Artificial Intelligence (AI) is fast growing in the healthcare system allowing for several benefits from optimized administrative tasks, automated diagnosis, and advanced personalized health care services. Though AI has added significant value to the healthcare industry, presently, it is nowhere near the popular science fiction scenarios where AI virtually takes over for an actual human physician. But people tend to associate AI with grand schemes and exaggerations, which in reality overshadows the immense potential AI has in assisting physicians.
AI can make diagnoses faster, more accurate, help personalized care, quicken drug discoveries, help with research and derive meaningful insights from the huge volumes of medical data available. Even though AI technology is evolving rapidly, its adoption into healthcare systems needs to be taken care of with careful and well-evaluated steps.
Healthcare systems are highly safety-critical, and hence there are certain risks and challenges to be met before AI can be adopted on a wide scale. Read on to know more about some of the promising benefits and associated risks that AI presents in the healthcare industry.
In this article, we will cover:
Benefits Of AI In Healthcare
In traditional healthcare, much of the treatment and services are relegated to in-hospital care under the physician’s direct supervision. Patients have to wait for their appointments and receive consultations regularly or when they have an acute medical condition. Constant care and supervision outside of the hospital can be difficult and expensive.
With AI, it is possible to provide better after-care and monitor the patient’s vitals continuously. For instance, applications like Google Health can monitor internal injuries like kidney injury about two days in advance, paving the way for better treatment and care. AI algorithms can assess the patient’s status in real-time and increase preventive care and diagnosis efficiency.
AI can also help deliver personalized healthcare by analyzing each patient’s unique biomarkers and genetic makeup and help prescribe the right drug combinations with optimal efficacy.
One of the major advantages AI can bring to the medical community is democratizing medical knowledge and expertise. AI can enhance the performance of medical specialists by giving them quick access to information and better diagnostic analysis with their ability to deal with massive datasets. Likewise, general practitioners can get easy access to specialized data and make informed decisions about treating a patient.
For instance, AI-based image analysis methods have proven to be very helpful in reducing the time it takes to diagnose eye conditions. These programs have made it possible to make accurate eye diagnoses without necessarily consulting a specialist ophthalmologist.
It is estimated that administrative tasks consume around 1/6th of a physician’s working hours. Spending so many hours on administrative tasks can be exhausting and tolling on the physicians leading to lowered job satisfaction. AI systems can help automate most of the administrative drudge work, manage data efficiently and make it accurate and error-free. AI can also help discover data from the piles of electronic medical records much easier and organize unstructured data to derive better insights.
AI can be a huge help to hospital management by helping with optimized staff allocation and resource management. For instance, AI systems can predict which departments would require additional staffing in the coming month and allocate resources accordingly.
While AI does present a promising path to efficacy in medical care, it also comes with quite a few risks. Any bias, inaccurate data, errors in learning models can pose severe threats to patient safety. The potential risks include:
The major reason why people can be skeptical about a fully artificial intelligence medical diagnosis and care is the obvious risk when AI systems go wrong. AI systems are not infallible. They are still evolving and could show up false positives and false-negative results.
AI errors also have a wider range of impact as, unlike a physician’s error, its application will not be limited to a single patient or single case. Hence, it makes sense to use AI to assist technology as people will be less willing to trust an AI system’s capabilities fully.
Impact On Medical Profession
This is a long-term risk of wide-scale implementation of AI where certain professionals could be at risk of losing their jobs. For instance, radiologists could lose their jobs if a fully automated AI system replaces their functions.
Integration And Training AI
Training AI systems mandate the availability of huge amounts of medical data sourced from electronic medical records, insurance records, pharmacy records, patient medical history, and more. A huge variety of structured and unstructured data needs to be consolidated and properly processed for use in AI systems. And much of this data resides in silos split among the various medical institutions, formats, multiple medical systems, and so on. It can be a huge challenge to gather data to be used in training AI.
Data Issues And Security
The results you get from an AI system are highly reliant on the accuracy of the data that you feed. Inaccurate and insufficient data can lead to suboptimal results that add no value or cause serious repercussions if used in actual care delivery. So extreme care must be taken to ensure AI systems used in healthcare have proper data.
Here are some of the data issues that AI has to deal with:
- Digitizing and consolidating huge amounts of relevant medical data
- Data compliance with the local laws and privacy regulations. In many cases, it could be illegal to use patient data without their consent.
- Manual human interventions and bias in feeding data to the AI systems could pose a severe risk to the legitimacy of the AI system’s results.
- Data security must be taken care of to avoid unauthorized access and data tampering.