Over the years, the role of decision-making has mostly relied on physicians. Today, as technology advances, Artificial Intelligence (AI) has become diversified in numerous applications in healthcare. From health services management, predictive medicine, clinical decision-making, to patient data and diagnostics, the use of AI in healthcare is expanding. While AI in healthcare shows the ability to output human-like performance and has proven to be helpful in the field, its adoption is still taking baby steps as many questions about its trustworthiness remain. Decision-making is critical in healthcare, as one small mistake may affect a person’s livelihood forever. This is where Explainable AI in healthcare matter.
The Importance of Explainable AI in Healthcare
The benefits of AI in healthcare have been widely discussed and exposed to the world over and over again. AI involves in-depth data analysis to provide healthcare professionals with a better understanding of the condition and needs of their patients, enabling them to make effective decisions for the treatment of a disease or health problem. However, while AI most of the time is able to provide the best results, it also has a history of providing incorrect outcomes. For example, a deep learning model that uses irrelevant input, such as the scanner’s position, while screening X-rays for pneumonia. Therefore, understanding how an AI model achieves a decision or result is crucial, hence the need for Explainable AI.
Explainable AI provides transparency in both diagnosis and treatment solutions. When it comes to our health, it’s crucial to identify the root cause of the problem accurately. The right conclusion from a healthcare professional can effectively treat the issue, while the wrong one can worsen the condition. A clear understanding of how an AI model arrives at a solution, combined with the expertise of a healthcare professional, not only improves the effectiveness of treatment but also increases the patient’s confidence in the suggested solution. By providing transparency and comprehensibility in AI’s decision-making, Explainable AI helps build trust in AI technology in healthcare and ensures that the patient is receiving the best possible care.
The reliability of an AI model is critical in decision-making, and the implementation of Explainable AI in healthcare can improve the model’s reliability. By evaluating how an AI model arrives at a solution and detecting errors and miscalculations when generating an outcome, healthcare professionals can correct and improve the AI model.
When an AI model is transparent and highly reliable, healthcare professionals can enhance their accuracy and capability to provide excellent services to their patients. With a highly reliable AI model and a better understanding of the recommended diagnostic plan by the AI model, the rate of successful treatment is significantly higher. By using Explainable AI, healthcare professionals can improve their decision-making process, resulting in a more accurate diagnosis and effective treatment plan for patients. The integration of Explainable AI in healthcare leads to a more efficient and precise healthcare system, with better patient outcomes.
All in all, Explainable AI is essential in healthcare as it provides significant advantages that result in better outcomes. By promoting transparency and enabling healthcare professionals to better understand how decisions are made, Explainable AI builds trust in AI models. It also allows for the improvement of AI models, which increases accuracy in diagnosis and treatment, further enhancing patient care. The implementation of Explainable AI in the healthcare industry is crucial to achieving better results in healthcare practice and ultimately, improved patient outcomes.
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