The healthcare sector is undergoing a rapid transformation globally due to the new advancements in artificial intelligence (AI) technologies such as machine learning, natural language processing, deep learning, context-aware processing, and Intelligent Robotics.
AI is slowly becoming a fool-proof and scalable solution, covering the entire spectrum of clinical applications from prevention to diagnostics to treatment and non-clinical applications such as patient engagement workflow process and claims processing, thanks to the increasing volumes of healthcare data captured using cloud-based applications.
Many hospitals around the world have already implemented AI to diagnose critical diseases like cancer. This is advantageous because it improves the accuracy of early detection of the condition. Enlitic, a medical imaging startup based in the United States, uses deep learning for tumor detection; its algorithms have been designed to detect tumors in human lungs using a Computed Tomography (CT) scan.
AI is currently being used in the data mining of medical records. IBM Watson Health is helping healthcare organizations apply cognitive technology to unlock vast amounts of health data to power diagnosis.
Chatbots powered by artificial intelligence are being used as health assistants and personal trainers. Scheduling doctor appointments, providing medication reminders, and identifying the condition based on symptoms are just a few of the applications of chatbots in healthcare. Startups like Babylon Health and Your MD are well-known AI-powered healthcare assistant applications, which help physicians, patients, and caregivers in the above functionalities.
Many technology companies currently develop AI-powered surgical robots, utilizing machine learning applications such as Google DeepMind, IBM Watson, and others. The use of AI-enabled robots can result in less damage, increased precision, and faster recovery.
Drug discovery is another area where AI is becoming more widely used. Helix, an artificial intelligence startup, uses machine learning to respond to verbal questions and requests, allowing researchers to increase efficiency, improve lab safety, stay current on relevant research topics, and manage inventory.
Because of AI, drug design and compound selection can now be automated. Peptone predicts protein characteristics and features using AI and Keras and TensorFlow integration, allowing researchers to simplify protein design, detect production and characterization issues, and discover novel protein features.
GNS Healthcare, which uses AI to transform diverse biomedical and healthcare data into computer models, is one example of how AI is widely used in clinical trials. The models allow doctors to predict how patients will respond to treatments based on their individual characteristics, facilitating personalized medicine and treatment on a large scale.
Current AI use cases in healthcare
1. Diseases prediction using data mining and AI
Data has become an important fuel for driving innovation in the age of ubiquitous technology. Data mining is a technique for extracting information and patterns from large databases. Patient records are collected in large quantities in the healthcare industry. The healthcare industry can address many diseases before they occur with proper data analysis and machine learning tools.
Data mining is currently being used in the healthcare industry to develop early detection systems based on clinical and diagnosis data. AI is being used by tech giants like Google and IBM to unearth patient data, both structured and unstructured. The information is gleaned from medical records or by analyzing physician-patient interactions (voice and non-voice-based interactions).
2. Medical imaging and diagnostics
AI has made significant progress in medical imaging and diagnostics in recent years, allowing medical researchers and doctors to deliver flawless clinical practice. Deep learning is assisting in preventing diagnostic errors and improving test outcomes by paving the way for quantification and standardization. Furthermore, AI improves medical imaging assessment to detect cancer and Diabetic Retinopathy (DR). It also aids in the quantification and visualization of blood flow.
According to a recent poll conducted by European Radiology Experimental, over half of the global healthcare leaders expect AI’s role in monitoring and diagnosis to grow significantly. Arterys, a Deep Learning medical imaging technology company, recently announced a partnership with GE Healthcare. Arterys’ quantification and medical imaging technology are combined with GE Healthcare’s Magnetic Resonance (MR) cardiac solutions in this collaboration. It is now possible to conduct cardiac assessments in a fraction of the time it takes to perform traditional cardiac MR scans, thanks to implementing these technologies.
3. Lifestyle management and monitoring
Individuals can now manage their own health and comfort due to increased digitization, and the data generated by digitization will power AI technology in the future. Parents can now keep tabs on their infants’ health, sleeping patterns, and development. Fedo, a startup, recently discovered a way to identify individual risks for lifestyle diseases. They used AI to create a risk stratification algorithm that predicts people’s readiness for seven non-communicable diseases, including Diabetes II and Myocardial Infarction.
4. Nutrition
Currently, there are a plethora of nutrition-related apps available in app stores, each with its own set of features and accuracy. Nutrition apps that incorporate AI can provide personalized recommendations and suggestions based on a person’s preferences and habits.
VITL, a London-based startup, uses artificial intelligence to diagnose patients’ nutritional needs and deficiencies. It also provides users with a personalized nutrition plan and daily vitamin pack in addition to the diagnosis. The startup uses an AI engine called LANA (Live and Adaptive Nutritional Advisor), which uses a wide range of lifestyle and diet data points to map out human nutrition experts’ logic and thought process.
5. Emergency room and surgery
The first surgical robot, named da Vinci Surgery System, approved by the FDA for general laparoscopic surgery, was developed 15 years ago. Since then, many other surgical robots were introduced, including the current generation of robots integrating AI in surgery.
The next generation of surgical robots is being powered by machine learning and AI. In the near future, we will witness AI platforms such as DeepMind, IBM Watson, and other advanced AI tools enabling physicians and hospitals to deliver promising surgical interventions.
IBM Watson has advanced medical, cognitive, and NLP capabilities to respond to surgeon’s queries. Further, similar AI platforms aid in monitoring blood in real-time, detect physiological response to pain and provide navigation support in arthroscopy and open surgery.
6. Hospital information system (HIS)
Most hospitals and clinics now use HIS software to manage appointment scheduling, treatment follow-up, and other administrative tasks by integrating with patient EHRs. There is a lot of potential for these systems to be used to provide better health care.
For example, Google’s DeepMind Health team collaborates with NHS hospitals to use a mobile application to track a patient’s condition. The app enables hospitals to detect any deterioration in a patient’s condition quickly and accurately, allowing them to provide treatment as quickly as possible.
Furthermore, AI in healthcare assists clinicians with real-time predictive analytics and solves operational challenges across the hospital. Automated data collection, analysis, reporting, and communication saves time, reduces steps, and eliminates paper-based processes.
7. Drug discovery
The way pharmaceutical companies develop medicines is changing thanks to artificial intelligence. To understand how a drug affects a patient’s tissues/cells, AI searches biological systems. Precision medicine and predictive medicine, for example, are used to predict a patient’s treatment rather than studying a larger group of patients.
A pharmaceutical startup called BERG has developed an AI platform that uses biological data to track how cells change from healthy to malignant. The software uses data from the Human Genome Project from 2003 and over 14 trillion data points from single-cell tissue. As a result of this research, BERG created a new cancer drug that could reverse the process.
8. Virtual Assistants
Virtual assistants/AI assistants are being developed to enhance human-like interactions and save time and resources. Nuance, a company that has created a Medical Virtual Assistant, streamlines clinical workflows for the 500,000 clinicians who use Dragon Medical for clinical documentation every day. It enables people who use specialized medical terminologies to communicate naturally and accurately.
9. Wearables
Wearables such as smartwatches, clothes, and shoes will be popular in the near future due to the upcoming trend of miniaturization in AI applications. Researchers and manufacturers are attempting to capitalize on this trend by making it available for everyday use and clinical applications. In the absence of an AI engine, a product’s data would be of no use to the user. As a result, AI engines are being integrated into the product’s health solutions to capture an individual’s health insights. Users can approach a physician or choose an AI doctor to detect an abnormality using clinical-grade wearable technology.
