AI in Healthcare — An Overview

Introduction

AI systems are sometimes seen as the answer to many healthcare challenges. Researchers and politicians acknowledge that healthcare worldwide is grappling with significant economic, medical, and social hurdles. A particular concern is our ability to manage demographic changes effectively and how to tackle resource shortages while addressing patients' health concerns promptly.

Although we’re living healthier and longer lives, the cost of healthcare in our final years has skyrocketed. Healthcare also faces other critical challenges, such as limited resources and personnel, increasing globalization, and a rapidly evolving society. We’re also exposed to sudden global changes. The COVID-19 pandemic showed how quickly viral outbreaks can overwhelm communities and place a heavy load on healthcare systems — a scenario that could recur.

In response, we need innovative and impactful solutions. AI technology is often highlighted as a way to address these issues. By combining advances in medical technology with changes in management and regulation, AI can potentially enhance both the quality and efficiency of healthcare. It’s believed that the healthcare system can deliver more services using fewer resources through new, cost-effective, and automated systems.

What AI is?

Artificial intelligence (AI) simulates human intelligence in machines, particularly computer systems, offering solutions to real-world problems in health, education, and the environment. AI can sometimes perform tasks more efficiently or methodically than humans.

There are three main types of AI, categorized by their capabilities, the technologies they employ, their applications, and their theoretical understanding of mind:

1. Artificial Narrow Intelligence (ANI) / Weak AI / Narrow AI: This type of AI has a limited range of abilities. It includes systems like Google’s RankBrain, voice assistants such as Siri (Apple), Alexa (Amazon), Cortana (Microsoft), image/facial recognition software, disease mapping tools, email spam filters, social media monitoring tools, and personalized recommendations in entertainment or marketing. ANI systems can also encompass self-driving car technologies. They operate within a specific context, using large data volumes for deep learning to provide personalized experiences.

2. Artificial General Intelligence (AGI) / Strong AI / Deep AI: AGI is supposed to match human intelligence and capabilities, a goal that remains unachieved. Achieving AGI would require machines to possess consciousness and a full set of cognitive abilities, making them capable of performing any intellectual task that a human being can.

3. Artificial Superintelligence (ASI): ASI is supposed to exceed human intelligence and ability, where machines become self-aware and surpass human cognitive capabilities. The concept of ASI, often depicted in dystopian science fiction, involves AI evolving to understand human emotions and experiences so profoundly that it develops its own emotions, needs, beliefs, and desires.

Each type of AI represents different stages of development and potential impacts on society, with ongoing research pushing the boundaries of what machines can do.

What are the biggest challenges of healthcare systems

In the last decade, technological advancements have significantly improved healthcare and consumer experiences across numerous countries. However, a range of critical issues persists that could greatly benefit from further innovation and technological solutions. These challenges include preventable medical errors, suboptimal amenable mortality rates, a lack of transparency, difficulties in finding qualified healthcare providers, high costs of care, inadequate insurance coverage, shortages of nursing and physician staff, systemic inefficiencies, late or incorrect diagnoses, management of chronic conditions, and the care of the elderly.

Tackling these healthcare challenges necessitates recognizing these issues as substantial real-world problems. Transformative change is within reach through the strategic application of technology, innovation, and the dedication of pioneers seeking solutions. The journey toward a better healthcare future starts with a comprehensive understanding of these existing challenges and a firm commitment to finding and implementing innovative solutions.

Applications of AI in Healthcare

In recent years, artificial intelligence (AI) has emerged as a game-changer in healthcare, offering innovative solutions to some of the sector’s most daunting challenges. AI is transforming healthcare delivery in numerous ways, from enhancing disease diagnosis to refining treatment protocols, thereby promising better health outcomes, increased operational efficiency, and improved patient care experiences. AI’s capability to execute healthcare-related tasks with equal or superior proficiency compared to human intervention is noteworthy.

AI’s positive influence extends across three pivotal areas of medical practice:

• Clinical Impact: AI significantly enhances the quality of treatment by boosting the speed and accuracy of diagnoses. This advancement not only aids in the early detection of diseases but also in tailoring precise treatment plans for patients.
• Pharmaceutical Impact: In the pharmaceutical domain, AI accelerates research and the compilation of reports, facilitating the discovery and development of new drugs. This acceleration is crucial for responding swiftly to emerging health crises and for the ongoing improvement of healthcare treatments.
• Administrative Impact: On the administrative front, AI streamlines revenue cycle management and simplifies bureaucratic procedures through automation. By processing data efficiently and automating routine tasks, AI frees up healthcare professionals to focus more on patient care and less on paperwork.

By addressing these key components, AI is not only optimizing healthcare processes but also paving the way for a future where healthcare systems can deliver more personalized, timely, and effective care to individuals.

Let’s take a look at some of the most prominent use cases of AI for healthcare:

Healthcare Workflow Optimization

The adoption of Electronic Medical Records (EMR) and Electronic Health Records (EHR) systems has revolutionized patient documentation processes, which were once time-consuming, costly, and prone to errors. Healthcare organizations across various sizes and specialties now achieve more accurate treatment and increased efficiency through these systems.

EMRs and EHRs play a pivotal role in optimizing healthcare workflows, minimizing waste, and enhancing patient flow, all of which contribute to improved patient outcomes. Efficient workflows are crucial for maintaining effective, efficient, and compliant operations within healthcare organizations.

Regularly assessing a medical practice’s workflows is key to streamlining and enhancing daily operations. Through workflow mapping, the identification of bottlenecks that hinder efficiency, collection of user feedback, and thorough analysis and documentation of findings, healthcare providers can evaluate and refine their processes.

By implementing and adhering to standard healthcare workflows and adding a layer of AI on top, clinicians and staff can ensure that patient care is prioritized and administered promptly, thereby also minimizing the risk of errors.

Leading EHR systems that stand out in the industry include CureMD, Epic Care, athenaOne, AdvancedMD, Cerner Ambulatory, eClinicalWorks and Allscripts.

Triage

The integration of artificial intelligence (AI) into the triage process holds the promise of significantly enhancing efficiency, accuracy, and patient outcomes. By utilizing advanced algorithms and machine learning techniques, AI can assist healthcare professionals in making more informed decisions during triage, a critical first step in the patient care journey.

Traditionally, triage is conducted by healthcare professionals who evaluate patients based on their symptoms, vital signs, and medical history. This essential process, however, can be intricate and time-consuming, posing challenges in accurately prioritizing patients, particularly in environments with high patient volumes. These challenges can impact the timely allocation of care and resources, potentially affecting patient outcomes.

AI’s potential lies in its ability to process and analyze vast datasets rapidly, identify patterns that may not be immediately obvious to humans, and provide real-time insights. This capability enables AI to support healthcare professionals by offering evidence-based recommendations for patient prioritization. In high-pressure settings, such as emergency departments, AI-assisted triage can lead to more accurate and faster decision-making, ensuring that patients receive the care they need when they need it most.

Moreover, AI can continually learn and improve over time, adapting to new data and evolving healthcare practices. This dynamic learning process means that AI systems can become increasingly effective in supporting triage decisions, potentially transforming how care is delivered and enhancing both the efficiency of healthcare operations and the quality of patient care.

The implementation of AI in triage has garnered significant attention, with healthcare organizations exploring its benefits and limitations. For AI in triage to work well, hospitals need good data, the right technology, and must address privacy and fairness issues. Success stories from around the world show that when done right, AI can greatly improve how patients are assessed and treated, leading to better outcomes and more efficient use of resources. However, it’s crucial to keep a balance, ensuring that AI supports but doesn’t replace the human touch in healthcare decisions. Moving forward, the goal is to use AI responsibly to make emergency care better for everyone.

Clinical Decision Support Systems (CDSS)

Clinical Decision Support Systems (CDSS) powered by Artificial Intelligence (AI) are transforming healthcare by leveraging patient data and medical knowledge. These systems analyze a wide range of patient information, from historical data to current health status, to identify safety concerns, errors, or potential improvements in patient care pathways. Their ability to deliver highly relevant and precise predictions paves the way for unprecedented optimization of patient care.

Several high-profile projects exemplify the potential of AI CDSS. Microsoft’s ‘InnerEye’ uses AI to identify tumors from radiography scans, demonstrating the system’s ability to perform critical diagnostic tasks with accuracy comparable to human specialists. Google’s DeepMind has developed the Streams algorithm, which can predict acute kidney failure earlier than traditional methods. Babylon’s Triage and Diagnostic system is another example, claiming diagnostic capabilities on par with or superior to human doctors. These systems showcase the possibility of replicating or even surpassing the diagnostic expertise of top medical consultants through the use of fixed algorithms and the ability to learn and improve over time.

The development of Clinical Decision Support Tools is not limited to these examples; companies like Think Research, RAMPmedical, Medical Algorithms Company, Cohesic, and Human Bytes represent a fraction of the entities working in this space. These tools are set to revolutionize healthcare by enhancing diagnostic accuracy, improving patient outcomes, and potentially making the expertise of top specialists more widely accessible.

Disease Diagnostics

AI has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-enabled diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. To make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases.

Here are some examples of companies showing AI’s ability to reduce errors and improve the diagnostic process: BrainSightAI, DoMore Diagnostics, Buoy Health, Enlitic, and Iterative Health.

Patient Navigation

Patient navigation involves guiding patients through the complex healthcare system to help them access the appropriate care and services. AI can enhance patient navigation by providing intelligent tools and solutions to streamline the process and improve overall patient experience. Chatbots for Triaging, Appointment Scheduling, Health Information and Education, and Remote Monitoring and Follow-up are some applications of AI in patient navigation.

The integration of AI in patient navigation aims to make healthcare more patient-centric, efficient, and accessible, ultimately improving outcomes and satisfaction for both patients and healthcare providers.

PatientGPT built on OpenAI’s ChatGPT aims to transform patient navigation, offering a seamless and personalized experience for healthcare organizations and their patients.

Precision Medicine

The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function, and environment. This offers clinicians the opportunity to carefully tailor early interventions — whether treatment or preventative — to each patient. Taking advantage of high-performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular diseases from available multidimensional clinical and biological data.

Oncxerna, Perceiv AI, Precomb Therapeutics, and AUM Biosciences are some Precision medicine startups, focus on targeted, personalized care using big data.

The objective of precision medicine is to make a diagnosis of disease or illness, treatment therapies, and prevention more personalized, proactive, predictive, and precise.

Patient Engagement and Education

In recent years, chatbots have become increasingly present in our digital lives. Though previously used mainly as virtual assistants and in customer service, ChatGPT has ignited our fascination with the potential of chatbots to change the world. Medical chatbots respond to prompts and data shared by users about their health to offer relevant information, guidance, and advice. As healthcare systems grapple with staffing shortages and overburdened resources, medical chatbots could offer a digital lifeline.

A healthcare chatbot can act as a personal health specialist, offering assistance beyond just answering basic questions. It can provide symptom-based solutions, suggest remedies, and even connect patients to nearby specialists. Healthcare chatbots prove to be particularly beneficial for those individuals suffering from chronic health conditions, such as asthma, diabetes, and others.
Additionally, Chatbots, AI systems programmed to process and generate human language, are currently being evaluated for various tasks in medical education. With their capability to summarize, simplify complex concepts, automate the creation of memory aids, and serve as an interactive tutor and point-of-care medical reference, chatbots have the potential to enhance students’ comprehension, retention, and application of medical knowledge in real time. While the integration of AI-powered chatbots in medical education presents numerous advantages, students must use these tools as assistive tools rather than relying on them entirely.
Woebot Health, OneRemission, eMed, Florence, Healthily, Ada Health, and Sensely are among the companies in Healthcare Chatbots.

Remote Patient Monitoring

The integration of artificial intelligence (AI) into Remote Patient Monitoring (RPM) is revolutionizing healthcare by enhancing patient care, increasing efficiency, and enabling early intervention. RPM leverages technology to monitor patients’ health conditions remotely, reducing the need for frequent in-person visits.

Medtronic CareLink, NexHealth, Tomorrow Health and Dexcom are some remote patient-monitoring startups

Clinical Research

In medical research, AI is most commonly employed to analyze and identify patterns in large, complex datasets. Importantly, this data can be analyzed in a significantly faster, more precise and more cost-effective way than traditional analytical methods — reducing spend and improving outcomes.

Today, AI gives scientists the ability to uncover complex associations within datasets that cannot be uncovered through traditional, equation-based statistical analysis. Once the AI system has collated and “translated” this vast haul of data into a common language, it can then employ machine-learning algorithms to make useful cancer drug-discovery predictions.

Aside from improving clinical decision-making and optimizing innovation, AI can make the research process far more cost-effective — and the key figures within healthcare are standing up and taking notice.

Altis Labs, ConcertAI, Neuroute, and Owkin are established companies, bringing the power of next-gen predictive and generative AI tools to the field of clinical and translational research.

Ethics of AI

The ethical issues with artificial intelligence in healthcare revolve around privacy and surveillance, bias and discrimination, as well as the role of human judgment. Where there is technology, there is always a risk of inaccuracy and data breaches, and mistakes in healthcare can have devastating consequences for patients. Because there are no well-defined regulations on the legal and ethical issues relating to artificial intelligence and the role it plays in healthcare, this is a crucial topic that needs to be explored.

The use of AI in healthcare raises concerns about inaccuracy and data breach possibilities. The use of electronic healthcare records can be used for Scientific studies, improving the quality of healthcare and clinical care optimization, however, this comes with a risk of data being hacked and shared for the wrong purposes. Other ethical considerations include the ownership of an individual’s healthcare records and patient history, who this will be shared with and when, and if consent needs to be given.

HIPAA and Using AI in Healthcare

HIPAA does not specifically address artificial intelligence, but it is important to remain compliant with all HIPAA controls, as each control applies even with this relatively new technology.

The three main requirements of HIPAA are:

• Appropriate safeguarded mechanisms must be in place to protect the privacy of protected health information and must only be accessed by authorized parties.
• The confidentiality, integrity, and security of ePHI must be protected via administrative, physical, and technical defenses.
• Notification must be provided as the result of a breach of any unsecured ePHI.

Privacy and Security Issues in AI

The following are key factors to consider when using AI in healthcare by HIPAA and patient trust.

1. Transparency

As artificial intelligence is infused into the medical field, patient data will be continuously absorbed. There is a right for patients/consumers to question the transparency of usage and the safety of their data.

Health organizations using AI should disclose how AI is used within systems and why the use of AI will benefit the organization’s processes when serving its patients. Furthermore, reveals the scope of patient data sets that will be used within AI systems (e.g., blood type, weight, gender, age, disease) as well as the purpose of the AI systems (e.g., diagnosis and public health trends).

In addition, allow patients to choose what kinds of ePHI will be used within your AI systems.

2. Potential AI Risks — Manage and Mitigate

The use of artificial intelligence in the healthcare environment is not without risk, and here as elsewhere in cybersecurity the mitigation of cyberattacks greatly depends on structured and efficient risk management.

Organizations need to choose the most appropriate risk management framework that aligns with their objectives and accounts for HIPAA compliance and patient trust.

3. Protection of Data

To produce accurate and illustrative results, artificial intelligence often requires enormous amounts of data, posing a high privacy risk. One of the ways to prioritize the protection of PHI is to apply detective and preventative controls — these should be designed to keep the confidentiality, integrity, and security of datasets and information systems.

4. Anonymization and Access Control According to HIPAA

According to HIPAA, the removal, or modification of personally identifiable information is one of the requirements necessary to protect PHI data. HIPAA permits users to have the applicable rights and/or privileges to access and perform certain functions as per their role and responsibilities.

Barriers to AI Adoption in Healthcare

The adoption of AI in healthcare faces challenges, notably the algorithmic limitations, data access limitations, regulatory barriers, and Ethics of AI.

Medical Billing

Medical billing is not an easy field to use AI, despite its potential. When implementing an AI platform, most healthcare organizations will face challenges such as complex coding systems, data integrity, legacy systems, limitations on resources, and resistance to change. Many healthcare facilities still use old systems that must be updated to work with new AI solutions and medical billing data that needs to be more consistent or correct can make it harder to make accurate AI models.

Physicians Interest

It may be challenging to integrate AI into daily clinical practice, depending on its intended purpose. Will physicians need to develop individual systems to coordinate AI results with EHR charting? The varying levels of technology literacy among physicians may add frustrations as they learn how to incorporate and utilize AI platforms while struggling with existing technologies like EHRs. In addition, understanding how AI algorithms work may add more responsibilities that exacerbate physician burnout. As an example, physicians should weigh the opportunity costs of utilizing AI technology versus seeing patients face-to-face.
Physicians may also have concerns over bias built into AI technology. AI platforms are limited by the concept of “what goes in, comes out,” which means that algorithms are only as good as their data sources.

Responsibilities

Challenges to implementing AI in healthcare extend beyond technical considerations. As organizational boundaries expand across various locations and data sources, the footprint IT professionals must secure is growing exponentially. Healthcare data, classified as Protected Health Information (PHI), demands strict privacy and security measures to comply with legal requirements. Preserving patient privacy is paramount. Any data breaches, misuse, or unauthorized access to protected health information can have severe consequences.
WHO reiterates the importance of applying ethical principles and appropriate governance, as enumerated in the WHO guidance on the ethics and governance of AI for health, when designing, developing, and deploying AI for health. The 6 core principles identified by WHO are: (1) protect autonomy; (2) promote human well-being, human safety, and the public interest; (3) ensure transparency, explainability, and intelligibility; (4) foster responsibility and accountability; (5) ensure inclusiveness and equity; (6) promote AI that is responsive and sustainable.

Several regulations and ethical frameworks are currently used to help guide the use of AI in healthcare. Ongoing development of these regulations is needed, however, to address the rapidly evolving landscape.

The Health Insurance Portability and Accountability Act (HIPAA) exists to set standards for the privacy and security of patients’ health data. In addition, the Food and Drug Administration (FDA) has its own set of regulations providing regulatory oversight on how software, such as AI applications, is used in healthcare. The EU has the General Data Protection Regulation (GDPR) which establishes requirements for the handling of patient data used in AI tools.

In conclusion, the integration of Artificial Intelligence (AI) in healthcare represents a transformative trend that has the potential to revolutionize patient care, diagnostics, and overall healthcare delivery. The benefits are evident, ranging from improved diagnostic accuracy and personalized treatment plans to enhanced patient navigation and remote monitoring.

However, as with any powerful technology, the responsible and ethical use of AI in healthcare is crucial. The call for policymakers to proactively understand AI and formulate regulations is vital. These regulations should achieve a delicate balance — ensuring the safety and security of AI applications in healthcare without limiting the innovative potential of these technologies. It is imperative to create a regulatory framework that fosters collaboration between technology developers, healthcare professionals, and regulatory bodies.

Policymakers should focus on addressing issues related to data privacy, patient consent, algorithm transparency, and ethical considerations. By doing so, they can help build trust in AI systems among both healthcare professionals and patients, creating a positive and collaborative environment for the continued integration of AI in healthcare.

In this era of digital transformation, it is not only advisable but necessary for healthcare systems worldwide to adapt and integrate AI technologies.