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As one of the fastest evolving technologies, Artificial Intelligence has been proven to be transformative in the field of healthcare by improving patient outcomes and reducing costs. There are patients, physicians, medical staff, and care-givers that have already benefited from one or more of the many applications of Artificial Intelligence in Healthcare.
Chatbots and AI Therapy
The quality of care provided to mental health patients has risen dramatically over the past decades. However, the high cost of therapy is a barrier to patients who require treatment. That, coupled with the appeal of round-the-clock availability has given rise to AI-based mental health bots. Sensely has developed an AI system with a human avatar and even added empathy to its interactions with patients. Another includes a “faith-based” chatbot called “Sister Hope.” While the chatbot apps are showing real promise for treating conditions like depression and eating disorders, more complicated mental health issues and emotional analysis are still too complex for the current capabilities of AI to analyze accurately.
Chatbots are also being utilized more frequently for day-to-day interactions with patients who need to book appointments, check insurance coverage or access drug information. , chatbots can also help ensure recently discharged patients are following treatment plans. Bots also assist physicians with diagnosis by asking patients to answer a few questions about their symptoms before being prescribed a course of action. Doctors can focus on more complicated cases and patients can access more services from home. One research study predicts the success of chatbot interactions (with no human intervention) will increase to 75% in 2022, compared to the 12% in 2017.
Chatbots can also be used to help solve the shortage of doctors in several regions, including Africa, the Middle East and Southeast Asia.
The level of comfort provided by prosthetics has risen over the years, with newly developed materials such as stronger and lighter alloys, and carbon. But the functionality and quality of life provided by current prosthetics could definitely be improved. This is where AI comes in. Current studies are focused on creating functioning prosthetics through the use of AI. One notable example is from researchers at Newcastle University who created a prosthetic fitted with a miniature camera that can capture images of objects. The captured image is then analyzed by AI to determine the action that needs to be performed. For example, if there is a ball in front of a prosthetic leg, the AI will move the leg to kick it.
Pharmaceutical Research and Development
The California Biomedical Research Association states that it takes close to 12 years and $359 million to develop a new prescription drug, which often is a barrier to the creation and timely release of new life-saving treatments. AI applications are increasingly used to create tailored medical treatments for patients and reduce the time required to get a new treatment approved and available in the market. One start-up, Verge Genomics, uses AI to monitor the impact of drug treatments on brain cells beginning with the preclinical phase, making it possible for manufacturers to assess the effectiveness of a drug early on and reduces the number of failed treatments advanced to the next stage of development.
Apple’s launch of ResearchKit and CareKit in 2015 created countless possibilities for the use of AI in clinical studies, when it comes to recruiting and monitoring patients. The frameworks allow researchers to create apps to monitor the participants’ daily lives. With one intuitive interface, over 500 doctors and researchers used these tools for clinical studies that involved 3 million participants. The availability of these tools led to an increasing number of health and wellbeing apps being created, ranging from using the iPhone’s camera for Autism recognition to Parkinson’s disease studies.
With data mining and big data analysis becoming increasingly important in identifying potential new medicines, creating better drug trials and identifying best-suited candidates for clinical trials, the healthcare industry is more open to and is moving towards an ever-increasing use of AI in the process of drug development.
Reducing Human Error
Errors made in hospitals are one of the leading causes of death in the United States, with over 400,000 people dying. Furthermore, Accenture research suggests that industry-wide savings of $1.6 billion could be achieved if dosage errors were reduced and patient information was accurately transferred from one care giver to another. AI is not only used to calculate and verify the correct dosage of medicine but also to ensure a smooth and complete transfer of patient data through image recognition and hospital records standardization. Using AI systems reduces costs and saves lives.
SRI International began investigating the use of robots for surgery in the 1980s. Since then, the field has steadily grown to now include the use of robots in a range of surgeries and procedures. Microsurgical procedures, in particular, require precision and accuracy. Robots powered by AI reduce inefficiencies in procedures, effectively reducing the risk to the patient and post-op recovery time. Training costs are also reduced, further increasing the appeal of investing in AI technology. Recently, Zimmer Biomet won FDA approval for the ROSA Knee system it developed using data analysis and 3D imaging to assist orthopedic surgeons in knee replacement operations.
There are numerous devices that help people track their fitness levels and other metrics such as blood pressure and blood glucose levels. With these and other gadgets, such as baby temperature monitors and heart monitors, a tremendous amount of data is being collected and used to build medical profiles that assist doctors in monitoring the wellbeing and recovery of their patients.
Medical image diagnosis was one of the earliest and largest uses of AI in healthcare. FDA-approved algorithms used to detect cancer in imaging have been around since 1998. X-rays, MRIs, CT scans and ultrasounds are all examples of medical imaging that use AI to analyze the results.
Pathology also utilizes machine learning and deep learning to create AI that assists in the analysis of the high volume of results and images often generated from various tests. AI in pathology can replace the need for physical tissue sampling and make the available radiology tools more accurate and detailed.
AI is also being used in the review and translation process of mammograms. Current technology can complete this process 30 times faster than humans and has achieved an amazing 99% accuracy.
Growth Predictions and Investment Trends
The next decade is likely to see a shift to solutions for evidence and outcome-based healthcare, with an increasing clinical use of AI. Over the past few years the level of investment into the market has risen dramatically, as investors realized the enormous potential AI has to improve patient outcomes and reduce costs within the healthcare industry. The global healthcare AI market size in 2018 was $1.4 billion and continues to grow, already reaching the $2 billion mark in Q1 this year, and is predicted to be $6.6 billion in 2021 (according to BGV). While estimates of market value in 2026 vary largely, from $17.8 billion (Zion Market Research) to $36.1 billion (Markets and Markets), it is clear that the market will continue to grow. The fact that Federal health funding is set to increase over the next 10 years further adds to the positive growth outlook.