What Is AI in Healthcare?

From research to patient care, healthcare generates massive amounts of data. To some extent, delivering appropriate and efficient care depends on making sense of all that information. Artificial intelligence, which encompasses machine learning, deep learning, generative AI (GenAI), and other algorithmic methods, is designed to analyze vast amounts of disparate data to find and act on patterns at a speed and scale beyond human abilities.

When applied to healthcare, AI offers myriad data-driven benefits for patients, clinical and nursing staff, and administrators. Outcomes such as improved diagnostic speed and accuracy, remote patient monitoring, and virtual assistants augment patient support. Streamlined workflows, automated administrative tasks, and improved inventory tracking reduce costs and free staff for higher-value personal interactions. In the lab, AI is automating laboratory instruments to deliver precise, accurate test results at scale; accelerate diagnosis and drug discovery; and enable precision medicine.

AI-augmented security solutions and AI PCs help healthcare organizations remain compliant and protect their systems and data, and that of their patients, from cyber threats.

Benefits of AI in Healthcare

AI is delivering tangible benefits in healthcare and life sciences:
 

• Return on investment: A 2023 Microsoft-commissioned IDC study found that healthcare organizations achieved a demonstrable return on AI investments within 14 months, with an estimated US$3.20 mean ROI for every dollar spent on an AI project and initiative.³
• Operational efficiency: AI can automate administrative tasks, freeing staff for higher-value personal interactions. Natural language processing (NLP) can be used for speech-to-text capabilities to streamline the electronic health records (EHR) documentation process. GenAI tools can be used to guide and personalize staff coaching and skills development. AI can be used to improve inventory management or analyze business processes to recommend efficiency improvements.
• Accelerated diagnostics and detection: AI-enhanced medical imaging workflows speed time to results with more visual details. AI-augmented analysis can flag anomalies and patterns missed by the human eye. Analysis of genetic data or patient records from across disparate systems can alert clinicians to early signs of disease, enabling quicker time to treatment.
• Improved access to care and monitoring: AI-enabled wearables can remotely track patient vitals and automate intake to save time during remote consultations. AI-enabled unified platforms in hospitals can provide more near-real-time and retrospective data to staff to improve workflows and care delivery or be used to create virtual nursing stations where a single nurse can monitor, in some cases, more than 50 patients on a single user interface across units and/or facilities.⁴
• Improved patient experience: AI-augmented self-service kiosks can enable streamlined patient check-in, scheduling, and payments. AI chatbots and virtual assistants can help patients generate care checklists from post visit notes or search provider materials for answers to noncritical health questions.
• Personalized medicine: AI can be used to create personalized treatment plans by analyzing data to predict the most-effective therapies. On a larger scale, in the lab, AI-enabled systems can analyze vast amounts of data to diagnose diseases and personalize treatments and patient-centric care for specific subsets of individuals that share genetic markers, traits, or conditions.
• Accelerated research and discovery: In search of new life-saving treatments, life sciences researchers increasingly rely on AI to accelerate time to insight and reduce time to market through efficiency and productivity gains. AI can enhance research and development processes, accelerate drug discovery, optimize clinical trials, and aid in personalized medicine.

AI Considerations

While using AI brings considerable benefits, it can also present challenges and risks that must not be overlooked:
 

• Security and privacy: Healthcare is one of the most regulated industries in the world and for very good reason. The fact that medical facilities have access to and store so much protected health information (PHI) may lead to caution among some administrators in adopting artificial intelligence in healthcare. However, because of the regulatory landscape, healthcare entities already have data governance policies in place, which provide an important foundation for beginning to use AI.
• Biases: AI models are trained on and learn from existing data that may hold biases. Therefore, there is a potential that AI models may inherit those biases and propagate them in the subsequent responses they generate. To address this, there is a growing emphasis on explainable AI, which gives organizations, decision-makers, and data scientists traceable insight into how an algorithm arrived at a specific result. With transparency into how the algorithm works, users can identify potential biases and discover how variables contribute to an outcome. Explainable AI is often required for regulated industries such as healthcare.

• Responsible use of AI: Additional challenges to using AI include ethical and societal implications. Leaders in AI innovation are collaborating on and committing to the pursuit of responsible AI practices that are transparent, inclusive, and accountable to help cultivate mindfulness about the potential impacts of AI on society and ensure that advances in AI continue to uplift communities.

AI Use Cases in Healthcare

While AI is already being applied in healthcare and life science, organizations have only just started to explore its potential. Here are some additional ways AI is providing value across the industry.

AIOps

A robust IT infrastructure is necessary for any healthcare organization, with particular emphasis on privacy and security. AI is well suited to automating systems monitoring, systems analysis, and security functions, giving IT professionals the information they need to keep things running smoothly:
 

• AI governance in healthcare: Bringing together business innovation and regulatory compliance benefits everyone. AI can help aggregate historically compartmentalized data, creating a clearer picture for clinicians and giving patients more control over their individual data.
• AI systems monitoring: IT teams can use AI-assisted security solutions to identify cybersecurity risks proactively. AI operations (AIOps) can identify and resolve or alert staff to PC and network issues more quickly than manual monitoring or self-reporting to limit business interruptions.

AI in Patient Care

Patient care is the most important aspect of healthcare, and use cases of AI to improve patient care continue to emerge. Earlier diagnoses, better patient monitoring, and faster medical imaging analyses are just three of the areas where AI is already making a positive impact on patient treatment:
 

• AI in predictive analytics: Finding cancer earlier improves outcomes. Pancreatic cancer is difficult to diagnose early, but the five-year survival rate is far lower when diagnosis comes at a later stage. An emerging use of AI is to help find cancerous lesions earlier. For example, analyzing scans of the upper abdomen where the pancreas is located may provide a human-confirmed AI medical diagnosis of pancreatic cancer much earlier and through a noninvasive scan. Models trained using anonymized data from thousands of patients who had lesions show great promise. Machine learning in healthcare, tailored to a specific purpose, such as finding lesions, is one of the most successful examples of AI in medicine.
• AI in nursing: As the population ages and birthrates decrease, the current shortage of healthcare workers is expected to grow. AI in nursing offers opportunities, such as remote patient monitoring, to help keep patients safe during times when the nursing staff is tending to a large number of patients.
• AI in imaging: One of the most common uses for AI currently is in imaging. AI is already being used extensively in imaging, and as generative AI becomes increasingly powerful, new use cases continue to emerge. Huge amounts of data must be processed for MRIs, CT scans, and other types of images to be useful. AI shortens both the length of time imaging requires, reducing the patient’s exposure, and the time it takes for images to be processed.

AI in the Lab

Research is central to improving healthcare. Drug discovery and epidemiology are only two of many areas where AI can be deployed to accelerate research successfully:
 

• AI in drug discovery: The study of genomics is redefining personalized medicine and generating enormous datasets. AI addresses the challenges presented by genomics research while also improving scientists’ ability to gain insights from the data.
• AI in epidemiology: When AI-powered technology is applied to epidemiology, it’s possible to track the spread of infectious diseases more accurately and predict future outbreaks and trends. AI tools are currently being used to predict when and where outbreaks will most likely occur. Researchers are also working to develop tools to provide public health alerts so that precautions can be taken before diagnosis trends upward.

The Future of AI in Healthcare

AI is developing quickly. Healthcare organizations are well positioned to explore the potential of AI solutions for healthcare. The regulatory landscape means health systems already have foundational data strategies, a crucial first step in implementing AI technology.

From the lab to discharge, AI in healthcare is helping to improve how the industry functions for providers, staff members, patients, and their families. Widespread adoption is happening now, and organizations and the patients they serve are already realizing the benefits.