What Are The Top 5 Benefits of AI in Healthcare?

8 minute read | 10 Apr 2024

By Boomi

AI is making its presence felt across all industries, including healthcare. Finding a hospital or other healthcare institution that doesn’t use this technology in some capacity might be tough. But there’s still a perception that integrating AI in Healthcare environments is a complex challenge, primarily due to compliance and regulations.

It doesn’t help that the role of technology in healthcare has often been a point of contention, and many people are particularly apprehensive about the introduction of AI. While artificial intelligence offers many advantages, it’s essential to understand all the pros and cons before diving in.

What Are the Top 5 Benefits of AI in Healthcare?

Due to its numerous advantages, AI is fundamentally transforming many aspects of healthcare. Here are the key benefits its adoption offers patients and professionals:

1. Personalized Treatment

AI allows for more personalized treatment. Medical professionals can analyze patient health records and other relevant data with predictive analytics. AI algorithms can process large volumes of data to identify patterns often overlooked by the human eye.

This capability delivers critical insights into patient health. For example, an algorithm might find that a patient’s condition responds well to a specific combination of medications and alert their healthcare provider, who can tailor their treatment accordingly before they notice any difference. This type of value-based care goes a long way towards ensuring positive patient outcomes.

2. Enhanced Diagnostics and Detection

Along with aiding in personalized treatment, AI algorithms can help with the early detection of diseases. It can quickly analyze complex medical data to determine what health conditions patients are at risk of developing.

This technology is especially important in radiology and pathology, where it detects anomalies in X-rays, MRIs, and tissue samples. Its ability to learn from vast datasets increases accuracy over time.

3. Operational Efficiency

AI also helps with more mundane things. Healthcare providers can use it to streamline their workflows and perform administrative tasks, primarily through automation. AI automated tools can schedule appointments, manage patient records, and process insurance claims – all easing the burden on human staff.

The less time healthcare workers need to spend on routine tasks, the sooner they can focus on the patient in front of them.

4. Telemedicine Capabilities

One of the positives of the recent pandemic was the rising use of telemedicine. These digital services allow for expanded access to healthcare and can reach patients who struggle to attend in-person appointments.

AI plays a vital role in enhancing telemedicine capabilities. It allows for the automated monitoring of remote patients, and virtual assistants can offer round-the-clock medical support. It also augments the functionality of wearable devices to aid in managing chronic conditions, providing real-time feedback and continuous monitoring.

An integration platform as a service (iPaaS) is an easy-to-implement, no-code solution that connects applications, data, devices, and people. iPaaS can securely consolidate and integrate all patient data, including medical records and up-to-the-second information from wearable devices, to enhance the accuracy and effectiveness of telemedicine.

5. Improved Patient Outcomes

By enabling early disease detection, automating routine tasks, and expanding access to telemedicine, AI helps create a 360-degree view of patients, allowing providers to give their patients the best care possible.

What Are the Potential Risks of AI in Healthcare?

While AI offers many advantages to the healthcare field, there are legitimate concerns about the technology.

Racial and Gender Bias

One of the most high-profile issues with AI is its propensity for bias, especially regarding race and gender. While researchers and organizations strive to ensure the quality of the datasets used in AI training, bias can still filter through.

For example, if an AI system is trained predominantly on data from one specific racial group, it may be less accurate for others. This can result in dangerous misdiagnoses or inappropriate recommendations for treatment.

Another factor to consider is AI’s tendency towards gender bias. Historically, medical research has focused on male subjects, which means there’s a significant knowledge gap regarding female-specific health issues. The uncritical use of AI can perpetuate this lack of understanding, leading to poorer healthcare outcomes for women.

Data Privacy

Patients should be fully informed on how AI applications use their data. But they aren’t always given the opportunity to provide consent. Privacy around data is particularly important in healthcare, where sensitive patient data and medical history are often vulnerable to unauthorized access. Healthcare is second only to finance regarding data theft. Interconnected AI systems that store large datasets are significant targets for cyber attacks.

An integration platform as a service (iPaaS) can help healthcare organizations take advantage of all the benefits of AI while ensuring robust GxP compliance and security.

Ethical Considerations

Should AI be used instead of a doctor to diagnose patients and predict health outcomes? Medical ethicists are divided on this question. Some feel that human professionals are better suited to decide patient care than a computer system, while others believe introducing AI physicians is inevitable.

The insurance industry commonly uses AI, but there is mounting concern that these systems inadvertently perpetuate bias regarding claims approvals. What’s more, some AI models may lack transparency in the decision-making about approvals or denials.

Model Drift

Model drifting refers to the phenomenon in which the predictions of some AI models become less accurate over time due to changes in the underlying data or environment. In healthcare, where decisions are often a matter of life or death, addressing model drift is vitally important.

While patient outcomes can be adversely affected when machine learning algorithms degrade, the risk can be mitigated by regularly updating the model as new data becomes available. Its performance should be continually evaluated and adjusted to reflect current knowledge and practices.

The Future of AI in Healthcare

The AI revolution in medicine has only just started and has the potential to reshape healthcare as we know it radically. Though the technology has room for improvement, it will play a significant part in everything from epidemic prediction to robotic surgery.

By understanding and addressing the risks associated with AI, healthcare professionals can play a critical role in helping perfect these tools, improving patient outcomes everywhere.

The low-code Boomi automation platform is also helping to modernize healthcare architectures. Boomi makes it easy to leverage AI for a 360-degree view of the patient, along with real-time data, insights into care plans, and patient outcomes.

Boomi is HIPAA/HITECH certified, supports GxP environments, and is HL7v2- and FHIR-compliant. The Boomi platform bridges the gap between legacy and cloud systems with pre-built application connectors and solutions.

Explore our ebook on some of the current tech trends shaping technology integration to see how Boomi is helping pave the way to a better future.