Since the acceleration of digital transformation, 92% of medical professionals have reported improved workplace performance after integrating innovation into their routines. Such a result set a firm course for technology as healthcare's key problem solver, risk minimizer, and progress enabler.
The sector has particularly high hopes for AI as private medical institutions already provide the first successful use cases that can be learned from and adopted on a greater scale. We gathered the most relevant insights and use cases to fully outline the potential for artificial intelligence in healthcare and give a glimpse into the near future.
The role of AI in the future of healthcare
Healthcare has always been a very intense and dynamic field—and yet, since 2020, medical professionals, facilities, and organizations remain under particularly extreme pressure, facing the round of global and social factors that can either trigger progress or cause one setback after another. For that reason, the picture of the future of healthcare is made from the challenges the industry is facing in the present.
- Increased employee burnout
According to the Medscape Physician Burnout and Depression report, nearly 73% of physicians experienced a decline in their work-life happiness. Overwhelming bureaucracy, unhealthy working conditions, and poor workplace communication were among the factors contributing to the poor mental state. - Surging hospital mergers
The rise of M&A in healthcare services since Q2 of 2023 has brought good news and frustrating complications. On the one hand, the return to the pre-2020 pace of mergers signifies much-needed evolution and change in hospital structure, practices, and approach. On the other hand, gaps in communication, inefficient risk and documentation management, and employee resistance to change can lead to the deterioration of patient care and drops in productivity. - Low patient satisfaction
Ultimately, the state of the industry is defined by how its target audience perceives it. From that perspective, the views on the global healthcare status remain rather mixed. While around 53% of patients expect to receive the best treatment, 46% of adult patients worldwide admitted to struggling to get access to medical aid in their country. In addition, they named workforce shortage as one of the reasons for their negative experience. - Health workforce crisis
In 2023, 53%ofhealthcare workers complained about overwhelming workloads caused by understaffing and a lack of necessary resources. This issue isn't to be taken lightly, as WHO estimates that low-income and middle-income countries will be at least 10 million healthcare workers short by 2030. Such a shortfall will undoubtedly impact the quality of medical services and the ability to identify, address, and mitigate health threats. - Growing healthcare costs
Population growth and aging are naturally followed by an increase in healthcare expenditures. However, it’s the imbalance between expenses and outcomes that poses an issue for the global healthcare system. For instance, the US spent around $4.5 trillion on healthcare in 2022, yet it has made much slower progress regarding reducing premature death rates, hospital admission rates, and post-operative complication rates.
On a global scale, after reaching their peak in 2023, medical costs are estimated to decline by 9.9%. However, the long-term perspective anticipates the medical trend increase following global conflicts and crises.
Since healthcare workers and facilities are among the world’s first-line responders to global events endangering people’s health and safety, the ongoing instability keeps delivering complications that are too much to handle. However, with AI entering the scene, there is a realistic chance of improving the state of global healthcare and potentially revolutionizing it by introducing better practices, more effective risk prevention protocols, and optimized resource utilization.
There is one thing about AI in healthcare that should be addressed first and foremost: knowledgeable application of the technology. AI makes a solid foundation for tools that can considerably improve the work of healthcare professionals, but only when decision-makers have a detailed understanding of what to improve. So, if you ask yourself, "Can AI fix the issues we're dealing with right now?" you need to communicate with your teams and professionals and identify what’s holding them back and how they see improvement. Based on this feedback, you can outline the exact solution and identify where you need it.
The future of AI in healthcare: emerging trends
Estimated to save about 250,000 lives each year and increase industry revenue up to $110 billion globally, GenAI and other artificial intelligence types became an opportunity to explore and seize for multiple healthcare professionals and organizations.
The peaking interest in AI for healthcare has outlined several noteworthy trends that will define the industry's future.
Administrative tasks management
According to the Nuance research, healthcare professionals must dedicate at least 13.5 hours per week to paperwork. In some cases, the number of hours can reach up to 15 hours per week, while the amount of documentation-related challenges has remained unchanged since 2015. Additionally, healthcare workers admitted not having necessary records 25% of the time they needed to process paperwork, which affected their productivity.
Such complications became one of the key reasons for increased employee burnout rates, prompting multiple medical associations to look for ways of reducing administrative burdens and improving inventory management.
Following the successful cases of introducing AI into hospital systems and enabling medical workers to dedicate twice as much time to patients, the technology's role in transforming administrative healthcare flows became very apparent. The future of AI in healthcare envisions it as a vital element in streamlining numerous repetitive daily tasks while compensating for the workforce shortage.
Data entry
Removing manual data entry by extracting information from relevant sources and automating data input.
Claims processing and billing
Utilizing decision-making agents to perform billing procedures and process claims, reducing human error and improving precision.
Appointment scheduling
Taking over the process of finding and booking accessible time slots and rescheduling appointments.
Patient outreach
Providing patients with preventive care, forwarding personalized instructions and reminders based on professional recommendations.
Communication management
Using intelligent virtual assistants to interact with patients, gather their feedback, verify their appointment dates, and generally oversee their treatment progress.
Drug development
A very complex and time-consuming process, drug development is also notorious for its propensity towards failure. For instance, around 97% of cancer drugs usually fail during clinical trials—and each attempt to introduce a new cure to the market costs more than $1 billion.
However, as new health threats such as viruses and diseases keep emerging, the need to accelerate drug discovery and improve current drugs is greater than ever. Thus, drug development became yet another field where implementing AI can make a great difference. Due to its ability to work with large volumes of information and glean valuable insights from a vast range of databases, AI allowed healthcare organizations to reduce the time needed to collect data and proceed to the next stages of drug discovery.
In addition, GenAI technology enabled innovative de novo drug design methods, using predictive analytics to synthesize molecules from scratch and explore their properties for specific purposes, opening new opportunities in drug optimization and discovery.
Drug candidates identification
Providing a faster approach to discovering ways to tackle untreatable diseases by generating molecules and exploring their effect on certain conditions.
Drug component modification
Optimizing the structure of a drug candidate by modifying the attributes of the molecules for maximum treatment efficiency and safety.
Drug repurpose
Exploring the potential of existing drugs for treating other health conditions by altering drug molecules and applying them to specific diseases.
Drug discovery acceleration
Identifying viable compounds, predicting toxicity and trial outcomes by processing historical data, and liberating research team hours by automating documentation management and data submission.
Medical data analysis
While digitization delivered considerable advantages to the healthcare industry, it also came with complications.
In particular, medical professionals are dealing with large volumes of data from diverse sources and formats. Without establishing a unified framework, such issues as data fragmentation and data silos become unavoidable. As a result, highly valuable insights slip through the cracks, causing setbacks in cure discovery and patient treatment.
The future of AI in healthcare sees the technology working in synergy with healthcare professionals, assisting them with making sense of all the knowledge they have at their disposal. As generative AI is capable of identifying relevant data and presenting it to experts, there is a powerful potential for faster problem solving and better service quality.
Trend analysis
Detecting underlying trends and patterns by analyzing numerous datasets used for research.
Search and retrieval
AI algorithms are used to retrieve important data from the necessary data source, preventing inaccuracies and securing efficient data analysis.
Predictive analytics
Converting historical data into a robust knowledge base capable of predicting potential outcomes and suggesting relevant strategies to medical professionals.
Medical file interpretation
Leveraging NLP to interpret insights from complex healthcare documents (of various formats) to facilitate information gathering and exchange.
Data source integration
Enabling a more streamlined approach to analyzing medical data through smart integration of previously diverse data sources and securing a comprehensive flow of information.
Contribution to clinical diagnosis
Healthcare workers consistently work towards having more visibility into the patient's health condition, and AI can assist them considerably with that mission. Although only 11% of clinicians' decisions are backed up by AI, this statistic is very likely to change in the near future, given that AI has already delivered impressive results in certain fields and vectors.
For example, AI was more successful at detecting early symptoms of breast cancer, demonstrating 91% efficiency. Similarly, AI tools were used for accurately diagnosing diabetic retinopathy, showing 96% sensitivity and 95% specificity, making them a crucial factor in preventing vision loss. Also, AI used for diagnosing severe thyroid eye disease delivered a 94% accuracy rate when analyzing patient CT scans.
Additionally, the future of AI in healthcare involves utilizing the technology for early Alzheimer’s diagnosis and developing proactive strategies that prevent and minimize the damage from the disease.
Disease detection
Replicating various scenarios through historical data analysis to prepare for potential diseases and identify outbreaks at the earliest stages.
Medical image segmentation
Using deep learning algorithms to identify objects on images, compare visuals, find discrepancies and segment images by the object type or custom categories.
Improved testing
Running a wider range of tests and simulated scenarios to select the most optimal treatment plan and predict treatment progress and possible disease outcomes.
Healthcare risk prediction
While patient safety is the priority at any healthcare facility, medical professionals must consider many risks and dangers. From cases of misdiagnosis and surgery complications to cybersecurity issues and a 17% increase in counterfeit drugs from 2022 to 2023, medical workers need constant awareness of external and internal factors capable of endangering patients and facilities.
In this scenario, AI for risk management provides healthcare organizations with additional transparency and visibility into complex processes and issues while enabling smart adaptation to potential cyber threats.
Paired with ML, AI demonstrates incredible results when processing data, detecting anomalies as well as identifying and predicting patterns. Such capabilities made AI a versatile tool for addressing a diverse range of risks, potentially reducing costs, and creating a safer environment for patients and employees.
Diversified planning
Analyzing past and present data to recreate different threat scenarios and prepare the most efficient measures for addressing them.
Reinforcing supply chains
Ensuring steady global logistics of medical supplies and medicine by reducing potential setbacks via predictive analytics and visibility-increasing monitoring.
Fraud prevention
Preventing reputational loss and risk to patients’ health by identifying fraudulent drugs and timely removing them from public access.
Early issue detection
Predicting potential negative surgery outcomes based on patient data, avoiding administrative errors and medical adverse events.
Patient care personalization
Regarding customer interaction, a wide range of services is transitioning from a cookie-cutter approach to a more personalized one. Healthcare is not an exception to this trend–and it's not just about meeting expectations and keeping up with needs. The very health of patients is at stake.
According to recent findings, the high 50% misdiagnosis of the inflammatory skin condition known as hidradenitis suppurativa (HS) is caused by blindspots in patient data and not taking patient experience into account. Patient care personalization allows replacing such blindspots with valuable insights such as patient history, lifestyle details, diet preferences, and many other details that can affect treatment, symptoms, or condition progression.
While personalized care had a positive effect, resulting in the FDA approving 19 cell-based therapies based on processing personalized patient data, the shift from the cumbersome trial-and-error method requires more effort.
The trick to personalized patient care lies in constantly updating each patient profile, collecting data in real time (if necessary), and building proactive treatment strategies. However, not all medical workers have enough time and resources to gather such data, making AI a valuable support in the endless sea of patient information and engagement.
Patient support
Automating patient interactions, gathering real-time feedback on their treatment progress, condition, and lifestyle changes, as well as providing instruction and suggestions.
Prescription guidance
Monitoring patient response to prescribed medicine, documenting progress, and reporting insufficient results along with recommendations.
Precision medicine
Extracting specific patient data and using it to develop new, improved treatment methods.
Improved telehealth
Automating patient consultations, collecting and processing their queries and symptoms, and getting them in touch with proper professionals.
The most valuable thing about AI in the future of healthcare is how it helps healthcare workers. If they are burdened by monotonous tasks such as data entry and paperwork, AI can take care of these tasks. If they need to comb through tons of data sets, gen AI can bring all relevant insights to them in minutes. If they need an assistant to send reminders, survey patients, and handle frequently asked questions, AI is their helper. Artificial intelligence can't do a doctor's job, but it will let doctors do their job by taking care of time-consuming tasks and keeping burnout at bay.
Improving healthcare literacy
Contrary to popular opinion, the imminent advancement of technological progress and accessibility of information don't automatically imply an increase in literacy. For instance, recent research has revealed that 88% of adults in the US have substandard health literacy. Therefore, around 88% of US citizens don’t have sufficient knowledge of what kind of medical services are available to them or how to use them efficiently.
Such gaps naturally lead to a decline in national health, an increase in mortality rates, and a growth in dissatisfaction with the healthcare system. Therefore, educating patients on interacting with health facilities, tracking their health-related behaviors, and taking necessary precautions is another part of healthcare workers' mission. Leveraging AI for patient interaction and communication considerably increases engagement and encompasses large audiences through diverse approaches—while keeping track of results.
Tailoring content
Using AI algorithms to modify medical information and make it more comprehensive for certain audiences (senior citizens, children, first-time parents).
Evidence-based approach
Instantly responding to popular queries (vaccination, treatment plans, and opportunities) by automatically attaching links to verified sources and scientific proof.
Guidance and onboarding
Educating citizens on health matters within AI-managed courses and training, complete with providing final tests and rating results.
Interactive education
Answering patient questions about new treatments, health conditions, or diseases within a highly responsive interactive system or intelligent chatbot.
Current applications of AI in healthcare
While the potential of AI in the future of healthcare is picking up speed, there are already first examples and use cases of gen AI that set new trends.
- In April 2023, AdaptyvBio started leveraging gen AI for a full-stack protein engineering foundry to accelerate medicine development.
- In July 2023, Recursion Pharmaceuticals partnered with NVIDIA to utilize AI for faster, safer, and more efficient drug discovery.
- In March 2024, Nuance Communications announced a partnership with Stanford Health Care to launch its next-generation AI platform, which the organization intends to use to minimize administrative burden and reduce employee burnout.
Among other big brand names, it is also worth mentioning that Google introduced its Med-PaLM generative AI technology designed to assist healthcare workers with data analysis, data entry and medical information processing.
It may seem that AI is widely used in healthcare already, but it's a false impression. The future of AI in healthcare is only beginning. Artificial intelligence needs a lot of research, resources, and knowledge to set up and fine-tune before it can deliver results. For that reason, it's important to stay aware of actual use cases and trends, monitor research results, and cooperate with reliable tech partners that can translate your medical facility needs into AI-powered features.
If you want a realistic and insights-rich perspective on adopting AI for your medical organization, let's chat!
At Trinetix, we leverage our expertise in healthcare and innovation to assist our clients with streamlining administrative tasks, gaining greater transparency, and creating optimized workflows with state-of-the-art AI systems. Our AI team will work with you in perfect synergy, realizing your product within an impactful journey from a concept to a value-driving tool.
