Artificial intelligence is utilized in healthcare for various purposes, including responding to patient inquiries, aiding in surgical procedures, and creating new pharmaceuticals.
How can artificial intelligence enhance healthcare?
According to Statista, the market for artificial intelligence in healthcare, which was valued at $11 billion in 2021, is expected to surge to $187 billion by 2030. This significant growth suggests that substantial transformations in the operations of medical providers, hospitals, pharmaceutical and biotechnology firms, and other healthcare sector participants are likely to continue.
The expansion of AI in healthcare is driven by improved machine learning algorithms, increased data availability, more affordable hardware, and the introduction of 5G. These factors speed up changes within the sector. AI and machine learning technologies are capable of processing vast amounts of health data—from medical records and clinical trials to genetic information—much more rapidly than human analysis allows.
AI could enhance the efficiency of healthcare operations
Healthcare organizations are leveraging AI to streamline a variety of processes, ranging from administrative duties to patient care. Here are some ways AI can aid both healthcare staff and patients:
Administrative Workflow: Healthcare employees often spend considerable time on paperwork and other administrative duties. AI and automation can take over many of these repetitive tasks, allowing staff to focus more on direct patient interactions. For instance, generative AI can assist clinicians in taking notes and summarizing content to maintain detailed medical records. It can also improve the accuracy of coding and facilitate the sharing of information across departments for billing purposes.
Virtual Nursing Assistants: A study revealed that 64% of patients are comfortable with AI providing continuous access to support typically offered by nurses. AI-driven virtual nurse assistants, such as chatbots or apps, can handle inquiries about medications, relay reports to doctors, and assist with scheduling doctor visits. Automating these routine tasks can alleviate the workload on clinical staff, enabling them to dedicate more time to patient care, where human judgment and interaction are crucial.
Dosage Error Reduction: AI has the potential to identify mistakes in medication administration by patients. For example, a study published in Nature Medicine noted that up to 70% of patients do not follow prescribed insulin regimens correctly. An AI tool operating in the background could monitor and alert to errors in using devices like insulin pens or inhalers.
Less Invasive Surgeries: AI-enhanced robots can be employed in surgeries involving sensitive organs and tissues to minimize blood loss, reduce the risk of infection, and alleviate post-operative pain.
Fraud Prevention: Healthcare fraud, which costs about $380 billion annually, inflates medical premiums and out-of-pocket expenses for consumers. AI can detect unusual or suspicious patterns in insurance claims, such as charges for unperformed expensive services, unbundling of procedures, or unnecessary tests to exploit insurance payouts.
AI has the potential to enhance the healthcare experience for users
A recent study revealed that 83% of patients cite poor communication as the most negative aspect of their healthcare experience, highlighting the need for improved interactions between patients and providers. AI technologies such as natural language processing (NLP), predictive analytics, and speech recognition could significantly enhance how healthcare providers communicate with patients. For example, AI could provide more detailed information about treatment options, enabling providers to engage in more meaningful discussions with patients for collaborative decision-making.
AI could also boost efficiency in healthcare diagnoses
Harvard’s School of Public Health suggests that although it’s still early, utilizing AI in diagnosis could cut treatment costs by up to 50% and enhance health outcomes by 40%.
For instance, researchers at the University of Hawaii discovered that applying deep learning AI technology can enhance predictions of breast cancer risk. Although further research is necessary, the lead researcher noted that an AI algorithm could be trained on a much larger dataset of images than a radiologist can handle—potentially over a million radiology images. Moreover, this algorithm can be replicated with minimal costs aside from hardware.
Additionally, an MIT team developed a machine learning algorithm to determine when human expertise is essential. In cases such as detecting cardiomegaly in chest X-rays, they observed that a combination of human and AI efforts yielded the best results.
Another study demonstrated that AI outperformed experienced doctors in identifying skin cancer. Researchers from the US, Germany, and France employed deep learning on over 100,000 images to diagnose skin cancer. When compared to the assessments of 58 international dermatologists, the AI achieved superior performance.
AI could enhance health monitoring and preventive care in healthcare organizations
With the growing popularity of health and fitness monitors, and as more individuals use apps that collect and analyze their health data, these real-time data can be shared with doctors to monitor health conditions and issue alerts if problems arise.
AI technologies, including big data applications, machine learning algorithms, and deep learning algorithms, could be employed to help humans analyze extensive data sets for clinical and other decision-making purposes. Additionally, AI could be instrumental in detecting and tracking infectious diseases like COVID-19, tuberculosis, and malaria.