Top 12 ways artificial intelligence will impact healthcare


Data have become increasingly valuable across industries as technologies like the Internet and smartphones have become commonplace. These data can be used to understand users, build business strategies and deliver services more efficiently.

However, healthcare data are some of the most precious — and most targeted — sources of information in the digital age. When used by health systems, providers and patients, these data can help significantly improve care delivery and outcomes, especially when incorporated into advanced analytics tools like artificial intelligence (AI).

Healthcare AI has generated major attention in recent years, but understanding the basics of these technologies, their pros and cons, and how they shape the healthcare industry is vital.

This list details — in alphabetical order — the top 12 ways that AI has and will continue to impact healthcare.


At their core, clinical decision support (CDS) systems are critical tools designed to improve care quality and patient safety. But as technologies like AI and machine learning (ML) advance, they are transforming the clinical decision-making process.

In the early days of CDS tools, many were standalone solutions that were not well-integrated into clinical workflows. Today, many CDS systems are integrated into electronic health records (EHRs) to help improve deployment and gain more value from the use of these tools at the bedside.

AI takes this one step further by enabling providers to take advantage of information within the EHR and data pulled from outside of it. Because AI tools can process larger amounts of data more efficiently than other tools while allowing stakeholders to pull fine-grained insights, they have significant potential to transform clinical decision-making.

By utilizing AI’s advanced pattern recognition capabilities, CDS tools can incorporate risk stratification and predictive analytics, allowing them to help clinicians make more informed, personalized treatment recommendations in high-value use cases like chronic disease management.

Some providers have already seen success using AI-enabled CDS tools in the clinical setting.

Last year, experts from the Stony Brook Cerebrovascular and Comprehensive Stroke Center and Mayo Clinic sat down with HealthITAnalytics to discuss how Rapid Aneurysm, an AI-based volumetric measurement tool, can help augment clinical decision-making.

They noted that the tool — used to study aneurysms that ruptured during conservative management — could accurately identify aneurysm enlargement not flagged by standard methods. The potentially life-threatening nature of aneurysm rupture makes effective monitoring and growth tracking vital, but current tools are limited.

Rapid Aneurysm allows clinicians to create 3D models, providing aneurysm measurement tools that extend beyond traditional linear measurements, which gives a more complete picture of a patient’s rupture risk to inform clinical decision-making.

AI’s ability to capture insights that elude traditional tools is also useful outside the clinical setting, such as drug development.


Drug discovery, development and manufacturing have created new treatment options for a variety of health conditions. Integrating AI and other technologies into these processes will continue revolutionizing the pharmaceutical industry.

High drug development costs and other challenges are driving clinical researchers to seek out new tools to get new drugs to market more efficiently. The process is often high risk, high reward: the drug development lifecycle takes billions of dollars and decades of research, but new medicines are not guaranteed to receive regulatory approval from the United States Food and Drug Administration (FDA).

AI and other technologies can help overcome major drug discovery and development barriers.

AI and ML, in particular, are revolutionizing drug manufacturing by enhancing process optimization, predictive maintenance and quality control while flagging data patterns a human might miss, improving efficiency.

These tools are also useful in the data-gathering systems for complex drug manufacturing, and models to identify novel drug targets are reducing the time and resource investment required for drug discovery.

In June 2023, research published in Science Advances demonstrated the potential for AI-enabled drug discovery. The study authors found that a generative AI model could successfully design novel molecules to block SARS-CoV-2, the virus that causes COVID-19.

The tool effectively identified drug-like molecules that would bind with two COVID-19 protein targets, which are responsible for transmitting the virus to the host cell and helping to spread the virus.

The capability of AI models to sift through vast datasets is key to unlocking advances not only in drug development but also for deriving insights from another wealth of information: electronic health records (EHRs).


EHRs hold vast quantities of information about a patient’s health and well-being in structured and unstructured formats. These data are valuable for clinicians, but making them accessible and actionable has challenged health systems.

AI has given healthcare organizations a unique opportunity to overcome some of these hurdles, and some already see the benefits.

EHR adoption aims to streamline clinical workflows while bolstering cost-effective care delivery, but instead, clinicians are citing clinical documentation and administrative tasks as sources of EHR burden and burnout.

AI tools are key to addressing these issues and giving providers back their time so that they can focus on patients. There are multiple AI use cases to tackle clinician burnout, most of which aim to automate aspects of the EHR workflow.  

Health data extraction solutions can help clinicians find the information they’re looking for quickly and effectively, reducing information overload. Many of these tools leverage natural language processing (NLP), an AI approach that enables algorithms to flag key components of human language and use those insights to parse through text data to extract meaning.

AI is also useful when healthcare organizations move to new EHR platforms and must undertake legacy data conversion. This process often reveals that patient records are missing, incomplete or inconsistent, which can create significant inefficiencies.

Typically, inconsistencies pulled from a medical record require data translation to convert the information into the ‘language’ of the EHR. The process usually requires humans to manually translate the data, which is not only time-consuming and labor-intensive but can also introduce new errors that could threaten patient safety.

AI-based tools can automate this process, saving time and effort for care teams.

Advanced analytics solutions are also critical for effectively utilizing newer types of patient data, such as insights from genetic testing.


Genomics has sparked a wealth of excitement across the healthcare and life sciences industries. Genetic data allows researchers and clinicians to gain a better understanding of what drives patient outcomes, potentially improving care.

Particularly, genomics plays a key role in precision and personalized medicine, but making these insights useful requires analyzing large, complex datasets.

By enabling providers to combine the power of genomics and big data analytics, AI models can tailor care and treatment recommendations for various medical conditions. These tools are invaluable for overcoming a significant obstacle to using genomics in clinical settings: the actionability of the data.

Access to a patient’s genome sequence data sounds promising, as genetic information is relevant to identifying potential health concerns, such as hereditary disease. However, to truly transform care delivery, providers need to know more than just what the data says about a patient’s genetic makeup; they also need to be able to determine how that information can be used in the real world.

One approach to achieve this involves integrating genomic data into EHRs, which can help providers access and evaluate a more complete picture of a patient’s health. But AI can take this further.

In a July 2023 study published in Med, a research team from Harvard Medical School detailed how an AI tool could successfully conduct real-time genomic profiling of gliomas — a common but aggressive type of brain tumor — during tumor removal surgery.

This method could determine the molecular identity of a tumor, information that surgeons could use to decide how much brain tissue should be removed, and whether or not tumor-killing drugs should be placed in the brain while the patient is on the operating table.

Using current methods, this information can take days or weeks to receive, highlighting the potential of AI to improve patient outcomes and make care more efficient.

But the efficiencies aren’t limited to when a patient is actively receiving care. AI technologies also have utility when applied to hospital operations more broadly.


Managing health system operations and revenue cycle concerns are at the heart of how healthcare is delivered in the US. Optimizing workflows and monitoring capacity can have major implications for a healthcare organization’s bottom line and its ability to provide high-quality care.

However, monitoring and managing all the resources required is no small undertaking, and health systems are increasingly looking to data analytics solutions like AI to help.

Capacity management is a significant challenge for health systems, as issues like ongoing staffing shortages and the COVID-19 pandemic can exacerbate existing hospital management challenges like surgical scheduling.

Addressing these challenges requires health systems to juggle staffing restrictions with surgeon preferences, which data analytics and AI can help with.

In a 2022 interview with HealthITAnalytics, leadership from Community Health Network and Baylor Scott & White Health shared how AI-enabled operating room scheduling tools have transformed each organization’s capacity management approach.

Variations in surgeons’ operating room (OR) usage or scheduling preferences often lead to inefficiencies, such as equipment sitting idle, ORs being unused when they’re available, and surgeons being unable to get OR block time.

To tackle this, both health systems have implemented a cloud-based capacity management platform to support scheduling optimization. The tool uses data on surgery type, length and other information to help staff streamline OR scheduling, which has led to improvements in primetime OR utilization and proactively released OR time.

AI and ML technologies have also enhanced aspects of medical imaging.


Medical imaging is critical in diagnostics and pathology, but effectively interpreting these images requires significant clinical expertise and experience. Imaging analytics, often driven by AI, aims to tackle this.

AI technologies are already changing medical imaging by enhancing screening, risk assessment and precision medicine.

A March 2024 study published by Johns Hopkins researchers in Communications Medicine showed that a deep neural network-based automated detection tool could assist emergency room clinicians in diagnosing COVID-19 by analyzing lung ultrasound images.

The tool is designed to identify B-lines — bright, vertical image abnormalities that indicate inflammation in patients with pulmonary complications — to diagnose COVID-19 infection with a high degree of accuracy.

The model’s success suggests that a similar approach could be applied to other serious conditions, like heart failure, to diagnose patients efficiently at the point of care.

The researchers emphasized that such a capability would be extremely useful in scenarios where emergency department clinicians face high caseloads, such as the early days of the COVID-19 pandemic, or for integration into wearable technologies and other wireless devices for enhanced remote patient monitoring.

In addition to helping monitor a patient’s status and detect potential health concerns earlier, AI technologies can also be deployed in clinical trials and other research.


Medical research is a cornerstone of the healthcare industry, facilitating the development of game-changing treatments and therapies. But this research, particularly clinical trials, requires vast amounts of money, time and resources.

AI tools can be used to streamline data collection and management, break down data silos, optimize trial enrollment and more in medical research.

These technologies are especially valuable for accelerating clinical trials by improving trial design, optimizing eligibility screening and enhancing recruitment workflows. Further, AI models are useful for advancing clinical trial data analysis, as they enable researchers to process extensive datasets, detect patterns, predict results, and propose treatment strategies informed by patient data.

However, before AI can help ease these pain points, it must be integrated effectively. In a November 2023 interview with PharmaNewsIntelligence, leadership from QuartzBio, part of Precision for Medicine, indicated that stakeholders must prioritize privacy, security and model validation to successfully integrate AI into clinical trials.

Outside of the research sphere, AI technologies are also seeing promising applications in patient engagement.


Patient engagement plays a major role in improving health outcomes by enabling patients and their loved ones to be actively involved in care. Often, patient engagement solutions are designed to balance convenience and high-quality interpersonal interaction.

While digital technologies cannot replace the human elements of the patient experience, they have their place in healthcare consumerism. AI, specifically, can be valuable for personalizing patient engagement tools.

Communication is a key aspect of patient experience and activation, and EHRs can help facilitate that communication by allowing patients and providers to send messages to one another anytime. However, overflowing inboxes can contribute to clinician burnout, and some queries can be difficult or time-consuming to address via EHR message.

This creates frustration on both sides, as clinicians want to spend more time on care and less on administrative tasks, while patients want their healthcare to be accessible and frictionless.

AI chatbots are emerging as a potential solution to this conundrum, as they are well-suited to sorting through patient needs and providing resources in certain areas. For example, a health system may deploy a chatbot to help filter patient phone calls, sifting out those that can be easily resolved by providing basic information, such as giving parking information to hospital visitors.

These AI tools can also be applied to clinical needs, using patient symptom data to provide care recommendations.

AI-driven patient engagement can also take the form of solutions designed to conduct patient outreach based on clinical risk assessment data or tools to translate health information for users in a patient portal.

Often, these tools incorporate some level of predictive analytics to inform engagement efforts or generate outputs.


In recent years, the rise of predictive analytics has aided providers in delivering more proactive healthcare to patients. In the era of value-based care, the capability to forecast outcomes is invaluable for developing crucial interventions and guiding clinical decision-making.

To successfully utilize predictive analytics, stakeholders must be able to process vast amounts of high-quality data from multiple sources. For this reason, many predictive modeling tools incorporate AI in some way, and AI-driven predictive analytics technologies have various benefits and high-value use cases.

Predictive analytics enables improved clinical decision support, population health management, and value-based care delivery, and its healthcare applications are continually expanding.

AI-based risk stratification is a crucial component of many of these efforts, as flagging patients at risk for adverse outcomes and preventing those outcomes is integral to advancing high-quality care delivery.

Recent research published in JAMA Psychiatry demonstrated just how valuable these tools can be by detailing the development of an ML-based predictive model capable of accurately stratifying suicide risk among patients scheduled for an intake visit to outpatient mental healthcare.

The researchers underscored that many patients stop mental health treatment following their first or second visit, necessitating improved risk screening to identify those at risk of a suicide attempt. However, the small number of visits that these patients attend leads to limited data being available to inform risk prediction.

The study’s model uses data from mental health intake appointments to forecast the potential for self-harm and suicide in the 90 days following a mental health encounter. The tool could effectively stratify these patients based on suicide risk, leading the research team to conclude that such an approach could be valuable in informing preventive interventions.

In addition to predictive analytics, AI tools have advanced the field of remote patient monitoring.


Remote patient monitoring (RPM) has become more familiar to patients following the COVID-19 pandemic and the resulting rise in telehealth and virtual care. However, RPM technologies present significant opportunities to enhance patient well-being and improve care by allowing providers and researchers to take advantage of additional patient-generated data.

AI can be incorporated into RPM tools or used to streamline the processing of RPM data.

Common RPM tools that take advantage of advanced analytics approaches like AI play a significant role in advancing hospital-at-home programs. These initiatives allow patients to receive care outside the hospital setting, necessitating that clinical decision-making must rely on real-time patient data.

RPM solutions enable continuous and intermittent recording and transmission of these data. Tools like biosensors and wearables are frequently used to help care teams gain insights into a patient’s vital signs or activity levels.

AI bolsters the capabilities of these solutions by helping to predict complications, allowing care teams to preemptively intervene in cases of clinical deterioration, and flagging patients who are likely to benefit from hospital-at-home services compared to inpatient care.

These technologies are also useful because they can “learn” a patient’s baseline biometrics, which can help catch deviations from that baseline and adjust accordingly or alert the care team when a patient is at high risk for an adverse event.

Whether care is happening remotely or in person, AI tools can also streamline revenue cycle management for providers.


Revenue cycle management is crucial to ensuring that health systems can focus on providing high-quality care for patients. However, effectively tackling revenue challenges and optimizing operations requires heavy lifting on the administrative side.

AI tools can help ease these burdens in a variety of ways.

Revenue cycle management still relies heavily on manual processes, but recent trends in AI adoption show that stakeholders are looking at the potential of advanced technologies for automation.

In particular, providers are investigating AI- and automation-based tools to streamline claims management. The claims management process is rife with labor- and resource-intensive tasks, such as managing denials and medical coding. To that end, many in the healthcare space are interested in AI-enabled autonomous coding, patient estimate automation and prior authorization technology.

Healthcare organizations are seeking more information on their return on investment prior to adopting these tools. However, adoption is likely to center on operational optimization, leading to automation tools being deployed in areas with the highest administrative burden, like claims management.

AI technologies can take over mundane, repetitive tasks, such as checking a claim’s status, and enabling the human staff to focus on more complex revenue cycle management objectives.

Some healthcare organizations have already seen success implementing AI-driven revenue cycle tools.

In February, leaders from Mount Sinai detailed how the health system is deploying autonomous medical coding technology. The tool currently codes approximately half of the organization’s pathology cases, but the health system aims to increase this volume to 70 percent over the next year.

Mount Sinai is also exploring how autonomous coding might successfully be applied across other specialties to minimize some of the associated healthcare administration burdens.

AI tools are also useful for streamlining labor-intensive tasks in the clinical setting, as evidenced by the rise of healthcare robotics.


In healthcare, it’s often helpful to have another pair of hands when completing various care-related tasks, from gathering necessary supplies to performing complex surgeries. In the wake of ongoing healthcare workforce shortages, having enough staff to do the critical work of patient care is challenging.

The rise of advanced tools like AI has shown promise in addressing some of the challenges associated with staffing shortages. Still, concerns about bias, health equity and clinician over-reliance on these tools highlight the importance of the human aspect of healthcare.

AI and other healthcare solutions cannot replace humans, but as these tools continue to advance, they are showing increasing promise to help augment the performance of the healthcare workforce.

Robotics is an example that’s generated significant attention recently. To date, many of the applications for healthcare robots are surgical. For example, surgeons can use robotic arms to conduct procedures, allowing for improved dexterity and range of motion.

According to the American College of Surgeons, robotic surgery is used in a host of surgical procedures, including general, gynecology, colorectal and cardiothoracic.

Many health systems that have deployed AI-enabled robotic surgery are seeing benefits to the approach.

Last year, New Jersey-based AtlantiCare implemented pre-operative AI assessment tools and surgical robotics techniques to support early lung cancer diagnosis and treatment.

Under the new workflow, the AI will help care teams flag and monitor patients at risk for lung cancer, facilitating earlier interventions, and those patients who need a biopsy will receive robot-assisted bronchoscopy designed to enhance nodule treatment.

These impacts are just the beginning of how AI is poised to transform the healthcare industry, and many more changes are likely to emerge as these technologies advance to improve care delivery and patient outcomes.


Leave a Reply

Your email address will not be published. Required fields are marked *