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September 9, 2022
AI, Data Science and Machine Learning

AI in the healthcare industry: Use cases for artificial intelligence in the healthcare market

Artificial intelligence (AI) is ushering in a better healthcare industry through algorithms, robotics, and data analytics. These improvements have become more apparent during the COVID-19 pandemic, where robots were deployed in hospitals and clinics to sanitize rooms, perform remote diagnoses and monitoring, and transport specimens to the labs.

Advancements in software and cloud-based technologies have also led to automated medical imaging, telehealth, more accurate diagnoses, and personalized patient care. These developments have led to increased investments in AI solutions in the industry, including venture capital firms funding healthcare-related AI projects worth USD $85 billion, according to consultancy firm McKinsey. The firm also highlighted that about 23 AI applications are already widely used in the sector. In this article, we’ll discuss some of these benefits and use cases, and how they are transforming the healthcare industry.

Benefits of AI in healthcare

AI is poised to change the future of healthcare as data analytics drive more accurate decision-making, provide better medical references, and streamline research and development.

Accurate medical imaging analysis

Histopathology images are some of the most utilized medical imaging to identify cancers. However, pathologists’ workload has been steadily increasing, leading to misdiagnoses. A 2021 Tulane University research showed that AI can accurately detect and identify colorectal cancer through tissue scan analysis. A machine learning program was used to analyze 13,000 images of colorectal cancer across 13 cancer centers in China, Germany, and the US. At the end of the research, the algorithm was able to identify the cancer at 98 percent accuracy, [JC1] slightly better than the average human pathologist at 97 percent. Scientists are optimistic that AI-driven medical imaging analysis can significantly speed up diagnoses.

Enhanced diagnoses and treatment applications

Misdiagnoses are some of the most severe consequences of poor healthcare service. Unfortunately, it happens too often as healthcare providers become understaffed and doctors are stretched too thin. One of the early AI solutions to automate diagnosis was IBM’s Watson for Health, focusing on precision medicine, particularly cancer treatments. Watson can refer to thousands of medical information, including medical journals, case studies, treatment plans and their results, and symptoms. Another AI solution is Google’s DeepMind Health, which collaborates with physicians and researchers to diagnose real-world diseases. To do this, the technology uses machine learning and neuroscience to create a deep learning model that can connect information better. Another example is the groundbreaking AlphaFold, which uses AI to predict cell protein structures to understand how they malfunction and lead to diseases. Aside from diagnosis, this tool can greatly advance drug development and research.

Early detection

Through AI’s access to thousands of disease databases, it has become better at detecting illnesses, specifically cancer. According to the American Cancer Society, before AI, mammograms often give false results, leading to 1 of 2 healthy patients being misdiagnosed. With AI, the analysis of mammogram results is now 30 times faster with 99 percent accuracy. This has eliminated the need for costly and unnecessary biopsies. Meanwhile, at-risk patients for specific genetic comorbidities like diabetes can now also be monitored 24/7 using wearables. These devices can alert doctors and clinics of any irregularities in their patients’ heart rates or blood sugar levels. Potential heart attacks are also flagged early, giving patients more chances of fully recovering.

Fast-tracked drug development and research

Developing drugs and vaccines is one of the most expensive projects in healthcare. According to the California Biomedical Research Association, it takes about 12 years to transition a drug from laboratory to clinical practice. In addition, success rates are often low, with only five in 5,000 medicines in preclinical testing advancing to human testing. On average, developing a new drug costs USD $359 million. However, drug discovery has become more efficient and less time-consuming with AI and machine learning. A combination of software and physical robots can run experiments 24/7, monitor results, record analyses, and suggest further steps. Aside from assisting in lab tests, AI can help prepare the hundreds of complex documents needed to move a new drug through each research phase.

How AI Superior can help

Are you looking to implement AI-based solutions in your healthcare organization or pharmaceutical business? We can help identify which process you can automate using machine learning and natural language processing. Our services and solutions can help the research and development of pharma products, including drug analysis and cancer image translation. We look at all the factors that affect your healthcare business, so we can tailor a customized AI solution that addresses all your needs.

Contact us for any query or demo request.

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