How Artificial Intelligence is Accelerating Innovation in Healthcare

AI would propose a new support system to assist practical decision-making tools for healthcare providers. In recent years, healthcare institutions have provided a greater leveraging capacity of utilizing automation-enabled technologies to boost workflow effectiveness and reduce costs while promoting patient safety, accuracy, and efficiency [77]. By introducing advanced technologies like NLP, ML, and data analytics, AI can significantly provide real-time, accurate, and up-to-date information for practitioners at the hospital. According to the McKinsey Global Institute, ML and AI in the pharmaceutical sector have the potential to contribute approximately $100 billion annually to the US healthcare system [78]. Researchers claim that these technologies enhance decision-making, maximize creativity, increase the effectiveness of research and clinical trials, and produce new tools that benefit healthcare providers, patients, insurers, and regulators [78].

For instance, training data-driven AIs, such as those used in image processing in radiology, requires many excellent-quality data sets. AI can help doctors make decisions more quickly by analyzing data and providing recommendations in real-time. This can be especially helpful in emergency situations where quick decision-making is critical. While artificial intelligence (AI) is not new, most healthcare organizations are still in the early stages of adopting it. Traditional drug discovery is a time-consuming process, often taking years to bring a new medication to market. AI is revolutionizing this landscape by simulating biological interactions, predicting potential drug candidates, and significantly expediting the research phase.

Privacy of health data

As more vital processes are automated, medical professionals have more time to assess patients and diagnose illness and ailment. AI is accelerating operations to save medical establishments precious productivity hours. Making vital patient data available through mobile devices can engage patients in their treatments. Mobile alerts can inform doctors and nurses of urgent changes in patient statuses and emergencies. Another AI technology with relevance to claims and payment administration is machine learning, which can be used for probabilistic matching of data across different databases. Reliably identifying, analysing and correcting coding issues and incorrect claims saves all stakeholders – health insurers, governments and providers alike – a great deal of time, money and effort.

• Second, Lee and colleagues figured out a way to provide a window into an AI’s decision-making, cracking open the black box.
• AI systems can spot subtle changes in medical data that indicate a disease is just getting started.
• A key to delivering this vision will be an expansion of translational research in the field of healthcare applications of artificial intelligence.
• These give geneticists a thorough and accurate look at genomes, transcriptomes, and epigenomes.

Using chatbots to communicate with patients not only frees doctors from mundane work, but also allows them to answer patients’ questions quickly and clearly, as well as help patients schedule doctor’s visits. It is, however, not an ideal algorithm or system, especially when you consider the whole of humanity involved in healthcare. Hospital expenses can be significantly reduced as AI takes over tedious tasks and replaces them with sophisticated algorithms. AI can also be used to review cases and help determine what is needed for the hospital. After we have given you a brief overview of AI usage, let us now look at the main benefits of AI in healthcare to help you decide if it is worth your investment. Let’s explore the advantages and disadvantages of this ground-breaking technology that is revolutionizing the delivery of healthcare.

Real-life data and experience in the spotlight

Doing nothing because AI is imperfect creates the risk of perpetuating a problematic status quo. Even if AI systems learn from accurate, representative data, there can still be problems if that information reflects underlying biases and inequalities in the health system. Resource-allocation AI systems could also exacerbate inequality by assigning fewer resources to patients considered less desirable or less profitable by health systems for a variety of problematic reasons. Predictive analytics can be gleaned as these data points are compiled to provide a view into the populace. These insights can then be used for risk stratification of populations based on genetic and phenotypic factors as well as behavioral drivers and social determinants.

As well as providing thought leadership around AI in healthcare, we are developing new products and services that deliver cutting-edge technology to transform healthcare. The EIT Health contribution sets out key areas of input to EU policy makers in response to the particular regulatory and policy needs of AI and data-rich solutions in health and healthy aging. For example, a 2019 study reported in The Lancet Oncology has shown the accuracy of an AI system in diagnosing prostate cancer in tissue samples.

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