Artificial intelligence has revolutionized most if not all sectors and the healthcare industry has not been left behind. Powered by machine learning algorithms to train computer systems to think, act, and make decisions like humans, AI in the healthcare industry is being applied to transform the patient experience, clinical practice, diagnosis, treatment, resource management, and other processes. This is by analyzing, interpreting, and drawing useful insight from the vast amount of data generated in the industry. The adoption of AI drove the healthcare spend on AI to the projected growth of 48% annually between the years 2017 and 2024 with robot-assisted surgery and virtual nursing assistants at the top of the spend. With this also came the demand for healthcare professionals with AI training.
One main reason why AI is being adopted in healthcare is that it makes it possible to analyze the connection between treatment/disease prevention and patient outcomes. AI application in the healthcare industry falls into three broad categories. These include
- Patient-related AI
- Clinician-related AI
- Administrative and operation-related AI
Benefits of AI in healthcare
Adopting AI in healthcare comes with several benefits.
- Automation of medical processes with the help of online medicine delivery apps helps to deliver improved and cost-effective healthcare services.
- Accurate analysis of patient data for appropriate patient care and improved doctor-patient time ratio.
- Operations and administration cost savings. It has been established that at least 30% of healthcare costs are pegged on such repetitive administration tasks as bill follow-up, appointment follow-up, and records maintenance. Entering AI and automating such tasks makes it possible to ease the workforce and trim down costs while at the same time increasing efficiency.
- Improved patient satisfaction. Patient insights gathered from healthcare data make it possible to improve specific aspects of healthcare delivery such as improving patient-provider encounters, ultimately meeting customer expectations.
- Efficient staffing and resource management. Prudent utilization of resources includes optimal staffing, utilization of scarce resources, and maximization of revenue approaches.
Applications of AI in healthcare
The growth and access to healthcare data along with diagnostic approaches give AI great potential when it comes to developing solutions required to drive healthcare to a level that matches current technological advancements. With this growth comes the successful application of AI in various aspects of the healthcare industry such as:
Digital consultation
Given the access to a patient’s medical history, it is now possible through predictive analytics to not only conduct medical consultations digitally but also get recommendations as to what actions to take thereafter as in the UK’s Babylon consultation application.
Early disease detection
Life-threatening diseases like cancer can be managed better and longer when detected in their early stages. AI is useful in screening body cells and carrying out diagnostic tests to discover cancer cells at their earliest stages. This forms the baseline for developing new drugs and treatment protocols to treat or manage the disease.
New drug discovery and development
Overly long research periods and high development costs are two top limitations that the drug development industry is facing. However, with AI, the biopharmaceutical industry is now witnessing faster, more accurate drug development and clinical trials.
Patient screening
Atomwise AtomNet neural network has been designed to screen up to 20 million genetic compounds for fast accurate clinical trials for life-threatening diseases like Ebola.
Disease diagnosis
It is not enough to diagnose diseases, it is equally important to make an accurate diagnosis. The PathAI ML system, for instance, was developed to help pathologists make accurate diagnoses. The more individualized patient intervention is, the lower the error rate and the more accurate the diagnosis will be.
Disease prevention
Particularly in outbreak situations where there is high population mobility, the risk of an outbreak disease spreading fast is high. This requires a holistic technique that looks into electronic health records for clinical diagnosis from a wide range of institutions including community hospitals, secondary, and tertiary hospitals to develop a reporting and early disease risk warning approach.
Primary care
AI health applications provide a platform through which patients gain access to relevant information for self-care or place-based care to meet the healthcare needs of patients ultimately reducing patient visits.
Drug interactions
AI has the potential to identify possible drug-to-drug interactions and give warnings on adverse drug effects particularly for patients who take multiple medications as they have a higher risk of drug interactions. Further, algorithms are currently being developed that can give personalized interaction warnings.
Precision medicine
Towards precision medicine, treatment has shifted away from the more generalized patient treatment to personalized intervention programs in therapies, medication, and other treatment options. Precision medicine has been enabled by robust data analysis and prediction software in machine learning such as Google’s recently released DeepVariant application.
Telemedicine
Both AI and diabetes telemedicine have common benefits, convenience, flexibility, and versatility. AI has endless possibilities, through accurately identifying patterns and analyzing past patient records, of developing telemedicine diagnosis and treatment protocols. Also, developing best practices for decision making during medical procedures. The latter is an area that has posed the greatest challenge and AI has the potential to overcome this challenge for better patient outcomes.
Radiology assistant
The development of AI-powered assistants for interpreting imaging scans and other unstructured medical data are notable breakthroughs when it comes to radiology diagnosis based on image scans. A good example is San Francisco’s Enilitic deep learning medical platform for analyzing unstructured medical data.
Overall service delivery
Customer experience in the healthcare sector is pegged on seamless patient journeys through hospital systems and processes. A study conducted in 2016, found that lack of customer service, cumbersome paperwork, and poor reception was among the top reasons for poor patient experience. AI technology aimed at streamlining service delivery for improved patient experience would include automation of the repetitive processes which frees professionals to work on developing solutions for the issues that plague institutions.
AI Robot-assisted surgery
This is by far one of the greatest AI breakthroughs in the healthcare industry. Robotics is today being applied to minor and major surgeries successfully. Procedures that were deemed too complex and high-risk for doctors can now be performed with precision and control by doctors with the help of robots. Another area that is being explored is the combined use of virtual reality and robotics to provide 3D magnification during minimally invasive surgeries.
Conclusion
The widespread adoption of AI in healthcare is no longer new. The future of healthcare lies in the capabilities that machine learning and neural networks bring into healthcare systems and processes. However, for AI to have a lasting positive impact in the healthcare industry, continuous access to quality appropriate data is vital. ML in healthcare is concerned with analyzing structured data.
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