The Tech Revolution in Healthcare: How AI Is Reshaping Medicine

An accelerating technology revolution is changing virtually every aspect of healthcare: how patients are screened, diagnosed, and treated, as well as how research is done, data is compiled and probed, and business operations are conducted. Artificial intelligence (AI) is at the forefront of much of this, as researchers and front-line users alike extend frontiers and seek to determine the best uses of machine learning, generative AI, and other AI-enabled processes.

For hospitals, health systems, physician groups, and other healthcare organizations, one promise of these changes is a better experience for patients. Telehealth, wearable devices, and digital health platforms are altering how patients gain access to increasingly personalized, more effective care. A shift toward value-based care, which aligns the interests of stakeholders across the healthcare industry—to improve care and outcomes—is central to much of this, and approaches such as concierge medicine, which ensures that patients receive truly individual and attentive care from their providers, also have the potential to make patients healthier. 

AI and Healthcare: A Transformative Impact

Artificial intelligence is a constantly expanding range of applications that are embedding themselves in healthcare. Predictive analytics, for example, uses AI to analyze large, complex sets of data and predict outcomes. Innovations in this technology are driving advances in diagnostics, drug discovery, personalized medicine, and health delivery.

AI technology is being used to help address many critical needs, such as helping clinicians to make sense of the exploding universe of disease information and treatment options. AI-enabled clinical decision support systems (CDSSs) utilize predictive analytics and other tools to sort through and analyze data in electronic health records, clinical knowledge databases, and other sources of information to facilitate personalized diagnosis and treatment. Several leading healthcare and technology companies, including McKesson, Siemens Healthineers, and Philips, now produce CDSSs.

AI-powered tools also enhance the accuracy of diagnostics through an expanding roster of applications, especially in radiology. Of the more than 700 diagnostic algorithms approved by the U.S. Food and Drug Administration (FDA), more than 70% are applications for radiology, according to the KPMG 2025 Healthcare and Life Sciences Investment Outlook. Many of these applications are designed to assist radiologists, helping them read images and detect disease, but as the field expands and deepens, new kinds of devices and applications are being developed.

Handheld point-of-service devices, for example, can help nonspecialists screen patients for serious conditions. DermaSensor, approved by the FDA in January 2024, is AI-powered and uses spectroscopy to examine skin lesions, enabling primary care doctors to evaluate patients quickly and refer suspected skin cancer cases to dermatologists.

Another recently approved product, TumorSight Viz, from SimBioSys, transforms two-dimensional breast MRI images into detailed, interactive 3D digital twins that surgeons can use to plan breast-conserving surgery. It’s an example of spatial biophysics, an emerging discipline that attempts to capture the physics, chemistry and biology of a tumor at the tissue and cellular levels.

Multiple diagnostics companies, meanwhile, are using AI to create dozens of new algorithms for detecting breast cancer, non-small-cell lung cancer, and other cancers.

Healthcare is also moving rapidly toward the ideal of personalized or precision medicine, which assesses each patient’s needs on a molecular or genetic basis and then delivers treatment precisely tailored to those needs. In 2024, the FDA approved 48 new drugs or indications for precision medicine, compared with just 28 the year before. Here, too, AI is accelerating progress. At each stage of the precision medicine continuum—which progresses from risk assessment, screening, and diagnosis through staging and prognosis, therapy selection, and monitoring—AI can facilitate faster, more accurate results.

In making cancer diagnoses, for example, machine learning and deep learning algorithms can help assess hereditary risk by analyzing volumes of data to identify high-risk genes and stratify patients according to their genetic profiles. CDSS tools then can help to determine the optimal therapy.

The Administrative Side of AI in Medicine

On the administrative side of healthcare, AI is improving efficiency across operations, automating everything from medical coding and billing and insurance processing to patient outreach and scheduling. AI-enabled automation can take over many repetitive, time-consuming tasks such as data entry, appointment scheduling, and other routine back-office functions. In human resources, AI applications can help recruit and screen job candidates, and chatbots can handle routine employee inquiries and requests. In all of these cases, automated solutions free people to take on more important roles—including, in many cases, monitoring and confirming AI-enabled processes.

At Kaiser Permanente facilities, ambient listening technology, an AI-supported application that transcribes and summarizes conversations between clinicians and patients, relieves providers and support staff of the laborious work of documenting what occurs during patient visits. Studies have shown that clinical documentation often takes as much time as patient care itself. In implementing this AI application, Kaiser Permanente took several precautions to ensure accuracy, data privacy, and ethical use. Patients must give their consent, and multiple quality checks are in place.

AI and Data Analytics

Leveraging AI for data-driven decision-making is enhancing operational efficiency and patient care. This will be a key driver of modernizing the cost structure of healthcare and delivering an efficient model of care to patients. AI-enabled predictive analytics is also an increasingly popular field for investment, with an estimated global market in 2023 of $14.58 billion and a predicted compound annual growth rate of 24% through 2030.

AI and Revenue Cycle Management

One area where automation and AI are making a difference for healthcare organizations is in revenue cycle management (RCM). In today’s dynamic landscape, payment processes and collections are becoming increasingly complex, driven by factors ranging from the digitization of remittances to changing patient expectations. Banks play a vital role in helping healthcare providers innovate responsibly, balancing the drive for efficiency with regulatory compliance.

While digitization is a critical step forward, providers must focus on end-to-end automation to truly streamline their payment processes. A frequent pain point is in the reconciliation of an electronic remittance advice with electronic funds transfers—and making sure this information can be incorporated into electronic medical records and general ledgers.

The fragmentation of these systems continues to create inefficiencies, slowing down the payment cycle and adding operational burdens. To meet this challenge, Big Data Healthcare, an indirect wholly owned subsidiary of Fifth Third Bank, created an advanced analytics and AI solution that streamlines the reconciliation process. (See Easing Payment Friction Between Healthcare Providers and Insurers.)

Quality of care is the number one priority for patients, but they are also beginning to select doctors and practices based on the ease of doing business with them, the ease of making payments, and clear, concise billing. In one recent survey, 77% of patients said that the ability to make and receive digital payments would positively affect their relationship with their healthcare provider. That reflects growing expectations among patients for a seamless, digitized, “Amazon-like” experience when it comes to healthcare payments, including flexible payment options and streamlined digital interfaces.

Looking ahead, several emerging technologies and trends will continue to shape the future of healthcare payments. Key among these is the shift from fee-for-service to value-based care, which emphasizes quality of care over the volume of services provided. This transition will require new payment models that support quality metrics and outcomes.

The growing adoption of digital payments, including mobile wallets and contactless payments, is another trend that is making healthcare transactions faster and more convenient for patients. (See Fifth Third’s insights in The Benefits of Digital Payments Solutions for Healthcare Practitioners.)

AI applications could facilitate the shift toward fully digitalized RCM. In a recent report, consultants at McKinsey cite the capabilities of data analytics and generative AI in improving RCM. Yet while the potential impact is large, according to the report, success within an organization will require buy-in from leaders, a long-term vision, a clear path from pilot programs to full implementation, and a talent strategy that ensures that changes are led and supported by teams experienced in business, technology, and healthcare.

The future of AI in healthcare, particularly in payments, will be dynamic. The institutions that embrace technology-driven solutions will emerge as key players in the evolving healthcare ecosystem.

New Revenue Streams

The rise of telemedicine and remote patient monitoring offers added flexibility for clinicians and patients, while also creating new revenue streams. In one example of how this technology can be used, Cleveland Clinic is partnering with technology company Masimo to incorporate the Masimo Hospital Automation platform into a system for remote patient monitoring and virtual critical care. The system includes wearable sensors and other monitoring technology that helps providers spot changes in remote patients’ conditions and detect adverse events.

Healthcare Challenges Are Still Ahead

As technology plays an increasingly pivotal role in managing many processes, healthcare organizations will face multiple difficult issues, including:

• Data security and privacy. Protecting patient data from breaches and ensuring compliance with regulations such as the Health Insurance Portability and Accountability Act remains a top concern. AI’s use of protected health information can exacerbate the problem.
• Workforce shortages. There is a growing need for skilled healthcare professionals, and addressing this shortage is critical. In particular, there continue to be insufficient numbers of nurses, physical therapists, and home care providers. According to a survey by consultants Deloitte, 58% of health system executives expect talent shortages, retention issues, and the need for upskilling to affect their organizations in 2025.
• Cost management. Several factors, including an aging population, investments in new technology, labor shortages, and increasing prescription drug costs, are pushing the cost of healthcare sharply higher. Balancing those rising costs with the need to provide affordable care is an ongoing challenge.
• Regulatory compliance. Navigating the complex regulatory landscape and adapting to new policies can be difficult. Organizations in 2025 will need to address several emerging issues, including interoperability, which is the sharing of siloed healthcare information, as well as prior authorization processes, data privacy and AI, and transparency and enrollment regulation.

Fifth Third’s Healthcare team can help organizations invest in new technology and increase operational efficiency. Learn more.