Many benefits can be derived from AI implementation in Healthcare, including enhanced patient care and reduced costs. Moreover, AI can process complex images and health records to make accurate diagnoses in real-time. Recent studies have shown that AI can correctly diagnose diseases 87% of the time when reviewing medical images. Combining the skills of AI with clinicians can significantly reduce the rate of misdiagnoses and increase physician productivity.
AI's full potential in Healthcare
AI has the potential to change Healthcare, enabling patient self-service, improving diagnosis and drug discovery, and reducing costs and errors. But before this technology can be widely adopted, it needs to demonstrate its utility and reliability, and high-quality data must back it. The healthcare industry is struggling with a data problem. Too much-unstructured data isn't being leveraged to build a complete picture of a patient's health. That lack of information leads to poor outcomes and unnecessary costs.
Healthcare providers must invest in AI to make the technology more effective. However, unregulated AI use may put patients and communities at risk and compromise cybersecurity. In addition, AI systems may not perform well in low-income settings. The WHO report provides important guidelines for the development of AI systems in the healthcare industry.
While the scope of AI's potential in Healthcare is still unclear, some applications have already been developed. For example, artificial intelligence can improve the performance of medical staff by identifying patterns in vast datasets. This technology can also help patients understand their condition better.
Impact on patient care
With healthcare delivery becoming increasingly digital, there are increasing public concerns about the use of healthcare data. Therefore, it is critical for healthcare organizations to develop robust, compliant, and cost-efficient data-sharing policies. As AI applications increase in complexity, healthcare organizations must ensure that their data-sharing policies address ethical and clinical concerns. Healthcare organizations must also engage clinical staff in product leadership and development. These teams will have to determine how AI-based decision-support systems can contribute to wider clinical protocols. Meanwhile, designers specializing in human-machine interactions will be needed to develop new workflows and interfaces for AI-powered medical applications.
As healthcare organizations embrace AI solutions, they are looking to improve operations, reduce costs, and improve patient care. These solutions may include predictive analytics and patient-centric data analytics. They may also support a shift from hospital-based care to home-based care and increase patient ownership of their health. Ultimately, these new technologies can help improve patient care, but they require intensive engagement from health systems, providers, and professional bodies.
Cost of implementation
The cost of AI implementation in Healthcare can vary wildly depending on the specific application. For example, you could implement a computer vision system that can spot cancer tumours on CT scans. These systems are much more complicated and require a different set of requirements. However, their costs could be worth it if the benefits outweigh the costs.
AI technologies are expected to cut healthcare costs. In addition to reducing manual labour, these technologies will eliminate inefficiencies in care delivery, such as overtreatment and failures. According to the Journal of the American Medical Association, nearly half of all healthcare expenditures in the U.S. are spent on managing and regulating medical services.
Aside from cutting costs, AI also can make early disease diagnoses more accurate. For example, a computer could help identify breast cancer in its early stages. Using AI in this way could prevent the high rate of false diagnoses. According to the American Cancer Society, up to a quarter of mammograms yield incorrect results. This means that one in every two healthy women is diagnosed with breast cancer when they don't need it.