AI in healthcare: Use cases, applications, benefits, solution, AI agents and implementation

AI in healthcare: The future of patient care and health management

Hospital resource optimization involves leveraging predictive analytics to enhance the efficient allocation of resources within a healthcare facility, with a particular focus on bed management and staffing. By employing advanced algorithms, the system analyzes historical data, current patient loads, and other relevant factors to forecast future demands on hospital resources. For bed management, the predictive analytics model helps anticipate patient admissions, discharges, and transfers, ensuring that the right number of beds is available at any given time. Similarly, in staffing, the system predicts patient influxes and allocates appropriate personnel accordingly, preventing understaffing or overstaffing scenarios. This use case aids healthcare institutions in maximizing operational efficiency, improving patient care, and optimizing resource utilization, ultimately contributing to a more effective and responsive healthcare environment. Many healthcare professionals recognize the transformative potential of AI but remain cautious about its application in clinical practice.

• As healthcare continues to rapidly evolve, health systems must constantly look for innovative ways to provide better access to the right care at the right time.
• Consequently, it offers a global perspective on the evolution of chatbots within the health care domain.
• At present, GPT-4 is only accessible to those who have access to ChatGPT Plus, a premium service from OpenAI for which users have to pay US $20 a month.
• The use of ZBrain apps for healthcare fraud detection can contribute to fortified security and minimized risks.

Focusing on territories with limited access to psychological aid, it addresses critical gaps in service provision. People receive the required assistance and recommendations to improve their emotional state. This initiative demonstrates how chatbots can make care more inclusive and accessible. With this understanding, let’s look at the investment worthiness of bots in the domain. This is a paradigm shift that would be particularly useful when human resources are spread thin during a healthcare crisis. Conversational AI, on the other hand, allows patients to schedule their healthcare appointments seamlessly, and even reschedule or cancel them.

How AI is Revolutionizing Healthcare

In addition, digital assistants can collect information daily regarding patients’ health and forward the reports to the assigned physician. By taking off some of these responsibilities from human healthcare providers, virtual assistants can help to reduce their workload and improve patient outcomes. Artificial Intelligence in healthcare is changing many of the administrative aspects of medical care. Furthermore, artificial intelligence also has the potential to reduce human error by providing a faster way to review health records, medical imaging, claims processing and test results. With artificial intelligence giving medical professionals more autonomy over their workflow process, they are able to provide better quality patient care while maintaining budget efficiency.

Caption Health combines AI and ultrasound technology for early disease identification. AI guides providers through the ultrasound process in real time to produce diagnostic-quality images that the software then helps to interpret and assess. Biofourmis connects patients and health professionals with its cloud-based platform to support home-based care and recovery. The company’s platform integrates with mobile devices and wearables, so teams can collect AI-driven insights, message patients when needed and conduct virtual visits. This way, hospitals can release patients earlier and ensure a smoother transition while remotely monitoring their progress. Pfizer uses AI to aid its research into new drug candidates for treating various diseases.

Data Extraction

For example, in the case of a public health crisis such as COVID-19, a conversational AI system may distribute recommended advice such as washing your hands for 20 seconds, maintaining social distance, and wearing a face covering. Conversational AI may simplify and streamline the onboarding process, help patients through the prescription request process, enable them to update crucial information such as their address or a change in circumstances, and much more. An intelligent conversational AI platform can simplify this process by allowing employees to submit requests, communicate updates, and track statuses, all within the same system and in the form of a natural dialogue. We’ll help you decide on next steps, explain how the development process is organized, and provide you with a free project estimate. “We’re no longer in an infancy stage,” says Natalie Schibell, vice president and research director for healthcare at Forrester Research, noting the impact of the COVID-19 pandemic in accelerating digital transformation. In the United States alone, more than half of healthcare leaders, 56% to be precise, noted that the value brought by AI exceeded their expectations.

The good news is that most customers prefer self-service over speaking to someone, which is good news for personnel-strapped healthcare institutions. Schibell sees a deep need for AI to address healthcare problems such as chronic illness, workforce shortages and hospital readmissions. These factors are leading healthcare organizations, insurance companies and pharma and life sciences organizations to adopt AI, she says.

Predictive analytics enables improved clinical decision support, population health management, and value-based care delivery, and its healthcare applications are continually expanding. While digital technologies cannot replace the human elements of the patient experience, they have their place in healthcare consumerism. By actively monitoring, gathering feedback, iterating, and educating users, you can ensure your healthcare chatbot continues to evolve and deliver value in the long run. Train chatbots for specific scenarios, integrate natural language processing and offer escalation paths to human specialists.

This is because only NLP-based healthcare chatbots can truly understand the intent in patient communication and formulate relevant responses. This is in stark contrast to systems that simply process inputs and use default responses. Healthcare professionals can now efficiently manage resources and prioritize clinical cases using artificial intelligence chatbots. The technology helps clinicians categorize patients depending on how severe their conditions are.

Appointment scheduling and management represent another vital area where chatbots streamline processes. Patients can easily book appointments, receive reminders, and even reschedule appointments through chatbot interactions (6). This convenience not only benefits patients but also reduces the administrative workload on healthcare providers.

It is imperative to document and disseminate information regarding AI’s role in clinical practice, to equip healthcare providers with the knowledge and tools necessary for effective implementation in patient care. This review article aims to explore the current state of AI in healthcare, its potential benefits, limitations, and challenges, and to provide insights into its future development. By doing so, this review aims to contribute to a better understanding of AI’s role in healthcare and facilitate its integration into clinical practice. AI may also compromise the protection of patients’ rights, such as the right to informed consent and the right to medical data protection.[113] These challenges of the clinical use of AI have brought about a potential need for regulations. AI studies need to be completely and transparently reported to have value to inform regulatory approval.

They can also be programmed to answer specific questions about a certain condition, such as what to do during a medical crisis or what to expect during a medical procedure. Using the integrated databases and applications, a chatbot can answer patients’ questions on a healthcare organization’s schedule, health coverage, insurance claims statuses, etc. Backed by sophisticated data analytics, AI chatbots can become a SaMD tool for treatment planning and disease management. A chatbot can help physicians ensure the medications’ compatibility, plan the dosage, consider medication alternatives, suggest care adjustments, etc.

Conversational AI, on the other hand, uses natural language processing (NLP) to comprehend the context and “parse” human language in order to deliver adaptable responses. One of the more interesting new discoveries is the emergence of artificial intelligence systems such as conversational AI for healthcare. Our tech team has prepared five app ideas for different types of AI chatbots in healthcare. Integration with a hospital’s internal systems is required to run administrative tasks like appointment scheduling or prescription refill request processing. AI-powered chatbots have been one of the year’s top topics, with ChatGPT, Bard, and other conversational agents taking center stage.

According to the pre-fetched inputs, the chatbots can utilize the information to help the patients diagnose the ailment causing their symptoms. With an interactive bot and the data it gives, the patient may determine the appropriate amount of treatments and drugs. Chatbots are presently being used more and more to analyze a patient’s symptoms and check their medical status without requiring them to visit a hospital. NLP-based chatbot development can assist in interpreting a patient’s request regardless of the range of inputs. Making appointments is one of the jobs that is done in the healthcare industry the most frequently.

Several professional organizations have developed frameworks for addressing concerns unique to developing, reporting, and validating AI in medicine [69,70,71,72,73]. The US Food and Drug Administration (FDA) is now developing guidelines on critically assessing real-world applications of AI in medicine while publishing a framework to guide the role of AI and ML in software as medical devices [74]. The European Commission has spearheaded a multidisciplinary effort to improve the credibility of AI [75], and the European Medicines Agency (EMA) has deemed the regulation of AI a strategic priority [76].

Cognitive behavioral therapy can also be practiced through conversational chatbots to some extent. Chatbots gather user information by asking questions, which can be stored for future reference to personalize the patient’s experience. With this approach, chatbots not only provide helpful information but also build a relationship of trust with patients.

In the event of disagreements, the 2 authors will discuss in team meetings with the corresponding author (ZN) to reach a consensus. All interventional and observational studies published as journal papers or conference proceedings will be included. To offer a holistic view of the evolving usage of chatbots in health care, we will not set restrictions on the year of publication. Moreover, we will not exclude papers published in non–English language to incorporate research findings from low- and middle-income countries [30]. Studies that do not discuss the use of chatbots to promote health or wellness will be excluded. Systematic reviews pertaining only to chatbot designs and development, purposes, or features will be excluded.

Effective patient engagement

This technology not only enhances the capabilities of healthcare professionals but also empowers patients through personalized care, early disease detection, and improved treatment outcomes. As AI continues to evolve and integrate into healthcare, it promises to create a more proactive, precise, and patient-centered approach to medicine, ultimately leading to a healthier and more efficient healthcare ecosystem. AI plays a pivotal role in providing continuous support for individuals dealing with conditions like diabetes, hypertension, and asthma.

This safeguard includes designating people, either by job title or job description, who are authorized to access this data, as well as electronic access control systems, video monitoring, and door locks restricting access to the data. Using these safeguards, the HIPAA regulation requires that chatbot developers incorporate these models in a HIPAA-complaint environment. This requires that the AI conversations, entities, and patient personal identifiers are encrypted and stored in a safe environment. The Health Insurance and Portability and Accountability Act (HIPAA) of 1996 is United States regulation that sets the standards for using, handling, and storing sensitive healthcare data.

Among these challenges is the lack of quality medical data, which can lead to inaccurate outcomes. Data privacy, availability, and security are also potential limitations to applying AI in clinical practice. Additionally, determining relevant clinical metrics and selecting an appropriate methodology is crucial to achieving the desired outcomes.

In research, AI has been used to analyze large datasets and identify patterns that would be difficult for humans to detect; this has led to breakthroughs in fields such as genomics and drug discovery. AI has been used in healthcare settings to develop diagnostic tools and personalized treatment plans. As AI continues to evolve, it is crucial to ensure that it is developed responsibly and for the benefit of all [5,6,7,8].

The market for healthcare chatbots is expected to grow from $230.28 million in 2023 to $944.65 million by 2032. Navigating regulatory landscapes can present significant hurdles for AI chatbots in healthcare (30). Regulatory bodies like the Food and Drug Administration (FDA) in the US or the European Medicines Agency (EMA) in Europe have rigorous processes for granting approval to AI chatbot-based medical devices and solutions. These processes, while critical for ensuring safety and efficacy, can be time-consuming and resource-intensive.

They could be particularly beneficial in areas with limited healthcare access, offering patient education and disease management support. However, considering chatbots as a complete replacement for medical professionals is a myopic view. The more plausible and beneficial future lies in a symbiotic relationship where AI chatbots and medical professionals complement each other. Each, playing to their strengths, could create an integrated approach to healthcare, marrying the best of digital efficiency and human empathy. As we journey into the future of medicine, the narrative should emphasize collaboration over replacement. The goal should be to leverage both AI and human expertise to optimize patient outcomes, orchestrating a harmonious symphony of humans and technology.

One of the largest children’s hospitals in the US embarks on a digital transformation journey with DRUID’s conversational AI technology. The hospital implementing an automatic process ensuring COVID-19 checks are made without errors and with as little disruption and hassle for staff. One of the largest companies in the CEE and leader in the quality of medical care, Regina Maria, continues the journey of digital transformation with the help of DRUID conversational virtual assistants. Monitor how the Chatbot is performing, what queries it is handling well, and where it might be falling short.

While they improved efficiency by freeing up human resources from mundane tasks, they were quite limited in their capacity to understand and respond to complex patient inquiries. Their functionality revolved around a set of predefined rules, and they lacked the ability to learn from past interactions or provide personalized responses. The best option available to healthcare institutions to raise awareness and enhance program enrollment is medical chatbots. Thus, whether a patient wants to check the status of a claim, register a claim, or confirm their existing coverage, a healthcare chatbot may provide them with a simple method to get the information they need.

Yes, reputable healthcare chatbots prioritize data security and comply with industry regulations like HIPAA (Health Insurance Portability and Accountability Act) in the United States. They utilize encryption protocols, secure servers, and stringent access controls to safeguard patients’ sensitive medical information. Additionally, they undergo regular security audits to ensure compliance and mitigate any potential risks. Within the realm of telemedicine, chatbots equipped with AI capabilities excel at preliminary patient assessments, assisting in case prioritization, and providing valuable decision support for healthcare providers. A noteworthy example is TytoCare’s telehealth platform, where AI-driven chatbots guide patients through self-examination procedures during telemedicine consultations, ensuring the integrity of collected data (9).

Which can help reduce healthcare costs and improve patient outcomes by ensuring patients receive timely and appropriate care. However, it is pivotal to note that the success of predictive analytics in public health management depends on the quality of data and the technological infrastructure used to develop and implement predictive models. In addition, human supervision is vital to ensure the appropriateness and effectiveness of interventions for at-risk patients. In summary, predictive analytics plays an increasingly important role in population health.

At a minimum, ensure any conversational AI solution adheres to stringent privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), safeguarding sensitive patient information and maintaining trust. Take things a step further by ensuring that any vendor in the consideration mix is HITRUST certified. Careful planning and close collaboration with your IT team should be the norm when working to implement any AI technology for healthcare. More than likely, there are existing governance standards that have been established and should be applied to the deployment of conversational AI. This data will train the chatbot in understanding variants of a user input since the file contains multiple examples of single-user intent.

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. 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 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. Find out how the healthcare chatbot from Master of Code Global can revolutionize patient care and optimize clinic operations.

AI would propose a new support system to assist practical decision-making tools for healthcare providers. In recent years, healthcare institutions have provided a greater leveraging capacity of utilizing automation-enabled technologies to boost workflow effectiveness and reduce costs while promoting patient safety, accuracy, and efficiency [77]. By introducing advanced technologies like NLP, ML, and data analytics, AI can significantly provide real-time, accurate, and up-to-date information for practitioners at the hospital. According to the McKinsey Global Institute, ML and AI in the pharmaceutical sector have the potential to contribute approximately $100 billion annually to the US healthcare system [78]. Using automated response systems, AI-powered virtual assistants can handle common questions and provide detailed medical information to healthcare providers [79]. AI-powered chatbots help reduce the workload on healthcare providers, allowing them to focus on more complicated cases that require their expertise.

Moreover, virtual assistants offer guidance on sickness symptoms, suggesting home remedies and indicating when medical intervention is advisable. AI-enabled patient engagement chatbots in healthcare provide prospective and current patients with immediate, specific, and accurate information to improve patient care and services. A survey on artificial intelligence (AI)-powered chatbots – such as ChatGPT – showed that both patients and health care professionals see the technology as having the potential to improve care and reduce costs. This provides patients with an easy gateway to find relevant information and helps them avoid repetitive calls to healthcare providers. In addition, healthcare chatbots can also give doctors easy access to patient information and queries, making it convenient for them to pre-authorize billing payments and other requests from patients or healthcare authorities. LLM healthcare chatbots hold immense promise for revolutionizing the healthcare landscape, improving patient care, promoting well-being, and streamlining administrative processes.

Additionally, AI can analyze images and data during surgeries, leading to more accurate and efficient procedures. AI integration raises concerns about the potential erosion of patient autonomy and the value of the human touch in healthcare. While AI can assist in diagnosis and treatment, it should not replace the patient-physician relationship, which is fundamental to healthcare delivery. Ensuring that AI supports, rather than undermines, patient autonomy and the personalized care provided by healthcare professionals is an important ethical consideration.

One of the prevalent challenges in drug development is non-clinical toxicity, which leads to a significant percentage of drug failures during clinical trials. However, the rise of computational modeling is opening up the feasibility of predicting drug toxicity, which can be instrumental in improving the drug development process [46]. This capability is particularly vital for addressing common types of drug toxicity, such as cardiotoxicity and hepatotoxicity, which often lead to post-market withdrawal of drugs. AI agents, unlike traditional AI models, possess distinctive characteristics that distinguish them in their functionality. These agents exhibit a higher degree of autonomy, allowing them to operate independently without constant human intervention. Furthermore, they are equipped with sensory capabilities, enabling them to perceive and interpret their environment through various data inputs.

Diagnosis accuracy

This global experience will impact the healthcare industry’s dependence on chatbots, and might provide broad and new chatbot implementation opportunities in the future. Conversational chatbots with different intelligence levels can understand the questions of the user and provide answers based on pre-defined labels in the training data. This feedback concerning doctors, treatments, and patient experience has the potential to change the outlook of your healthcare institution, all via a simple automated conversation. Once you integrate the chatbot with the hospital systems, your bot can show the expertise available, and the doctors available under that expertise in the form of a carousel to book appointments. You can also leverage multilingual chatbots for appointment scheduling to reach a larger demographic. Ever since the introduction of chatbots, health professionals are realizing how chatbots can improve healthcare.

In certain situations, conversational AI in healthcare has made better triaging judgments than certified professionals with a deeper examination of patients’ symptoms and medical history. Conversational AI combines advanced automation, artificial intelligence, and natural language processing (NLP) to enable robots to comprehend and respond to human language. Meanwhile, ML is used to predict patient outcomes, including hospitalization, and to identify which patients may have COVID-19. RRI uses deep learning to analyze images from smartphones or tablets to assess a patient’s arterio-venous vascular access, which is used to connect a patient to the dialysis machine.

The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity. The joint ITU-WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) has built a platform – known as the ITU-WHO AI for Health Framework – for the testing and benchmarking of AI applications in health domain. As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.

With the ability to create and analyze 3D visualizations, Butterfly Network’s tools can be used for anesthesiology, primary care, emergency medicine and other areas. From faster diagnoses to robot-assisted surgeries, the adoption of AI in healthcare is advancing medical treatment and patient experiences. These impacts are just the beginning of how AI is poised to transform the healthcare industry, and many more changes are https://chat.openai.com/ likely to emerge as these technologies advance to improve care delivery and patient outcomes. Last year, New Jersey-based AtlantiCare implemented pre-operative AI assessment tools and surgical robotics techniques to support early lung cancer diagnosis and treatment. Remote patient monitoring (RPM) has become more familiar to patients following the COVID-19 pandemic and the resulting rise in telehealth and virtual care.

This trend is primarily driven by the convenience of chatbot-powered search for users, as it eliminates the need for users to manually sift through search results as required in traditional web-based searches. However, no recognized standards or guidelines have been established for creating health-related chatbots. We believe that with theory-informed and well-trained algorithms, chatbot technology in healthcare chatbots can also be used as health care digital assistants to provide consumers and patients with quick, precise, and individualized answers. You can foun additiona information about ai customer service and artificial intelligence and NLP. For example, Weill Cornell Medicine reported a 47% increase in appointments booked digitally through the use of AI chatbots [39]. Chatbots are a type of AI software that interacts with users through messaging systems.

By using HyFDCA, participants in federated learning settings can collaboratively optimize a common objective function while protecting the privacy and security of their local data. This algorithm introduces privacy steps to guarantee that client data remains private and confidential throughout the federated learning process. A chatbot can personalize questions and alter the dialog flow based on the user’s answers.

Furthermore, moving large amounts of data between systems is new to most health care organizations, which are becoming ever more sensitive to the possibility of data breaches. To secure the systems, organizations need to let the good guys in and keep the bad guys out by ensuring solid access controls and multifactor authentication as well as implementing end Chat GPT point security and anomaly detection techniques [29]. One of the most critical considerations in implementing AI chatbots like ChatGPT is ensuring data security and privacy. This is even more important in highly regulated industries, such as health care delivery, pharmaceutical delivery, banking, and insurance, where AI tools collect client information.

The Cleveland Clinic teamed up with IBM on the Discovery Accelerator, an AI-infused initiative focused on faster healthcare breakthroughs. The joint center is building an infrastructure that supports research in areas such as genomics, chemical and drug discovery and population health. The collaboration employs big data medical research for the purpose of innovating patient care and approaches to public health threats. Valo uses artificial intelligence to achieve its mission of transforming the drug discovery and development process. With its Opal Computational Platform, Valo collects human-centric data to identify common diseases among a specific phenotype, genotype and other links, which eliminates the need for animal testing. Owkin leverages AI technology for drug discovery and diagnostics with the goal of enhancing cancer treatment.

AI redefines drug discovery by swiftly analyzing vast datasets to predict potential drug candidates. It accelerates the early stages of discovery, enabling researchers to concentrate on the most prospective compounds. Additionally, AI optimizes clinical trials, identifying suitable patient cohorts and enhancing trial design, leading to a more efficient and cost-effective drug development pipeline.

This chatbot solution for healthcare helps patients get all the details they need about a cancer-related topic in one place. It also assists healthcare providers by serving info to cancer patients and their families. Buoy Health was built by a team of doctors and AI developers through the Harvard Innovation Laboratory. Trained on clinical data from more than 18,000 medical articles and journals, Buoy’s chatbot for medical diagnosis provides users with their likely diagnoses and accurate answers to their health questions. This free AI-enabled chatbot allows you to input your symptoms and get the most likely diagnoses.

• Together, these tools represent significant advancements in AI technology, empowering the development of intelligent systems capable of autonomously performing diverse tasks in various healthcare domains.
• But, while AI medical Chatbots have the potential to revolutionize patient care, there are some myths around the future implications as well.
• This predictive capability enables healthcare providers to offer proactive, preventative care, ultimately leading to better patient outcomes and reduced healthcare costs.
• It can provide symptom-based solutions, suggest remedies, and even connect patients to nearby specialists.

For the relationship between patients and an AI-based healthcare delivery system to succeed, building a relationship based on trust is imperative [106]. AI has the potential to revolutionize mental health support by providing personalized and accessible care to individuals [87, 88]. Several studies showed the effectiveness and accessibility of using Web-based or Internet-based cognitive-behavioral therapy (CBT) as a psychotherapeutic intervention [89, 90]. Even though psychiatric practitioners rely on direct interaction and behavioral observation of the patient in clinical practice compared to other practitioners, AI-powered tools can supplement their work in several ways. Furthermore, these digital tools can be used to monitor patient progress and medication adherence, providing valuable insights into treatments’ effectiveness [88].

In essence, AI transforms the traditional drug discovery process, making it faster, more targeted, and cost-efficient. Leveraging chatbot for healthcare help to know what your patients think about your hospital, doctors, treatment, and overall experience through a simple, automated conversation flow. They are likely to become ubiquitous and play a significant role in the healthcare industry. They are conversationalists that run on the rules of machine learning and development with AI technology.

Studies on the coverage of health-related chatbot research have predominantly been conducted in the form of scoping or systematic reviews [19,25,26]. The current body of research papers lacks the breadth of a comprehensive scientific performance mapping analysis. This overview will facilitate the identification of areas for improvement and promote the integration of chatbot technology into health care systems.