Artificial intelligence (AI) has unbelievable potential to revolutionize every aspect of our lives, including healthcare. There is currently a shortage of more than seven million physicians, nurses, and other health workers worldwide and the gap continues to widen.¹ Physicians are stretched, especially in underserved areas, to respond to the expanding needs of the population. AI can help. Healthcare companies need to take appropriate precautions to mitigate the risks that accompany the potential rewards of applying AI in the healthcare field.
3 Key Takeaways
There are three critical intersection points between artificial intelligence (AI) and the healthcare industry worth examining here.
#1 – Managing Digital Data Storage
With the evolution of digital capacity, more and more data is produced and stored in digital space. The amount of digital data is growing at a mind-blowing pace, doubling every two years.² While we are still in the embryonic stages of understanding the real potential of AI in the future and the impact it will have on our lives, we use it today through Google searches, Apple’s Siri, Amazon’s suggestions for products, Amazon’s Echo, smart wearable devices, and other similar technologies; however, the evolution of these opportunities is exponential and will be profound in healthcare.
#2 – Enhancing Patient Treatment Plans
Applying AI in healthcare and medicine will enhance patient treatment plans and provide physicians with more information to assist them in making better decisions. New AI technologies can identify subtle signs of disease in medical images faster and more accurately than humans.
For example, Enlitic’s Picture Archiving and Communications (PAC) division developed a deep learning algorithm that detects signs of disease in medical imaging modalities, such as MRI, CT scans, ultrasound, and x-rays. PAC examines the imaging data by comparing it to large data sets of past images. By analyzing ancillary clinical data, doctors are able to achieve 50 to 70 percent more accurate results compared to human radiologists at 50,000 times the speed.³
With the number of companies investing in AI in healthcare, the future is bright and the hope for improved delivery of care is strong. While IBM’s Watson is on the forefront in cognitive computing for healthcare, a number of other companies including: Dell, Hewlett-Packard, Apple and many more are testing new ideas and applications.
#3 – Creating Drugs and Managing Medication
AI is on the cutting edge of revolutionizing healthcare, not only in the design of patient treatment plans, but also in medication management and drug creation. This is only the beginning, with other applications taking AI to the next level. Other examples include:
- Mining medical records: Google launched its Google Deepmind Health Project, which mines medical record data to provide better and faster health services. This project, which is in its initial phase, has Google and Moorfields Eye Hospital NHS Foundation Trust collaborating to improve eye treatment.
- Designing treatment plans: IBM’s Watson launched a special program for oncologists that provides clinicians with evidence-based treatment options. Watson for Oncology can analyze the meaning and context of structured and unstructured data in clinical notes that may be critical in selecting a treatment plan
- Assisting repetitive jobs: IBM launched an algorithm called Medical Sieve. It assists physicians in clinical decision-making in radiology and cardiology. The “cognitive health assistant” can analyze radiology images to detect problems quickly and reliably.
- Online consultations: Babylon created a new application that provides medical AI consultation based on a person’s medical history and common medical knowledge. The users of this British app report their symptoms and the app checks them against a database of diseases using speech recognition. The app also reminds patients to take their medication and follows up to see how they are feeling.
- Health assistance and medical management: Medical start-up, Sense.ly, created the first medical virtual nurse called Molly. It has a smiling face with a pleasant voice and uses machine learning to support patients with chronic conditions between doctor visits, providing customized monitoring and follow up care.
- Precision medicine: AI will have a significant impact on genetics and genomics. Deep Genomics is focused on identifying patterns in large data sets of genetic information and medical records, trying to identify mutations and links to diseases. They are inventing a new generation of computational technologies that can tell physicians what will happen within a cell when DNA is altered by genetic variation, whether natural or therapeutic.
- Drug creation: Developing pharmaceuticals through clinical trials is costly and takes significant time. Speeding up this process would be more cost-effective and allow for innovation to impact everyday medicine. Atomwise uses supercomputers to unearth therapies from a database of molecular structures. For example, it launched a virtual search in 2015 for existing medicines that could be redesigned to treat the Ebola virus. They found two drugs that the company’s AI technology indicated may reduce the Ebola infectivity. The analysis, which typically would take months or years, required less than one day.
Conclusion
As we look forward to the promise of AI to improve the delivery and accuracy of care, we should not lose sight of the considerable risk inherent to the adoption of these technologies. The challenge of governing AI is less about managing completely new types of risks and more about understanding existing risks, which manifest in unfamiliar ways and are more difficult to identify due the inherent complexity and speed of AI solutions. Healthcare organizations do not require completely new risk management processes for dealing with AI, but they will need to enhance existing procedures to account for the potential risks inherent with the use of AI in the clinical setting and fill the necessary gaps.
The clinical setting and patient data necessitate the highest level of accuracy, reliability, security, and privacy, as well as provider and patient education. It is yet to be seen whether and to what extent developing AI technologies will be able to meet or exceed that standard. Guidelines, including the following, may help mitigate the risks that accompany the potential rewards of applying AI in the healthcare field.
- Creation of ethical standards that are applicable to, and obligatory for, the whole healthcare sector.
- The gradual development of AI to allow time for mapping of potential downsides.
- For medical professionals: acquirement of basic knowledge about how AI works in a medical setting to understand how solutions may help them in their everyday work.
- For patients: getting accustomed to artificial intelligence and discovering its benefits for themselves.
- For companies developing AI solutions: even more communication and dialogue with the general public about the potential advantages and risks of using AI in medicine.
- For decision-makers at healthcare institutions: instituting the necessary steps to measure the success and effectiveness of the system.
- For risk managers at healthcare institutions: evaluation of insurance coverage to identify whether sufficient coverage is available to protect against risks inherent in AI, including an assessment of whether current coverages (e.g., General Liability, Professional Liability/Errors and Omissions and Cyber/Privacy Coverage) need enhancement.
It is important to push companies toward offering affordable AI solutions since it is expected to lead to better patient outcomes and is a way to bring the promise of science fiction into reality, provided that related risks are evaluated and addressed. With the risks kept in check and the rewards ever on the horizon, AI could well become the technological breakthrough of the modern age – the stethoscope of the 21st century.
1 Dickson, B. “How Artificial Intelligence is Revolutionizing Healthcare.” Accessed on 10/6/18, source
2 The Medical Futurist. “Artificial Intelligence Will Redesign Healthcare.” Accessed on 10/6/18, source
3 Hamid, S., Dr. “The Opportunities and Risks of Artificial Intelligence in Medicine and Healthcare.” Accessed on 10/6/18, source
