data, extract meaningful insights, and make accurate predictions and recommendations on treatment outcomes.
1. Knowledge Graphs
Knowledge graphs use graph theory to store and manage data. They help medical experts better understand the relationships and patterns in medical data. Knowledge graphs integrate medical data from different sources which can then be used by AI systems to gain a comprehensive understanding of patients’ histories and clinical outcomes and to identify breakthrough treatments.
2. Natural Language Processing (NLP)
NLP enables machines to understand and process large amounts of human language data. In healthcare, this AI subdomain can be used to analyze patient records such as doctor’s notes, diagnoses, and treatment plans to extract important information and serve the patients better. Such as an NLP-powered chatbot can answer patients’ medical questions, send medication reminders, and schedule recurring appointments.
3. Computer Vision
Computer vision is an AI subdomain that enables computers to interpret and understand visual data. In the healthcare industry, computer vision solutions can be used to analyze medical images, such as X-rays and CT scans, to identify any injury or disease with more precision than medical experts. For instance, CV-based radiology solutions reduce the chances of misclassifying cancerous scans.
4. Predictive Analytics
Predictive analytics uses statistical algorithms and ML techniques to analyze historical data and make forecasts for future results. In the context of the medical industry, it can be used to classify critical patients, predict patient outcomes, predict readmission cases, run effective clinical trials, and develop customized treatment plans.
Using AI-Enabled Biomedical Knowledge Graphs
Biomedical knowledge graphs store relationships between biomedical entities such as patient records, genetic data, diseases, drug information, etc. These knowledge graphs consist of nodes that represent these entities and edges which depict the entity relationships. The development of biomedical knowledge graphs usually involves integrating data from various sources, including medical literature, electronic health records, and clinical trials.
Biomedical knowledge graphs can be applied in a variety of areas in the healthcare industry, including drug discovery and development, precision medicine, and medical surveillance. In this section, we will delve into these applications in more detail and explore how medical knowledge graphs are revolutionizing the healthcare industry when combined with AI-powered solutions.
1. Drug Discovery & Development
Biomedical knowledge graphs assist in drug discovery and development by integrating different data sources, such as clinical trial information, medical literature, and genetic data. A thorough analysis of this data can help prepare AI-driven simulations of existing treatments, leading to the discovery of more effective cures and interventions. For instance, AI tools such as DeepChem, DeepTox, DeepNeuralNetQSAR, ORGANIC, and Chemputer are helping researchers in different areas of drug discovery and development. Combining such solutions with knowledge graphs can enhance their performance.
2. Precision Medicine
Biomedical knowledge graphs also help in developing customized treatments for patients. They collect patient-specific data like medical history, previous treatment outcomes, and genetic data. AI solutions, then, use this data to recognize optimal treatment options and also predict treatment outcomes, leading to more efficient healthcare delivery.
3. Medical Surveillance
Biomedical knowledge graphs can be used to monitor and track disease outbreaks and public health trends. AI techniques can use the data from knowledge graphs to identify underlying patterns in the transmission of diseases. This can lead to the formulation of more effective public health interventions and disease prevention strategies.
Healthcare Challenges Addressed By Medical Knowledge Graphs
Biomedical knowledge graphs are proving to be a game-changing technology in healthcare, addressing some of the most significant challenges faced by the industry today. From amplifying patient safety to cutting healthcare costs, biomedical knowledge graphs are instrumental in tackling some of the primary challenges that have plagued the healthcare industry for decades.
1. Patient Safety
Patient safety incidents can pose significant challenges to healthcare providers. As per WHO, there is a 1 in 300 chance of a patient being harmed in a hospital. Biomedical knowledge graphs can enhance patient safety by keeping track of safety-related incidents in hospitals and other healthcare facilities. As a result, medical experts can develop targeted interventions to reduce the overall risk involved.
2. Healthcare Overheads
Expenses attributed to healthcare are a major challenge in the industry, especially in developing countries. Biomedical knowledge graphs can address this by optimizing healthcare resource utilization. Knowledge graphs can help in achieving this through the development of cost-effective treatment pathways, reducing the rate of unnecessary medical testing, and automating administrative tasks.
3. Data Integration
In healthcare, data is often dispersed across various sources, such as electronic health records and clinical guidelines. Biomedical knowledge graphs help overcome this challenge by accumulating data from these fragmented sources into a unified representation. This also benefits AI solutions in the healthcare industry as a more comprehensive view of patient data results in more accurate predictions and treatment outcomes.
Leverage NLP-Powered Biomedical Knowledge Graphs With Wisecube
Wisecube’s Knowledge Graph Engine is an open-source knowledge graph that utilizes state-of-the-art AI and NLP techniques to process data from different sources and extract human-understandable insights through high-quality analytics.
Biomedical researchers and healthcare providers can use Wisecube’s knowledge graph to access a unified view of medical data, identify hidden patterns and trends, and uncover breakthrough developments and discoveries.