Organizations across virtually every industry—from start-ups to multinational corporations—are implementing artificial intelligence (AI). A 2025 global survey by McKinsey & Co. found that 88% of companies used AI in at least one business function, illustrating the technology’s widespread adoption.
Healthcare is a sector poised to benefit significantly from AI advances. Many healthcare organizations are already deploying AI tools to improve diagnosis, streamline operations, and support more efficient medical research and innovation.
Despite its potential advantages, AI use in healthcare carries risks. Concerns over data security, ethical dilemmas, and cost present major hurdles to organizations looking to integrate AI tools into their operations.
For healthcare professionals interested in using the technology, understanding both the pros and cons of AI in healthcare is essential. Examining how AI is currently used, and the opportunities and challenges it presents, can help organizations make more informed decisions about how best to implement it.
How Is AI Used in Healthcare?
The earliest applications of AI-assisted medicine emerged in the 1970s with rudimentary consultation programs that provided physicians with patient management and treatment options for specific illnesses. One early example was the Causal-Associational Network (CASNET), which used disease data to guide clinical decision-making.
In 1986, physicians began using an DXplain, an AI clinical decision support system that allowed them to input patients’ symptoms and receive a diagnosis, along with a description of the disease, according to a 2024 report on AI use published in Clinical Pathology. It launched with approximately 500 catalogued diseases and now contains more than 2,400.
Since then, AI applications in healthcare have greatly expanded.
- Medical imaging and diagnostics: AI systems can analyze diagnostic images such as X-rays, MRIs, CT scans, and ultrasounds to detect abnormalities that may be difficult for the human eye to identify. These tools are commonly used in radiology and other diagnostic fields to assist physicians in interpreting imaging results.
- Personalized treatment: AI models can sift through large datasets that include patients’ histories, genetic information, and clinical outcomes. These systems build on advances in health informatics, providing data-driven insights that help guide treatment decisions for individual patients.
- Robot-assisted surgery: In certain procedures, robotic systems supported by AI can assist surgeons by translating their hand movements into more precise actions. These systems are particularly useful in specialties such as urology and gynecology.
- Drug discovery and pharmaceutical research: Machine learning and deep learning models can analyze biological and chemical datasets to predict how medication candidates may behave in the body. Pharmaceutical researchers use these tools to model pharmacokinetic properties such as drug absorption and metabolism, as well as toxicity.
- Remote patient monitoring: Wearable devices and sensors such as smartwatches and continuous glucose monitors collect real-time data on patients’ vital signs and health metrics. AI algorithms analyze these data streams to identify patterns that can inform clinicians in treating patients with chronic conditions.
- Automation: AI tools are increasingly used to support routine administrative tasks, such as scheduling appointments, generating clinical notes, managing patient records, and sending medication reminders.
Advantages of Using AI in Healthcare
As AI tools become embedded in clinical and operational workflows, many organizations are assessing the benefits and challenges of AI in healthcare. While the technology introduces important considerations around data security and systemic bias, it also offers significant opportunities to improve how care is delivered and reduce costs for both patients and providers, among numerous other potential benefits.
Below are notable examples of how AI is improving healthcare today.
Facilitating Early Disease Detection
One of the most widely discussed advantages of using AI in healthcare is its ability to help clinicians identify diseases earlier. AI-driven systems can scan through large volumes of medical data, including imaging results and patient records, at speeds that would be impossible for humans to replicate. In diagnostic imaging, for example, AI tools can help clinicians detect subtle abnormalities that may indicate conditions such as cancer or neurological disorders.
Research suggests that these capabilities could improve diagnostic accuracy in certain cases. A recent study published in the European Journal of Cardiovascular Medicine found that AI models analyzing radiographic images achieved 92% accuracy in detecting pulmonary conditions (pneumonia, tuberculosis, lung cancer), compared with 78% accuracy for manual interpretation by clinicians.
Improving Operational Efficiency
AI is also helping healthcare organizations manage complex operations more effectively. Hospitals must coordinate patient admissions, staffing levels, bed availability, and numerous other logistical variables—tasks that can become increasingly difficult as patient volumes rise. AI can analyze real-time and historical data to help hospitals anticipate demand and allocate resources.
Some health systems are already using predictive models to forecast patient flow and staffing needs. As reported by GE Healthcare, Duke Health used AI-powered workforce planning tools to reduce reliance on temporary labor by 50%, while also improving productivity by 6%. These types of systems can help organizations make more informed operational decisions across a wide range of clinical settings.
Allowing Providers to Spend More Time With Patients
Administrative responsibilities account for a substantial portion of healthcare professionals’ workloads. Tasks such as documenting patient histories, recordkeeping, and regulatory reporting often take time away from direct patient care.
A 2023 McKinsey & Co. report found that nurses spent 15% of their shift on documentation, with roughly 10% to 20% of their shift spent on tasks that could be optimized through technology such as automation tools. AI tools can relieve some of this burden by assisting with documentation and other routine processes. For example, automated note-taking tools can capture and organize information from patient encounters, allowing providers to avoid manual typing and spend more time connecting with patients.
Making Drug Development More Efficient
AI is also helping pharmaceutical researchers accelerate parts of the drug development process. Using chemical and biological data, AI systems can help scientists identify promising therapeutic targets and determine which compounds should advance to testing.
Pharmaceutical companies such as Pfizer, Bayer, and Roche have begun using AI tools to power their research and aid in decision-making, allowing them to streamline aspects of pharmaceutical innovation.
Reducing Healthcare Costs
Rising healthcare costs remain a major concern. Data from the U.S. Centers for Medicare and Medicaid Services shows that U.S. healthcare spending accounted for approximately 18% of gross domestic product (GDP) in 2024, and is projected to rise to 20% by 2033. Morgan Stanley notes that these figures are much higher than the 11% average in comparable countries, and that U.S. spending could rise to 25% of GDP by 2050.
AI technologies can help defray some of these costs by improving efficiency in areas such as hospital operations and drug development, as well as optimizing staffing and supply chain management. AI applications across the healthcare sector could generate savings of $400 billion to $1.5 trillion over time, according to Morgan Stanley, relieving the financial burden on both patients and providers.
Challenges of Using AI in Healthcare
While the benefits of AI are substantial, organizations also need to weigh the risks and challenges of using AI in healthcare before implementing these tools at scale. Issues related to privacy, bias, cost, and clinical decisionmaking have raised important questions.
Addressing the concerns below is essential for ensuring that AI technologies are used responsibly and effectively across the healthcare sector.
Data Privacy Risks
AI systems rely on vast amounts of sensitive patient data, including electronic health records (EHRs), diagnostic results, and demographic details. Because EHRs contain extensive personal information, they can become attractive targets for cyberattacks or unauthorized access. Healthcare is one of the most attacked sectors globally, and the cost of a data breach is the highest of any sector: $9.48 million per incident, $5 million higher than the global average, according to IBM.
The 2024 report in Clinical Pathology noted that AI’s ability to aggregate and analyze medical data in even greater detail than records filled out by healthcare professionals could potentially expose even more sensitive information about patients’ health histories, raising additional privacy concerns.
Perpetuation of Biases and Disparities
Another significant concern is the possibility that AI may reinforce existing health disparities. AI models learn from historical datasets, and if that data contains biases or lacks representation from certain populations, the resulting algorithms may produce uneven outcomes.
Researchers have warned that AI systems trained on incomplete or biased data may disadvantage minority groups or underrepresented communities. In some cases, AI-based health recommendations have been shown to vary based on demographic characteristics, such as whether patients are insured or uninsured. Without deliberate efforts to address bias, AI systems may unintentionally exacerbate inequities in care delivery.
Overreliance
AI tools can provide valuable insights, but relying too heavily on their recommendations may create new risks. Overreliance on AI-based systems could reduce critical thinking or lead providers to accept algorithmic outputs without sufficient scrutiny.
A 2023 report in Emergency Medicine Australasia found that clinicians worry that excessive dependence on AI tools could contribute to skill loss, particularly among less-experienced practitioners.
High Implementation Costs
Adopting AI technologies at scale typically requires significant financial investment. Healthcare organizations may need to upgrade infrastructure and invest in employee training to effectively leverage these new systems.
These costs can present particular challenges for smaller providers. According to industry analyses, the up-front expenses associated with deploying AI tools—including implementation, training, and maintenance—can limit adoption in some settings.
Using AI in Healthcare Effectively and Responsibly
As AI use grows across the sector, organizations need to carefully consider how to implement the technology. In addition to understanding the pros and cons of AI in healthcare, organizations need to establish practices to ensure that these systems are deployed ethically and in ways that support high-quality patient care.
Several strategies can help healthcare organizations integrate AI more responsibly while maintaining patient trust and supporting effective clinical decision-making.
Investing in Employee Training
Successful AI implementation depends not only on the technology itself but also on the people using it. Though they don’t need to be experts, healthcare professionals should understand how to use AI tools, interpret their outputs, and apply these insights within a clinical setting.
Proper training is essential to prevent overreliance and ensure that healthcare workers use AI appropriately. Educational initiatives can help clinicians understand the strengths and limitations of the technology while reinforcing the importance of maintaining professional judgment when evaluating AI-generated recommendations. Training programs should be designed to make AI accessible to staff regardless of their skill level, allowing organizations to integrate these tools more effectively into everyday workflows.
Training AI on Unbiased Data
Because AI systems learn from historical datasets, the quality and diversity of that data are crucial in determining how the technology performs. If training data reflects existing disparities in healthcare access or treatment, AI models may reproduce these inequities.
Addressing bias during the development process is vital for ensuring fair outcomes. Some researchers recommend intentionally incorporating data from underrepresented populations to create more balanced training datasets. Strategies such as oversampling marginalized communities and monitoring algorithms for bias—sometimes referred to as “algorithmovigilance”—can help organizations identify and address issues before they affect patient care.
Using AI as a Complement to Human-Centered Care
AI tools should be used as a supplement to human clinical expertise, not a replacement. While algorithms can analyze complex datasets and recognize patterns, healthcare professionals are responsible for interpreting these insights within the broader context of a patient’s medical history and personal circumstances.
This collaborative approach allows healthcare providers to use AI to enhance diagnostic accuracy and efficiency while still relying on human expertise and empathy—factors that remain central to patient-centered care.
Ensuring Transparency and Consent
Patient trust is crucial to successful AI adoption in healthcare. Because AI relies on extensive patient data, healthcare organizations need to clearly communicate how these systems use and process that information.
When AI systems are involved in patient care, organizations should prioritize transparency and informed consent. Clear policies around data use, confidentiality, and algorithmic decision-making can help healthcare organizations maintain patient trust while responsibly integrating AI into clinical practice.
Additional Resources for AI in Healthcare
Ongoing research continues to explore the pros and cons of using AI in healthcare, offering new insights into how these technologies may impact care delivery and operations. The following resources provide additional perspectives on the ethical, clinical, and economic considerations associated with AI deployment in healthcare:
Navigating the Rise of AI in Healthcare
AI is rapidly reshaping modern healthcare. As adoption expands, understanding the benefits and challenges of using AI technology will be essential for organizations seeking to harness its potential while safeguarding patient trust and clinical integrity.