3 Key Benefits and Challenges of AI in Digital Health Space

The healthcare industry has a dire need for automation to provide quality healthcare services to a large group of people. Artificial intelligence in healthcare is promising as it can learn, comprehend, interpret, and predict. It can be beneficial to care providers in analyzing, diagnosing, and helping patients get better treatment. Let us look at some of the major benefits of AI in healthcare:

1.      Clinical Decision Making

AI supports data-driven clinical decision-making accompanied by real-time data. AI can give real-time data immediately to help in accurate decision-making. Machine learning algorithms can find out potential risks, give status alerts in case of critical or emergency patients, avert diagnostic errors, and foster doctor-patient relations with adequate outcomes.

2.                  Strengthening healthcare process

AI permits smoother process and task automation by enabling innovations in operations and task management. AI has made healthcare processes easier, which include scheduling appointments, updating patient details, follow-up visits, surgeries, and tracking medical history. It also involves reviewing and resolving faulty insurance claims which are made non-chaotic with the use of AI.

3. Support Medical Research

AI is one of the most important digital health solutions that have the potential to support effective medical research without wasting a major amount of money, time and effort required for medical research. This is needed for better disease prevention and the discovery of new drugs. AI can aid in medical discoveries that can be life-changing by providing real-time information to share data across multiple networks.

What are the challenges of using AI in Healthcare?

Even though the implementation of AI has multiple benefits and uses in healthcare, it still comes with numerous hurdles. Here are some of its examples:

1.      Absence of standard medical data

Various healthcare datasets differ in nature and complexity. Apart from this, data can also be significantly different from one organization to another. The insufficiency of standard and consolidated data within the industry results in fewer training scenarios for AI to comprehend and grasp, which leaves them unsuitable for handling critical tasks.

2.      Performance metrics 

Performance metrics measuring the performance of AI applications do not promise efficiency when they are being applied in real time. The performance metrics used for testing algorithms can be limited and vastly different when clinically applied to various patients.

3.      Requires supervision 

It requires supervision as it is not applicable to replicating human capabilities. Even though AI robots can manage complex tasks, they still need human input to perfect such tasks. They are machines that use logic but do not understand or observe behavioral displays, which is necessary for healthcare to prevent misdiagnosis and ensure adequate patient outcomes.

Healthcare is one such industry in which millions of lives depend on the availability of data and its accuracy. Generative AI is genuinely revolutionizing life sciences and healthcare. It plays a pivotal role in changing patient care and research, making AI solutions for business. Due to its advancement, it has the capability to enhance diagnostics, drug discovery processes and treatment plans.

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