Artificial Intelligence and Machine Learning in Healthcare

By Dr. William Oliver Hedgepath  |  02/27/2025

For healthcare professionals, the use of artificial intelligence (AI) and machine learning (ML) in healthcare helps to improve patient data, patient experience, and outcomes. According to Onix, data from a Harvard University study estimates that:

• There is a 50% reduction in treatment costs when AI is used.

• There is a 40% improvement in patient health outcomes when healthcare professionals use AI.

Statista notes that by 2030, the estimated market cost of using AI in healthcare will be around $190 billion.

Artificial intelligence and machine learning are improving healthcare providers’ understanding of human biology as well as drug research and treatment options. However, with the use of AI and ML, there are still ethical issues, bias, and patients’ data protection concerns.

For example, artificial intelligence and machine learning allow the potential sharing of patient data to other medical professionals. There are also worldwide databases for confirming healthcare providers’ estimates of patient care, which could fall prey to attackers.

There Are Many Potential Uses for Artificial Intelligence and Machine Learning in Healthcare Fields

There are a number of ways that artificial intelligence and machine learning can be used in the healthcare field.

For instance, AI software could be used to more quickly analyze X-rays, MRIs, blood and urine samples, and any other information collected by one or more doctors. Plus, AI could be used to analyze a patient's use of certain drugs and quickly provide information about the patient’s current health and medical history.

AI could also be programmed to create more precise diagnoses and more personalized treatment plans for patients. For example, AI software may be used in the healthcare industry to more quickly search for genetic or molecular profiles to treat individual patients or groups of patients. By using AI tools to search global health intelligence databases, physicians could tailor their treatment recommendations to patients and improve patient safety.

AI could be used to create visual renderings of predictive analytics and update them with real-time metrics. This information could be especially useful for high-risk patients with serious health issues.

AI software could be programmed to allow chatbots to act as a virtual health assistant. These chatbots could be useful for patients who want to use a smartphone to have the chatbot quickly answer their general health questions.

Artificial intelligence and ML software using machine learning algorithms could be used to search and analyze healthcare data stored in databases across the U.S. They could potentially identify options for new drugs for use in treating new diseases, propose new combinations of drugs to combat chronic diseases, and analyze large databases of clinical data more quickly.

In some cases, AI is already being used for many applications involving healthcare facilities. For instance:

• Wearable devices are used for the remote patient monitoring of a patient's vital signs. This information is transmitted to a central database to provide physicians with more up-to-date knowledge and the opportunity for a better medical diagnosis when treating patients.
• Robotic systems are serving as assistants to surgeons. These robots are especially useful for delicate, minimally invasive surgeries where precision medicine is required.
• AI software is assisting in scheduling appointments for patients and is capable of recognizing human language. The software could also preschedule such appointments based on the continuous monitoring of patient health conditions.

The use of artificial intelligence and machine learning by medical providers immense potential for improving patient outcomes and operational efficiencies, but it also presents several challenges that must be addressed. For instance, safeguarding patient privacy, preventing unintended consequences to patients, improving natural language processing, and developing comprehensive regulatory policies are critical to ensuring the safe and ethical use of these technologies in healthcare organizations.

Additionally, the evolving nature of ethical standards, the integration of AI with human intelligence, and the need for cost-effective and accessible solutions are essential considerations. Some of the possible challenges for integrating artificial intelligence and machine learning include: 

1. Safeguarding patient privacy – Patient privacy must remain paramount; it will be necessary to keep sensitive patient data from being misused by hackers or scanners. In addition, there is the risk that false information regarding patients' healthcare could be published.
2. Training healthcare providers – Healthcare providers will need to be trained on how to properly use artificial intelligence and machine learning technology in the workplace and for data research. Preventing unintended consequences to patients – such as a misdiagnosis, inaccurate medical images, or a security breach involving a patient's computerized health records – is critical and must be prevented in healthcare systems for better patient outcomes.
3. Developing effective policies – Regulatory policies need to be developed to encompass all possible misuses of artificial intelligence and machine learning software in healthcare operations. Creating clear policies that prevent any errors in analysis is critical because improper treatment can cost the lives of people.
4. Maintaining ethical standards – As artificial intelligence and machine learning technology evolves, ethical standards for all applications of AI and ML in healthcare systems will need to be monitored and updated as needed. The ethical implications of protecting patients' data will also need to be considered and carefully supervised.
5. Controlling costs – Integrating artificial intelligence, natural language processing, and machine learning technologies into healthcare and medical devices is expensive, so that cost and accessibility of artificial intelligence and machine learning will impact healthcare costs. As the use of AI and ML becomes more widespread, perhaps that will reduce healthcare costs for medical professionals and patients.

Education and training for medical professionals will play a pivotal role in the successful adoption of artificial intelligence and machine learning. In the future, more healthcare education programs may see AI being used for virtual reality demonstrations. More realistic medical images could be created to help aspiring nurses and doctors to more easily visualize different diseases and how they could be treated.

AI could also become a personalized tutor to medical students who need to enhance their learning of new surgical techniques. This type of technology-enhanced precision medicine could improve direct patient care, patient safety, and patient outcomes in medical practices.

What Can Government Agencies Do to Encourage AI and ML by Healthcare Professionals?

Currently, the U.S. government seems to support the expanded use of AI and ML in every possible sector, which includes healthcare. As a result, there could be an increase in the regulations governing the safe applications of artificial intelligence and machine learning.

Several U.S. government agencies could also take a larger role in helping to implement artificial intelligence and machine learning in healthcare settings. Here are some agencies and their potential roles:

1. The Department of Health and Human Services (HHS) could release a strategic plan for the use of AI to enhance and protect the health and well-being of Americans. The plan outlines goals such as promoting trustworthy AI development, democratizing AI technologies, and cultivating AI-empowered workforces.
2. The Food and Drug Administration (FDA) might take a greater role regulating medical devices that incorporate artificial intelligence and machine learning. This agency could ensure that these technologies are safe and effective for use in healthcare.
3. The National Institutes of Health (NIH) could support studies to explore how artificial intelligence and machine learning can improve medical research, diagnostics, and treatment.
4. The Centers for Medicare & Medicaid Services (CMS) might explore how AI and machine learning can be used to improve healthcare delivery and reduce costs within the Medicare and Medicaid programs. They could also work on integrating these technologies into payment and service delivery models.
5. The Office of the National Coordinator for Health Information Technology (ONC), which focuses on the integration of artificial intelligence and machine learning into health information technology systems, could work on ensuring interoperability and the secure exchange of health information.
6. The Department of Defense (DoD) could use AI and machine learning in military healthcare settings to enhance medical readiness for servicemembers and improve care for both servicemembers and veterans. They could also explore the use of these technologies in medical research and logistics.

These agencies could collaborate to ensure that artificial intelligence and machine learning are implemented responsibly and effectively in the healthcare sector.

The Uses of AI and ML Are Only Limited by Our Imagination

In an era of healthcare transformation, the boundaries to using AI and machine learning are limited only by the extent of human imagination. These technologies could very well lead to numerous improvements in healthcare and revolutionize patient care over the next 30 years.

Healthcare Degrees at American Public University

For adult learners interested in gaining the knowledge and skills to pursue job opportunities in the healthcare field, American Public University (APU) offers a wide variety of degrees, including:

• An online Bachelor of Science in Health Information Management
• An online Bachelor of Science in Nursing (BSN)
• An Online Master of Science in Nursing (RN to MSN) with Nurse Leader and Nurse Educator concentrations

Taught by medical professionals, these degrees offer courses in electronic health records fundamentals, compliance, and informatics and analytics. Other courses include informatics and technology in healthcare, emerging scholarship and trends in healthcare, and healthcare systems and health policy.

For more information about these degrees, visit APU's nursing and health sciences degree program page.

Note: The bachelor's degree in health information management is accredited by the Commission on Accreditation for Health Informatics and Information Management Education ( CAHIIM®). CAHIIM® is a registered trademark of the Commission on Accreditation for Health Informatics and Information Management Education. 

The Bachelor of Science in Nursing and the Master of Science in Nursing programs are accredited by the Commission on Collegiate Nursing Education. The BSN program has specific admission requirements. This program is currently not open for admission to residents of Washington, D.C., or Washington state. The MSN program has specific admission requirements and is currently not open for admission to residents of Washington state.