Artificial intelligence (AI) is the most common watchword for investors, corporate leaders and researchers across industries and disciplines. Disentangling real potential from hype and making active, valuable use of the technology are now top priorities for savvy executives, including in demanding, high-stakes and heavily regulated fields like healthcare.

Part of the job of healthcare leaders is to find and harness breakthroughs that can improve overall care delivery for patients. AI technology and the related concept of machine learning (ML) are squarely in focus today. Organizations that can find productive and responsible uses for these technologies could transform their operations in the years ahead.

This combination of intensive interest, major challenges and massive potential means healthcare leaders need to study AI in depth. Structured forums like executive education can give them the insights they're looking for.

How is AI being used in healthcare?

One important point to remember when considering AI in healthcare is that this isn't a speculative future technology. ML in some form has a relatively long history, and care providers are already finding use cases for even newer AI offshoots.

In fact, the umbrella term "AI" is sometimes used to speak about a wide spectrum of technologies, including:

• Generative AI (GenAI).
• Machine learning.
• Deep learning (DL).
• Advanced data analytics.

Between these AI system types, there are several proof-of-concept projects underway at healthcare organizations. These provide practical ways to put the technology to use now, while researchers develop the next generation of breakthroughs.

Current AI technology use cases within the health system include:

• Assistance with diagnoses: ML and big data analytics tools are useful for finding patterns in massive, unstructured patient data sets, including medical images. This means they have potential as diagnostic technologies, searching for warning signs in test results that can lead to accurate, early diagnoses. In turn, these detections can enable better patient outcomes.
• Patient monitoring: Continuously running AI algorithms can also serve as components in real-time monitoring systems. In these cases, instead of finding details in static data sets, the technology would react to fluctuations in condition, as conveyed by sensors.
• Healthcare organization administration: Hospitals and other large care providers are complex businesses. This means they face the same daunting office efficiency challenges as other companies of their size. Breakthroughs in using AI tools to save time and manual effort on everyday tasks therefore apply to these organizations.

There are also other important uses emerging in related areas, such as the development of new drug formulas via AI-powered workflows. Wherever there's a potential application for powerful algorithms, researchers now have a new group of technologies to draw upon.

Leaders in the healthcare field who understand the underlying technical characteristics of AI are well-equipped to direct companies as they move from this present era into an even more technologically sophisticated future.

Read more: Delve deeper into the progress made throughout the global healthcare system in healthcare AI use cases.

What are the advantages of AI in healthcare?

Correctly applying AI technologies in a healthcare context can come with several advantages, helping hospitals and healthcare providers make sustainable progress toward their overall goals. Both operational efficiency gains and patient outcome transformations are on the table in these best-case scenarios.

The specific improvements healthcare leaders are seeking from an AI solution in any given case depend on factors ranging from the type of technology they're using to their specific business needs. While AI is an exciting and hyped tech area, its role is generally the same as any new addition: to give organizations a better way to address their objectives.

Good matches between AI's capabilities and healthcare providers' needs include:

• Faster diagnoses and imaging results: Fast and accurate diagnoses can drastically improve patient outcomes, giving clinicians and other healthcare professionals more time to develop and implement a unique treatment plan. Applying ML algorithms in the diagnostic process to interpret digital health data and medical images can help in this area, potentially saving lives.
• Improved clinical processes and care experiences: Healthcare provider organizations of all kinds are dedicated to enhancing health outcomes. Infusing their clinical workflows with advanced technologies can help these organizations ensure they're attentive to patients' needs at all times, bettering their ability to help those patients.
• Enhanced patient oversight efficiency: Managing the inpatient experience with AI assistance is a way for providers to accomplish more with their allotted resources. The potential increased efficiency can make a difference for organizations dealing with staffing shortages among clinicians or other healthcare workers.
• Monetary savings through enhanced processes: Efficiency in all aspects of a business can result in cost savings. For care providers, this could mean something big, like preventing patient readmissions through better diagnostics, or small, like saving time on administrative tasks. Using AI clerical or clinical workflows is a tech-forward way to address this issue.

Healthier patients, more efficient workplaces and other positive outcomes are all potential results of developing and deploying AI programs in a care delivery setting. Of course, they're not automatic. Leaders must be ready to grapple with the challenges of AI to unlock these upsides.

See more: Watch a webinar on the potential of AI in healthcare.

What are the challenges of using AI in healthcare?

AI system development, especially following the mainstream embrace of GenAI, has come with difficulties and considerations for businesses undergoing AI implementation. The related issues are especially intensive in heavily regulated fields like healthcare, because they often involve compliance challenges.

The need for scrupulous regulatory adherence in healthcare is obvious. This is an industry dealing with highly sensitive health information on a daily basis, and one with a massive impact on people's overall well-being. Finding ways to stay in compliance with the web of rules governing healthcare while still pushing the envelope of new technological approaches will be a priority for years to come.

Some of the specific issues for healthcare industry leaders to navigate include:

• A need for interpretability: To be confident in medical test results, experts need to understand how and why the systems came to these conclusions. This is important to keep in mind when dealing with GenAI, which can sometimes be somewhat of a "black box," obscuring the processes that lead it to produce specific answers.
• Ethics and bias concerns: Training datasets are a major issue where AI is concerned. If the health information used to educate a model contains inbuilt bias against certain groups or demographics, that could be replicated in the resulting answers. Avoiding such bias is an ethical issue in all industries, but especially in healthcare, where algorithms could someday be tasked with recommending crucial treatments with life-changing health outcomes.
• Regulatory compliance: The strict and seriously enforced regulations guiding all functions in healthcare — but especially those around patient treatment and health data management — were not designed with GenAI in mind. This is an important point, because it's up to healthcare leaders to ensure they're not violating these regulations with the way they implement technology and use health data.

It's easy to see multiple paths forward for healthcare executives today. Those who understand how to effectively implement the latest AI technologies are set up to seize new and unique advantages. Those who overlook key considerations, whether technical or regulatory, are less likely to succeed.

Executive education from a leading institution such as MIT Sloan can make a difference for these leaders, equipping participants with the know-how they need to navigate the AI era ethically and confidently.

Read more: See how manufacturers are using AI for another perspective.

Learning about cutting-edge healthcare leadership

Stewardship of a modern, technologically enabled healthcare organization means understanding where AI fits in. Current and aspiring executives can build the necessary knowledge from industry experts in MIT Sloan Executive Education courses like Transforming Healthcare with AI.

Courses from leading institutions such as this one connect participants with faculty members who are actively engaged with the concepts they teach. These instructors can provide up-to-date insights from the fast-moving AI space, which is important considering the rapidly changing conditions. In the less than five years since the breakthrough of ChatGPT, the healthcare industry’s thinking about the uses of AI has changed completely, and learners need to ensure they're receiving the current picture.

The Transforming Healthcare with AI curriculum teaches participants about:

• The principles that underlie GenAI, ML, DL and big data analytics.
• The specific ways healthcare organizations are using AI, including in diagnostics, monitoring and hospital management.
• Practical solutions for the difficulties with AI in medical settings, ranging from results interpretability to ethical challenges and compliance.
• Tools and mentalities that will help with the creation and integration of a sustainable AI strategy.

In an era when institutions across industries and around the world are searching for leaders who will help them create responsible, common-sense AI deployment strategies, there is a need for educational programs to teach the fundamentals. This is the idea behind Transforming Healthcare with AI: giving participants the perspective to take the lead in AI implementation.

Read more: Discover AI's value as a business tool.

Executive education for excellence in high-tech healthcare

Are you a technology executive considering a move into healthcare? What about a hospital executive seeking to become more engaged with the development and integration of advanced tech programs? Do you want to move up within a healthcare organization or build a strong resume for the field?

Those are all potential motives to deepen your knowledge of advanced healthcare AI strategies. Working with AI in healthcare is not a simple or straightforward process, due to the potential friction between cutting-edge technology and strict regulations. Executive education on this topic can help you resolve the challenges and build a strong case for your own expertise.

The pressure to work with AI in intelligent and impactful ways is a consistent theme in healthcare and beyond. Executive education can help you turn this fact from a challenge into an opportunity.

Enroll in Transforming Healthcare with AI