As an industry with a great need for more information, greater accuracy, and optimized time management, healthcare sees promising potential in artificial intelligence. With the market size of healthcare AI expected to increase to over $180 billion by 2030, the technology has seen considerable investment from healthcare organizations. However, large investments don’t always guarantee proportionally large results – not without the proper knowledge and understanding of the adoption process.
What should adopters know about? How can they pinpoint the true potential of a technology and integrate it in a way that works? This article provides a detailed exploration of these questions.
What is AI in Healthcare?
One important thing to remember about AI technology in healthcare is that it’s still an emerging stage. Therefore, executives who expect to get a set of verified use cases before they make a decision will find this task quite difficult. There are many nuances, details, and even flaws to keep in mind: in particular, it should be acknowledged that the use of machine learning, LLM, and GenAI in healthcare will always be tailored to the specific needs and pain points of a healthcare organization, its structure, workflows, and processes. Accordingly, AI in healthcare is a matter of how well healthcare providers understand their clinicians, current issues and many other factors impacting performance.
How can AI help in Healthcare?
Healthcare is as massive and fundamental as it is strained and risk-prone because it has people’s lives at stake. Rapid improvement of services, treatment techniques, technologies, and quality of care is often a must in the context of growing population rates and emerging new threats to global health.
Around 96% of executives trust in AI implementation in healthcare, believing that the technology will allow them to accelerate the process of improvement discovery and integration.
To evaluate whether AI can help them meet such a goal, it makes sense to look at two healthcare directions that have demonstrated the most promising applications of AI: medical diagnosis and patient care.
AI in medical diagnosis
The diagnostic process is complicated and heavily rooted in factors such as physicians’ competence and access to high-quality technology. Therefore, it is highly prone to errors.
- Nearly 16% of preventable harm is caused by misdiagnosis across the world.
- 80% of harm is caused by delayed diagnosis globally
- 1 in 14 hospital patients suffers from harmful diagnostic errors regularly
- The cost of harm due to misdiagnosis costs the U.S. healthcare system over $100 billion each year
The most challenging part is that diagnostic errors can occur at any stage of the diagnostic procedure and for a variety of reasons.
Therefore, the task of reducing and preventing diagnostic errors involves multiple aspects – from technological to organizational. AI technology in healthcare has robust potential to improve the former by injecting more precision and accuracy into diagnostic procedures and approaches via computer vision.
Radiology
The rapidly escalating demand for imaging services, coupled with reduced capacity, has become one of the top challenges in radiology practice. This development is caused by an overreliance on imaging as the best way to secure diagnostic certainty and reduced confidence in other types of assessment. The issue is further exacerbated by the amount of unnecessary imaging, often ordered by nonphysician practitioners or by clinicians who want to reduce the risk of malpractice accusations.
Aside from straining resources and consuming time, overutilized imaging also causes a drop in quality, delivering incorrect results that lead to overdiagnosis, financial damage, and even endanger patients’ health.
To solve this problem, healthcare facilities are recommended to reduce imaging utilization by introducing stricter guidelines and leveraging evidence-based data. Computer vision plays a particularly significant role in the latter, reducing labor work and injecting greater accuracy into the imaging process through precise pathology classification and detection of hidden injuries.
Ophthalmology
As diabetic retinopathy and cataract cases are expected to increase by 72% and 87% respectively by 2050, early diagnosis of eye diseases remains a key priority in ophthalmology. Early detection of emerging vision problems means faster and easier treatment and higher chances of patients retaining their eyesight.
However, since the first stages of eye diseases are usually asymptomatic, traditional detection methods don’t always provide accurate diagnoses and can be time-consuming; for instance, visual field testing takes 15-30 minutes per eye. Furthermore, treating eye conditions such as glaucoma and cataracts in their later stages often involves surgery and other invasive procedures.
Leveraging AI computer vision for diagnosing retinal diseases can considerably facilitate the work of ophthalmologists by reducing the number of complex and invasive manual examinations while maximizing the use of available data.
Studies leveraging glaucoma screening models and over 117,500 images from more than 20 databases across 21 countries have shown high performance of AI models, with 96% accuracy. Such results imply the potential for reducing the time needed to detect visual anomalies and pinpoint key indicators of a disease in its earliest stages. Therefore, it can be safely concluded that AI models have the robust potential for improving the quality of ophthalmology treatment, helping professionals prevent vision loss in their patients, and minimizing treatment costs.
Histopathology
While essential for the early detection of pathological diseases, histopathology still mostly consists of procedures that are performed manually and, thus, can deliver outcomes affected by human bias. The process of acquiring specimens lasts from 20 to 30 minutes, while the waiting time for results can range from 5 to 10 working days to 3 weeks. Given that histopathology suffers from the same issues as other healthcare areas (workforce shortages, growing numbers of patients, and testing requests), there is a need for introducing faster practices that require fewer steps without sacrificing accuracy.
Introducing computer vision into histopathology makes such transformation possible as it allows for the automation of specimen collection and analysis. Additionally, leveraging deep learning and machine learning for virtual histology creates safer work conditions for clinicians and histology lab workers by replacing the use of toxic chemical reagents used for specimen staining with virtual staining – the artificial generation of histological stains with the help of deep learning neural networks and curated data.
Computer vision is currently a very viable and actionable application of AI in healthcare industry. It can perform exceptionally well because there is already a lot of data ready for training. Hospitals have archives of MRI scans, X-ray images, and screening results. Using both public databases and hospital databases makes it possible to create highly efficient and accurate models that can provide results and diagnose patients in seconds after the screening is done.
AI in Patient Care
Regardless of the industry they interact with, people have the same expectations for the quality and responsiveness of services. When it comes to such a vital and fundamental sector as healthcare, timely reactions to patient needs, prompt schedule management, and instant communication between teams make a world of difference for both patients and healthcare workers who rely on reliable planning and streamlined routines.
However, what stops healthcare facilities from meeting such expectations? First and foremost, it’s the persistent shortage of healthcare workforce.
- 900,000 of U.S. nurses intend to leave their workplace by 2027
- 60% of physicians plan to leave medical practice entirely
- 64% of healthcare workers admit to being overwhelmed by their tasks and responsibilities
- 30.2% of new hires were reported to leave within a year
Many of our Healthcare clients come to us with the same pain point: a lack of personnel, such as nurses, physicians, and clinicians. This means that their clinicians lack administrative support to handle tasks like patient record management, scheduling or rescheduling appointments, and matching clinicians with patients based on the patients' needs and specifics.
Nursing staff and hospital staff, such as receptionists and administrative workers, are the pillars of well-timed and synergized Patient Care. When these pillars are missing, it leads to human resources and clinicians’ capacity being stretched thin, resulting in the following issues:
- Increased waiting times for patients
Patients admitted to Massachusetts General Hospital in Boston in September 2023 had to spend at least 14 hours in the ER on average – and for 26% of patients, the waiting period was 24 hours. Considering that around 20% of ER patients are senior citizens over 65, not having access to hospital care for such long periods creates additional risks to their health.
The situation isn’t any better in private clinics. For instance, in one private medical facility, it was impossible to make an urgent appointment: there was always a two-week wait, regardless of the situation. The shortage of workforce and busy clinicians' schedules were the key culprits, but such an approach wasn’t entirely effective since there was no agility or responsiveness in Patient Care.
- Poor clinician time management
The shortage of physicians and medical professionals means that there are too few available doctors to meet the increased patient demand. As a result, hospitals need to manage their planning very responsibly and be extremely proactive in engaging patients and overseeing their journey.
Most administrative records are still managed manually. In the context of a missing healthcare workforce, this becomes a problem rather quickly. For example, a hospital has only one dentist. That dentist has several appointments for the day, but some patients become no-shows, and the doctor isn’t informed in time because the receptionist is overwhelmed by numerous other tasks and unable to contact the patients or warn the doctor. As a result, the dentist loses time and the opportunity to work on other patients, while the hospital loses money.
- Reduced overall quality of care
Where there is a lack of receptionists, nurses, and physicians to admit, process, and timely treat patients, there is an increased danger of medical errors and underdiagnosis. The problems start right at the admission level, especially in regions as diverse as the U.S.: patients get connected with the wrong experts or their language preferences aren’t acknowledged, which creates communication barriers and prevents them from giving the full picture of their symptoms. As a result, patients with serious medical issues can slip through the cracks and end up with their conditions worsening due to the lack of timely treatment.
How can the use of AI healthcare address such Patient Care struggles?
Intelligent apps for dynamic patient management
Physicians and nurses rely on administrators in their routines to inform them about planned appointments and provide data on patients. Similarly, they communicate their updates on patients or any issues they come across to administrators.
When there are enough staff to handle and process feedback, the general workflow is usually smooth. However, too few administrators with too many administrative burdens lead to slow response times and blind spots, causing healthcare workers to miss their appointments and fail to cover their objectives for the day.
The use of AI in healthcare allows for removing administrators from the equation and giving nurses and clinicians more control over their patient management and planning through intelligent apps that provide them with multiple informative features:
Route planning
Real-time mapping for home healthcare nurses that lets them choose the fastest route to their next patient.
Smart scheduling
Organizing patient visits by priority, providing alerts, notifications, and fast schedule updates.
Reporting
Generating report on patient health and adding it to healthcare system.
Communication
Keeping patient automatically updated on clinician/nurse availability status.
Patient interactions don’t follow any templates and frequently go beyond the established appointment length. Some patients, especially seniors, are more eager to talk to doctors and nurses, sharing their feedback and asking for additional recommendations. Such situations are particularly common for home care nurses who care for many patients within one day. Ignoring patients isn’t an option, but neither is favoring one patient over others. The solution is to make patient management more flexible and gain more information for better planning – and AI applications in healthcare can make this possible.”
Healthcare support bots
Similarly to equipping physicians and nurses with digital means for keeping track of their progress, patients can be kept in the loop with the help of virtual AI assistants. The use of intelligent bots allows hospitals to promptly gather patient feedback, descriptions of symptoms, and information about their needs and preferences – and effectively connect the patients with necessary professionals. In the area of telehealth and telemedicine, virtual AI assistants have become particularly valuable, serving as a bridge between patients and physicians.
Answering basic questions
Providing detailed information on the most frequently asked questions about treatment, disease or medicine.
Medication tracking
Automated reminders for patients to take their medicine, complete with automated status updates for hospitals.
Symptom-checking
Surveying patients on the frequency or changes in their symptoms to determine the efficiency of treatment and gather necessary details.
Appointment scheduling
Sending appointment booking requests and booking time slots in accordance with urgency and physician availability.
No-show prevention
Reminding patients about the booked appointments, getting their confirmation or providing options for re-scheduling.
What is even more important, healthcare support bots can make a significant difference in shared decision-making: physicians and patients working together to choose the most fitting treatment.
Aside from 71% of patients generally preferring the shared decision-making approach, physicians and clinicians also acknowledge the value of such practice, citing increased patient satisfaction, enhanced health awareness, and faster communication as advantages.
With the help of smart AI healthcare bots, patients can forward their suggestions and updates on their symptoms directly to their physicians – all their feedback will be converted into comprehensive and easy-to-read reports, enabling greater clarity
An important note: artificial intelligence isn’t a direct solution for the workforce shortage. The experience and skills of clinicians, nurses, and physicians are priceless and irreplaceable, so hospitals need to work on ways to improve retention and engage experts. The role of AI in healthcare is not to replace the missing workforce, but to aid the present workforce with their tasks, ensuring that patients are processed in a timely manner and connected with relevant experts.”
Easier Electronic Health Record (EHR) management
A healthcare system isn’t a single body: it’s a multitude of independently functioning parts, including hospitals, primary care physicians, clinics, and specialists. Accordingly, each of these parts has different EHR systems that follow individual procedures and standards. The issues begin when a patient interacts with several different providers.
Due to the lack of synergy between healthcare components and no single standard or format for data, valuable information ends up missing or overlooked, and the overall coordination suffers. The issue of fragmented systems (also known as fragmented care) goes beyond patient management, affecting the way healthcare workers and executives make decisions in general:
- Less than 50% primary care physicians are aware of clinics making changes to their patients’ treatment programs and medication plans.
- Half of 50 healthcare organizations planned to increase their interoperability spending to 20% in 2023.
- Only 7% of healthcare providers are confident about their EHR records having all necessary patient data.
- Healthcare organizations leverage only 57% of their data for decision-making.
Interoperability has been at the top of U.S. healthcare challenges for at least 30 years. There have been multiple attempts to unify formats and standardize data for all EHR systems across various components of healthcare systems. However, these attempts have led to the creation of several different interoperability solutions, which doesn’t exactly resolve the issue.
Since data management and organization are among the strongest AI capabilities, healthcare executives see AI and GenAI in healthcare as the key to securing data interoperability.
Automated EHR management
Congregating all data related to specific patient from diagnostic labs, imaging services, and other healthcare organizations in a single electric health record.
Patient visit recording
Responding to trigger keywords to transcribe conversations between doctor and patient and summarizing the visit for the health record.
Translating unstructured data
Extracting unstructured data and converting into unified format automatically.
Improved billing
Identifying billing information for detailed eligibility checks and insurance verification.
The use of AI in Healthcare for record management can accelerate standard care delivery protocols. Whenever a patient comes to the hospital with health complaints, physicians have a set of steps in place to diagnose the reason for the complaints and provide adequate help. By pulling up full data from EHR records and identifying matching symptoms, AI solutions offer a more time-effective alternative, with fewer steps and more focus on what’s important.”
Future of AI in Healthcare: is it here yet?
Given the ample improvement opportunities offered by AI in healthcare and the seemingly transparent steps to implementation, the path seems clear and the general mood is optimistic.
According to the Future Health Index 2024 report:
- 85% of healthcare leaders are committed to exploring the role of AI in healthcare, planning to invest or already investing in GenAI in the next years.
- 43% of decision-makers are in the process of using AI for patient monitoring.
- 78% of health executives report utilizing AI for revenue cycle management.
Nevertheless, the previous article on the future of AI in healthcare established that it’s too early to speak about artificial intelligence as an integral and day-to-day part of healthcare. An in-depth look into research and surveys shows that the status of AI in healthcare suggests that this statement still holds true.
For example, the 2024 EY CIO Sentiment Survey revealed that only 13% of healthcare CIOs have specific and fully fleshed-out plans for implementing GenAI in their workflows – even though 49% of CIOs consider the technology to be an important tool for boosting value and doubling ROI.
As a result, the state of AI in healthcare remains controversial – there is a lot of enthusiasm and willingness to embed the technology, but there are also barriers holding back adoption.
Persistent risk of AI hallucinations
The issue of AI hallucinations and errors in healthcare is as relevant as in any other industry and is not to be taken lightly.
A study by the University of Massachusetts Amherst revealed 327 medical event inconsistencies and around 114 cases of incorrect reasoning across 50 summaries generated by ChatGPT-4.0.
Another study by teams from the University of Washington and Cornell University discovered that Whisper, the OpenAI tool used for medical transcription, was particularly prone to hallucinations, adding nonsensical phrases, fabricated sentences, or even offensive remarks to 1.4% of its texts. Following this discovery, researchers concluded that 40% of the tool’s hallucinations may have potentially caused harm by distorting the speaker’s intent.
The reason for AI hallucinations is rather simple: lack of diverse training data. It’s possible to train a functional AI data model on one data set. However, when such a model starts encountering queries and scenarios that go beyond its knowledge base, it will start “hallucinating” in its attempt to provide a response despite the lack of necessary data.
We hear about AI supremacy all the time, but these statements are far from reality. AI should be viewed as a helper that organizes curated data and makes it more accessible. Therefore, its responses and summaries should be reviewed by professionals, especially the experts using the tool for work.
Unclear ROI metric tracking
Although 60% of healthcare executives who implemented GenAI state they’re seeing positive results, very few of them have actually quantified the impact. Such a conclusion outlines a general issue with adopting AI in healthcare – the traditional mechanisms for tracking and predicting ROI prove to be inefficient, leaving hopeful adopters in the dark about the outcomes. In order to map potential milestones and gain some visibility in the journey, adopters encounter several questions that lead to even greater confusion.
In many cases, it’s difficult to assess who actually benefits from AI adoption: for example, C-level executives may want to implement AI for accelerating paperwork processing, but physicians may respond with resistance if the technology disturbs their workflow and approach.
In addition, executives usually don’t have full information about the time physicians spend on tasks other than treating patients. How is it possible to evaluate performance before and after AI without such knowledge?
Fragmented healthcare data doesn’t make the adopters’ task any easier – with much valuable information and many metrics lost in data silos, executives don’t have all the details they need to draw a conclusion.
Even though organizations usually introduce innovations to improve existing processes, AI in healthcare sometimes doesn’t change workflows – it creates entirely new ones instead. As a result, establishing ROI indicators can be more challenging than healthcare executives expect.
Unregulated AI meets regulated healthcare
Healthcare is a heavily regulated industry with an intense focus on data security, which is often reflected in healthcare organizations’ approach to digital transformation. As a rule, any digital transformation partner cooperating with a healthcare facility takes extra measures to ensure the confidentiality and safety of Protected Health Information. The traditional framework includes:
- Establishing boundaries
Custom software development companies sign an additional agreement that regulates what kind of patient data the contractor has access to and how the contractor must process it. The agreement also contains additional clauses on non-disclosure that the contractor must adhere to after the work is over - Obligatory HIPAA awareness training
Contractor teams involved in working on a healthcare digital product must undergo a HIPAA awareness training to prove their knowledge of the latest data security guidelines, the impact and consequences of data breach, and all relevant safety techniques. - Legal consulting
From their side, digital partners consult with legal teams, analyzing contracts to identify potential pitfalls and ensure there will be no issues with the tools used during the project.
Even with such protocols in place, healthcare organizations remain extremely cautious about adopting innovations, especially when it concerns PoC solutions. In many cases, the risk and potential outcome of a data breach are not worth the opportunity, given that those responsible for data leaks often face an investigation by the Department of Health.
- 80% healthcare providers name data safety, accuracy, and compliance with HIPAA guidelines their primary concern
- 95% of all identity thefts are committed through stolen patient health records
- 8.8% of data breaches occurred due to accidental disclosure in 2025
In this context, AI and GenAI are entirely new and unpredictable variables to healthcare executives, as there are no clear and globally established regulations on using AI in the healthcare industry.
For example, only 15.2% of countries have implemented legislation regarding artificial intelligence, while 47.2% of countries have yet to introduce any regulations. The U.S. still hasn’t introduced any regulations on the development or use of AI, which leaves most solutions self-regulated. In healthcare, this leads to an accountability vacuum. It’s unclear who is to be held responsible for an integrated AI system causing a data leak or resulting in a medical error – and such vagueness makes it harder to mitigate potential risks.
Since OpenAI introduced ChatGPT in autumn 2022, the pace of technology accelerated rapidly. We take our own precautions when working on projects involving AI by consulting with legal teams and going through our agreements to avoid any potential pitfalls to using AI and AI-powered tools. It’s a very meticulous, yet necessary process to ensure the best outcomes for us and our customers.
The compliance issues leave healthcare executives stuck between a rock and a hard place. On the one hand, they want to harness the capabilities of these technologies and apply them to areas of improvement. On the other hand, they’re held back by the lack of regulations synergizing solutions between healthcare systems and the government. In many cases, promising innovations are already designed and developed – but never get an opportunity to be launched.
Many AI solutions designed to tackle fragmented care have already been developed. However, we don’t see any improvement at healthcare facilities because these solutions haven’t been approved by state regulations. As a result, they remain inactive and non-operational.
Unlocking the benefits of AI in Healthcare: how to get adoption right?
With all the promise and challenges of adopting AI in healthcare outlined, one question remains: Is it possible to implement the technology and avoid complications?
In my opinion, it will take 5 to 10 years before we see AI fully implemented and functional in healthcare. Right now, it’s limited by vague compliance regulations and a lack of guidelines on addressing issues such as hallucinations. However, this doesn’t mean that healthcare organizations shouldn’t explore the benefits of AI today. With the right approach and knowledge of how to get started, it’s possible to make the most of the technology.
- Transforming unregulated operations
Patient scheduling, home care, and clinical visits are among the routines that don’t involve state regulations, making them the perfect starting point for AI adoption and exploring the potential value of the technology. Such processes also come with clear and easy-to-track metrics such as patient visits, no-show rates, and stabilized scheduling, allowing executives and decision-makers to calculate their ROI without complications. - Focusing on numbers
Regarding ROI calculations, decision-makers should start their preparations for AI adoption by dissecting their organizational processes and analyzing every detail. For instance, they need to know how much time it takes for their clinicians to process patients and how many hours clinicians spend on additional tasks. Doing so will reveal potential areas for improvement and provide a better understanding of ROI expectations.
Fun fact: Even OpenAI doesn’t have metrics for tracking their product efficiency. Instead, they rely on surveys to evaluate their product performance. This is not the most effective approach as it’s time-consuming and relies entirely on a third party’s perspective. A better course of action would be to gain more knowledge of your operations and processes, both from your EHR systems and by gathering feedback from your teams. You can even use predictive AI analytics to compare processes before and after the potential change to get a more accurate picture and plan different improvement scenarios.”
- Establishing safety nets
Remembering that AI is a new and evolving tool, not a fully developed digital decision-maker, is essential for safe and effective technology use. Adopters need to proactively prevent AI hallucinations and biases, as well as seek legal advice on handling potential negative scenarios and minimizing risks. Although these requirements may sound overwhelming, they are necessary for the safe use of AI in healthcare and to prevent potential reputational and financial harms.
Establishing an AI TRiSM framework
Working with legal teams on creating guidelines and practices for AI Transparency, Risk and Security Management.
Using AI agents
Safeguarding against AI hallucinations by leveraging AI agents for checking AI-generated reports and detecting any bits of text that match hallucination qualifiers.
Training employees
Pre-emptively preparing healthcare workers to operate AI tools, gathering their feedback and adjusting their experience.
Getting industry expertise
Collaborating with AI talents experienced with implementing AI in healthcare to get accurate assessment and calculate ROI expectations.
Healthcare should always be about people: those who need care and those who provide it. Regardless of the innovations you adopt, you should always maintain a human-centric approach. This is how you achieve clear results and honest feedback.
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