INGENIEUR
INGENIEUR
can process vast amounts of data much faster than human beings , leading to faster and more accurate diagnoses , treatments , and clinical trial results . AI has been proven to be especially effective with a large volume of radiology data to improve the quality-of-care services with medical imaging ( Pesapane et al ., 2019 ). If AIbased software can improve the accuracy of patient diagnoses , then it will help not only patients but also the work of medical staff . For example , the frequency analysis of mitosis in cancer cells through images or a microscope is a straightforward process but takes a great deal of time . AI software can perform this task with greater accuracy and speed , thus helping medical staff with their professional work while eliminating some of the drudgery . AI-supported medical software can get smarter by learning from the increased volume of accumulated data and new medical research . In fact , the increased accuracy of AI-supported medical software is approaching or exceeding the accuracy of medical experts in diagnosing diseases . Continuous research in the use of AI systems will augment the work of medical staff as they can alert them to missed areas or help minimise medical errors during patient treatment .
The use of AI in healthcare can also result in cost savings . By automating routine tasks and providing decision support , AI can help to reduce the cost of healthcare delivery . According to Lee ( 2019 ), an ideal healthcare service should encompass various elements , including data-driven disease prevention , accurate diagnosis , advanced treatment technologies , patient-centred personalised care , and compassionate medical staff . The integration of AI into healthcare can contribute significantly to achieving this ideal by ensuring both high-quality care and substantial cost savings . A report by ABI Research ( 2018 ), a marketing research consulting firm , suggests that leveraging AI in the healthcare industry could result in potential savings of up to USD52 billion in the US by 2021 . Major hospitals in the US have already adopted AI-based programmes for disease prevention , and the number of AI-supported devices aimed at training patients to prevent chronic conditions like diabetes and high blood pressure was projected to increase from 53,000 in 2017 to over 3.1 million by 2021 , demonstrating an annual growth rate of 176 %. Consequently , AI applications in healthcare have the potential to significantly reduce medical costs , benefiting both individual patients and society . These cost savings can be redirected to disease prevention initiatives , leading to an improved quality of life for citizens . Furthermore , AI systems can bring about operational innovations within healthcare organisations , enhancing the value chain . AI-enabled chatbots , nursing robots , and automated systems excel at routine operational tasks such as maintenance management , accounting , and information retrieval , thereby improving efficiency in healthcare processes . As per the reports of Acumen Research , the global market of AI in the healthcare industry is expected to rise to USD8 billion by the year 2026 .
Challenges in the Application of AI in Healthcare
One of the main challenges in the application of AI in healthcare is data quality and availability . Healthcare data is often fragmented , heterogeneous and of varying quality , making it difficult to develop accurate and reliable AI algorithms . For example , electronic health records can contain errors and inconsistencies , and genomics data can be complex and difficult to interpret . To overcome these challenges , it is important to have high-quality , annotated datasets that can be used to train AI algorithms .
Another challenge in the application of AI in healthcare is the lack of transparency and interpretability of AI algorithms . AI algorithms are often considered to be black boxes , making it difficult to understand how they reach their decisions . This can be a barrier to their adoption and implementation in the healthcare sector , as clinicians and patients may not trust the decisions made by AI algorithms . To overcome this challenge , it is important to develop AI algorithms that are transparent and interpretable , and to provide clinicians and patients with clear explanations of how AI algorithms make their decisions . This is supported by the development of explainable
56 VOL 95 JULY-SEPTEMBER 2023