Artificial intelligence is used in healthcare for everything from answering patient questions to providing surgical assistance to developing new medications.
How can artificial intelligence support healthcare?
According to Statista, the artificial intelligence (AI) healthcare market, which was worth $11 billion in 2021, is expected to be worth $187 billion by 2030. This massive growth means that we will likely continue to see significant changes in the way healthcare professionals operate hospitals, pharmaceuticals, biotechnology and other healthcare companies.
Better machine learning (ML) algorithms, better access to data, cheaper hardware and the availability of 5G have contributed to the increasing use of AI in healthcare, accelerating the pace of change.Artificial intelligence and machine learning can sift through and analyze vast amounts of health data– from medical records to clinical trials to genetic information – much faster than humans.
Artificial intelligence can help make healthcare operations more efficient
healthcare organizations are using AI to improve efficiency in everything from back-office tasks to patient care. Below are some examples of how AI can be used to benefit staff and patients:
Administrative Workflow: Healthcare workers spend a lot of time filling out paperwork and completing other administrative tasks. Artificial intelligence and automation can simplify many everyday tasks, freeing up staff time for other tasks and giving them more time to interact with patients. For example, generative AI can help doctors take notes and summarize content,which can help keep medical records as accurate as possible.AI can also help with accurate coding and information sharing between departments and billing.
Virtual Care Assistants: A study found that 64% of patients feel comfortable using AI to have 24/7 access to caregiver responses. AI-powered virtual care assistants (chatbots, apps, and other AI-powered interfaces) can be used to answer questions about medications, send reports to doctors or surgeons, and help patients schedule doctor visits. These types of routine tasks can help reduce the burden on clinical staff, who can then spend more time directly on patient care, where human judgment and interaction are critical.
Reduce dosing errors: Artificial intelligence can be used to detect errors when patients self-administer medications.One example comes from a study published in Nature Medicine that found that up to 70% of patients donot take insulin as prescribed. An AI-powered tool that runs in the patient’s background (similar to a Wi-Fi router) could be used to report errors in apatient’s administration of their insulin pen or inhaler.
Less invasive surgeries: AI-powered robots can be used to work on delicate organs and tissues to reduce blood loss, risk of infection and post-operative pain.
Artificial intelligence has the potential to improve the user experience in healthcare
A recent study found that 83% of patients rated poor communication as the worst part of their experience, indicating an urgent need for clearer communication between patients and providers. Artificial intelligence technologies such as natural language processing (NLP), predictive analytics and speech recognition can help healthcare professionals communicate more effectively with patients. For example, AI can provide more detailed information about a patient’s treatment options, allowing the healthcare provider
to have more meaningful conversations with the patient to make decisions together.
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AI can be used to increase the efficiency of health diagnosis
According to the Harvard School of Public Health, the use of artificialintelligence for diagnosis is still in its infancy, but can reduce medical costs by up to 50% and improve health outcomes by 40%.
One use case is that of the University of Hawaii, where a research team found that implementing deep learning technology based on artificial intelligence could improve breast cancer risk prediction.More research is needed, but the lead researcher pointed out that the AI algorithm can be trained on a much larger set of images than a radiologist: up to a million X-ray images, see more. In addition, this algorithm can be replicated for free except for hardware.
The MIT team developed an ML algorithm to determine when a human expert is needed. In some cases, such as detecting cardiomegaly on chest X-rays, they found that a hybrid human-AI model produced the best results.
Another published study found that artificial intelligence detects skin cancer better than experienced doctors.Scientists from the US, Germany and France used deep learning on more than 100,000 images to identify skin cancer. When the AI’s results were compared with those of 58 dermatologists from around the world, it was found that the AI performed better.
Artificial intelligence in healthcare could mean better health monitoring and prevention
As health and fitness trackers become more popular and more people use apps that track and analyze detailed information about their health, they can share these data sets with doctors in real time to monitor health problems and alert them if something goes wrong goes.
AI solutions such as big data applications, machine learning algorithms and deep learning algorithms can also be used to help people analyze large amounts of data to support clinical and other decision making. AI can also be used to detect and monitor infectious diseases such as COVID-19, tuberculosis and malaria.
Artificial intelligence can help link different health data
One of the benefits that the use of artificial intelligence brings to healthcare systems is that it facilitates the collection and sharing ofinformation. Artificial intelligence can help providers track patient data more effectively.
An example is diabetes. According to the Centers for Disease Control and Prevention, 10% of the U.S. population has diabetes. Patients can now use wearables and other monitoring devices that provide them and their healthcare team with information about their glucose levels.AI can help providers collect, store and analyze this information and provide insights based on data from large numbers of people. Using this information can help healthcare providers figure out how to better treat and manage illnesses.
Organizations are also beginning to use artificial intelligence to improve drug safety. For example, SELTA SQUARE is innovating in the pharmacovigilance (PV) process, a regulatory discipline that enables the identification and reporting of adverse drug reactions and the subsequent assessment, understanding and prevention of these effects. PV requires significant effort and attention from pharmaceutical manufacturers as it continues throughout its shelf life from the clinical trial phase to drug availability.Selta Square uses a combination of AI and automation to make the PV process faster and more accurate, which helps make medicines safer for people worldwide.
In some cases, AI could reduce the need to test potential drug compounds physically, which is an enormous cost-savings. High-fidelity molecular simulations can run on computers without incurring the high costs of traditional discovery methods.
AI also has the potential to help humans predict toxicity, bioactivity, and other characteristics of molecules or create previously unknown drug molecules from scratch.
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AI governance in healthcare
As AI becomes more important in healthcare delivery and more AI medical applications are developed, ethical and regulatory governance must be established.Areas of concern include the potential for bias, lack of transparency, privacy issues related to the data used to train AI models, and security and liability issues.
“Managing AI is critical, especially when it comes to clinical applications of the technology,” said Laura Craft, senior vice president at Gartner. “However, because new AI techniques are largely new territory for most [health care organizations], there is a lack of common principles, processes and guidelines for volunteer entrepreneurs to follow when designing their pilotprograms. »
The World Health Organization (WHO) spent 18 months speaking with leading experts in ethics, digital technology, law and human rights, as well as various health ministries, to produce a report entitled “Ethics and Governance of Artificial Intelligence for Health”. This report highlights the ethical challenges of using AI in healthcare, identifies risks, and presents six consensus principles to ensure AI works for the benefit of society:
Protection of autonomy
Promote people’s safety and well-being
Ensure transparency
Promote responsibility
Provide equity
Promote responsive and sustainable tools WHO Report
also includes recommendations to ensure that the management of AI in healthcare maximizes the technology’s potential and keeps healthcare workers accountable and responsive to the communities and people they work with.
The future and potential of artificial intelligence in the healthcare ecosystem
AI offers the ability to reduce human errors, support specialists and medical staff, and provide 24/7 patient services. As AI tools advance, intelligence can be used even more artificially to read medical images, x-rays, and scans, diagnose medical problems, and create treatment plans.
‘s AI applications will continue to help streamline a variety of tasks, from answering phone calls to analyzing population health trends (and likely applications yet to be considered). Future AI tools could, for example, automate the work of doctors and staff or make them more efficient. This allows people to spend more time receiving professional, personalized care that is more effective and compassionate.
IBM and artificial intelligence in healthcare
When patients need help, they don’t want to (or can’t) wait. Healthcare resources are limited, so help is not always available immediately or aroundthe clock, and even small delays can leave you frustrated, isolated, or make certain conditions worse.
IBM Watsonx Assistant AI healthcare chatbots can help providers do two things: focus their time where it’s needed and allow them to call patients to quickly get answers to simple questions.
IBM Watsonx Assistant uses deep learning, machine learning, and natural language processing (NLP) models to understand questions, find the best answers, and perform transactions using conversational artificial intelligence.