Artificial Intelligence (AI) has been dominating every technological conversation due to its potential and capabilities. Within the healthcare industry, AI has been key in providing medical practitioners and healthcare institutions with a variety of tools that decrease pressure on their workloads while streamlining and redefining their workflows.

Most tasks managed by AI applications in the healthcare industry tend to be repetitive, time-consuming and tedious. The technical, complex tasks are managed by algorithms developed specifically to provide real-time clinical decision support. But, regardless of the many advantages AI provides, it’s important to recognize that no technology comes without its set of limitations and AI is no exception. As a result, to achieve the desired results, businesses need to strategically implement AI and manage it intelligently.

Let’s look at a few advantages of using AI in healthcare:

Clinical decision making
There are many advantages to using AI in healthcare. It has helped change clinical decision making due to its ability to provide decision-makers with key, real-time data that can be utilized to formulate an accurate diagnosis, plan treatments and manage the population’s health. AI is also capable of leveraging insights in genomic, bio-market and phenotype datasets as well as providing specialized solutions in fields like radiology, pathology, identification and ophthalmology.

Streamlining processes
AI provides solutions that are capable enough to detect possible markers on radiology images. There are also AI-based applications that are capable of translating clinical notes, streamlining appointments, tracking patient notes and care recommendations, thereby reducing the burden on physician admins. Essentially, the benefits of AI in healthcare are almost as many as the applications it is invented and applied for.

Information sharing
Building on physician support, the advantages of AI in healthcare also include information sharing and precision medicine. AI can be utilized to track specific patient data more accurately which allows healthcare professionals to more plan for better care for the patient as well as improve doctor-to-patient time ratios.

In terms of precision medicine, AI can be leveraged to increase the precision of outcomes as well as increasing accuracy to detect patterns within patient data that suggest the probability of contracting a specific disease or illness in the future. Such insights are crucial for medical professionals as it can allow them to be proactive in treating the patients as well as fully streamlining patient care and decrease health risks by addressing any probable future problems at the root. The ability to read and analyze huge volumes of data makes AI a wonderful asset in unlocking the full potential of precision medicine.

Limitations of AI in healthcare
AI is still not a perfect technology and it has many more challenges to face ahead. It is also unrealistic to assume that AI will simply swoop in and fix all challenges and overcome insurmountable odds. The advantages to AI in healthcare depend on the strategy that is used while implementing AI as well as recognizing the risks and limitations of the technology itself.

Human Assistance
Ai is an excellent solution when it comes to executing specific commands that it is programmed to handle. One of the main limitations of AI is that it is not yet able to function solely on its own. AI is required to work together with physicians. For example, in radiology, AI systems function as a support system for radiologists and the ultimate decision on patient diagnosis and management rests with them. Basically, when it comes to healthcare, AI functions as a second pair of eyes but it requires human supervision and expertise to ensure that the results are relevant and accurate.

Implementing the right AI platform
Another limitation of AI in healthcare deals with seamless deployment in clinical environments. Even though the technology is not in its infancy anymore, it still needs to be refined and adapted regularly as new lessons are understood and algorithms become more adept and efficient. Medical institutions should invest in AI with caution, strategy and with the right collaborators as not as AI solutions are equal. The danger here is that a healthcare practice implements an AI platform that does not allow for rigorous control or accreditations.