The potential for artificial intelligence in healthcare PMC

H2O.ai’s AI analyzes data throughout a healthcare system to mine, automate and predict processes. It has been used to predict ICU transfers, improve clinical workflows and pinpoint a patient’s risk of hospital-acquired infections. Using the company’s AI to mine health data, hospitals can predict and detect sepsis, which ultimately reduces death rates. 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.

Quality control is also a crucial area in which AI is being utilized in diagnostic histopathology. AI algorithms can be used to evaluate the quality of tissue samples and improve the accuracy of diagnoses. This is particularly important for ensuring that patients receive the correct diagnosis and the most appropriate treatment. The integration of Artificial Intelligence (AI) in diagnostic histopathology has the potential to revolutionize the medical field. The application of AI in this area has the potential to bring about significant advancements in the accuracy of diagnoses, speed up the diagnostic process, and enhance the overall patient experience. Digital consultant apps use AI to give medical consultation based on personal medical history and common medical knowledge.

• These applications not only help in the early diagnosis of diseases but also assist in continuous monitoring and adaptive treatment.
• At Binariks we consider the pros and cons of AI in healthcare to ensure the greatest benefit to our partners.
• Using our strong domain expertise, integrated IT-BPM approach, and flexible operating model, improve your business performance and standardise processes that reduce costs.
• The sector creates vast amounts of intricate information – electronic medical records, test results, and numerous studies on conditions and treatments.

On average, for every patient they see, hospital staff must fill out over a dozen forms. New generative AI applications can extract data from patients’ medical records, populate it instantly into forms, record notes from patient sessions, and speed and improve patient communications. The use of AI technologies has been explored for use in the diagnosis and prognosis of Alzheimer’s disease (AD). AI has the potential to revolutionize clinical practice, but several challenges must be addressed to realize its full potential. Among these challenges is the lack of quality medical data, which can lead to inaccurate outcomes.

Is Mailing Medical Records HIPAA Compliant?

By deploying AI at general screenings, Freenome aims to detect cancer in its earliest stages and subsequently develop new treatments. The private-sector commitments announced today are a critical step in our whole-of-society effort to advance AI for the health and wellbeing of Americans. These 28 providers and payers have stepped up, and we hope more will join these commitments in the weeks ahead. Medical AI depends heavily on diagnosis data available from millions of catalogued cases. In cases where little data exists on particular illnesses, demographics, or environmental factors, a misdiagnosis is entirely possible. A 2018 World Economic Forum report projected AI would create a net sum of 58 million jobs by 2022.

Data science technologies can potentially assist in solving global problems, and we already observe some of such first signs. Overcoming antibiotic resistance could be one of the crucial benefits of artificial intelligence in healthcare. Additionally, AI can analyze patient data to identify high-risk individuals who may require more intensive and costly interventions.

AI can help providers gather that information, store, and analyze it, and provide data-driven insights from vast numbers of people. Using this information can help healthcare professionals determine how to better treat and manage diseases. Perhaps the most difficult issue to address given today’s technologies is transparency. Many AI algorithms – particularly deep learning algorithms used for image analysis – are virtually impossible to interpret or explain. If a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. Deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide an explanation.

Natural language processing is also a viable option for a digital consultation in healthcare. It is able to understand complicated sentences other than the selection of predefined options. When medical professionals pursue clinical research, recruitment of trial participants can be the most time-consuming and expensive part of the entire process. This is because it’s imperative to find the right group of people whose health characteristics make them eligible to qualify for each specific clinical trial.

Potential Cons of Using AI in the Healthcare Industry

Moreover, there are ethical considerations regarding the use of AI in exams, such as potential algorithmic bias, privacy issues, and the impact on human jobs. To address these issues, universities must carefully consider the benefits and drawbacks of AI integration and implement strict policies to ensure fair and ethical evaluation of medical students. It is also important for universities to educate students on the importance of academic integrity and ethical considerations related to AI use.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Much of the AI and healthcare capabilities for diagnosis, treatment and clinical trials from medical software vendors are standalone and address only a certain area of care. Some EHR software vendors are beginning to build limited healthcare analytics functions with AI into their product offerings, but are in the elementary stages. Better machine learning (ML) algorithms, more access to data, cheaper hardware, and the availability of 5G have contributed to the increasing application of AI in the healthcare industry, accelerating the pace of change. AI and ML technologies can sift through enormous volumes of health data—from health records and clinical studies to genetic information—and analyze it much faster than humans.

Rules-Based Expert Systems

Our analysis does not indicate a marked strand of the literature; therefore, we argue that the discussion of elements such as the transparency of technology for patients is essential for the development of AI applications. Great advances have been made in using artificially intelligent systems in case of patient diagnosis. They further established that the DCNN achieved performance at par to that of 21 board-certified dermatologists. The advent of high-throughput genomic sequencing technologies, combined with advancements in AI and ML, has laid a strong foundation for accelerating personalized medicine and drug discovery [41]. Despite being a treasure trove of valuable insights, the complex nature of extensive genomic data presents substantial obstacles to its interpretation. The field of drug discovery has dramatically benefited from the application of AI and ML.

Understanding this process and the choices it entails are important for appropriate usage of this automated system. The data used to learn from and the optimization strategy used has a deep impact on the applicability of the AI system to solve a particular problem. An understanding and appreciation of these design decisions is important for medical profession. Due to privacy concerns, data sharing is often inaccessible or limited between healthcare organizations resulting in fragmented data limiting the reliability of a model. Public perception of AI in healthcare varies, with individuals expressing willingness to use AI for health purposes while still preferring human practitioners in complex issues.

The «black-box» nature of some of these AI systems can also make it challenging to understand the basis for their decisions or where responsibility lies in the event of these errors. The increasing prevalence of AI in health care will have significant impacts on the workforce. This discussion guide will help leadership teams understand the implications of AI on workforce strategy and help ensure successful and effective integration into the workforce. Personalized health recommendations, such as tailored diet plans, exercise routines, medication reminders, and preventive care measures can improve population health.

Another forecast suggests that the healthcare AI market could reach a whopping $102.7 billion by 2028. And our goal is simple – help healthcare businesses—from major hospitals to local clinics—tap into the wonders of AI. Even with all their potential, AI tools often get tangled with biases that threaten health equity.

Beyond concerns about the effectiveness of AI, there are also concerns about the potential for bias in the underlying algorithms. Some studies have found race-based discrepancies in the algorithms and limitations due to the lack of healthcare data for women and minority populations. Given the impact that AI and machine learning is having on our wider world, it is important for AI to be a part of the curriculum for a range of domain experts. This is particularly true for the medical profession, where the cost of a wrong decision can be fatal.

For example, AI-driven tools can identify markers of chronic diseases like diabetes or cancer in their early stages, enabling healthcare professionals to intervene promptly and potentially prevent further progression. ClosedLoop.ai is an end-to-end platform that uses AI to discover at-risk patients and recommend treatment options. Through the platform, healthcare organizations can receive personalized data about patients’ needs while collecting looped feedback, outreach and engagement strategies and digital therapeutics.

Most observers feel that the Watson APIs are technically capable, but taking on cancer treatment was an overly ambitious objective. Watson and other proprietary programs have also suffered from competition with free ‘open source’ programs provided by some vendors, such as Google’s TensorFlow. The healthcare industry is data intensive, and the ability to analyse this data is the key to saving lives.

From the time of patient entry into hospital until reporting the result to the clinician, AI has the potential to do wonders. Three pain points in healthcare that blockchain solutions can resolve, plus two challenges that businesses need to respond to for effective implementation of blockchain in health care. The use of intelligent Internet of Things medical devices can allow for data sharing with doctors. For example, the introduction of ECG/EKG wearables by Qardio, an AI health company, reveals an effective method of collecting the most needed information for diagnosis at a short interval. Artificial intelligence can help healthcare professionals significantly reduce time spent on daily office operations and allocate resources on what really matters. With the use of classification and regression algorithms, this powerful technology elevates prognosis and can predict the risk of a certain disease.

Similar robots are also being made by companies such as UBTECH («Cruzr») and Softbank Robotics («Pepper»). In the accounting, business, and management research area, there is currently a lack of quantitative analysis of the costs and profits generated by healthcare organisations that use AI technologies. Therefore, research in this direction could further increase our understanding of the topic and the importance of ai in healthcare number of healthcare organisations that can access technologies based on AI. Finally, as suggested in the discussion section, more interdisciplinary studies are needed to strengthen AI links with data quality management and AI and ethics considerations in healthcare. Third, the authors analysing the research findings and the issues under discussion strongly support AI’s role in decision support.

Personalized Healthcare

In recent years, many healthcare disciplines have been privileged to access various technologies that provide tools for both research and clinical intervention. Despite the above limitations, AI looks well positioned to revolutionize the healthcare industry. AI systems can help free up the time for busy doctors by transcribing notes, entering and organizing patient data into portals (such as EPIC) and diagnosing patients, potentially serving as a means for providing a second opinion for physicians. Artificially intelligent systems can also help patients with follow-up care and availability of prescription drug alternatives.

Addressing the potential exacerbation of existing monopolies within the health care market is, perhaps, one of the most pressing concerns in this digital transition. This scenario places smaller, independent providers at a competitive disadvantage, unable to leverage AI to the same extent in enhancing health care delivery. Such a disparity could widen the gap in care quality and further disadvantage underserved communities. However, it is critical to acknowledge that their effectiveness hinges on the availability of substantial and diverse datasets. Information beyond what is traditionally captured in EHRs and HIEs, such as patients’ social determinants, lifestyle choices, and daily activities, plays a crucial role in their health outcomes.

The company has also partnered with NVIDIA to apply generative AI to its methods, making drug development even faster. AI can help provide around-the-clock support through chatbots that can answer basic questions and give patients resources when their provider’s office isn’t open. AI could also potentially be used to triage questions and flag information for further review, which could help alert providers to health changes that need additional attention. As this area advances, there is more interaction between healthcare professionals and tech experts,” Yang explains.

This AI also transforms claims processing, as RPA can extract and validate data from insurance claim forms. In rehabilitation and caregiving settings, AI-powered robotic exoskeletons and assistive devices help patients regain mobility and independence. These robots can also provide targeted assistance, monitor progress, https://chat.openai.com/ and adjust therapy plans based on real-time feedback and patient data. The benefits of AI in healthcare are evident through the application of Natural Language Processing (NLP). NLP enables computers to understand and process human language, which has several potential uses in the healthcare industry.

Large Language Models (LLMs) are a type of AI algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate, and predict new text-based content [1,2,3]. LLMs have been architected to generate text-based content and possess broad applicability for various NLP tasks, including text generation, translation, content summary, rewriting, classification, categorization, and sentiment analysis. NLP is a subfield of AI that focuses on the interaction between computers and humans through natural language, including understanding, interpreting, and generating human language. NLP involves various techniques such as text mining, sentiment analysis, speech recognition, and machine translation. Over the years, AI has undergone significant transformations, from the early days of rule-based systems to the current era of ML and deep learning algorithms [1,2,3].

However, the integration of AI into medical and dental education is not without its challenges. There may be concerns about the loss of human touch and empathy in medical diagnoses and treatments, and there is a risk that students may become overly reliant on AI and neglect to develop critical thinking and problem-solving skills. Additionally, there may be challenges in ensuring the accuracy and bias-free operation of AI algorithms, which could lead to incorrect diagnoses or treatment plans. Finally, AI algorithms can also be utilized to increase efficiency in diagnostic histopathology. Automating routine tasks in this area can free up pathologists to focus on complex cases and speed up the diagnostic process. This has the potential to greatly enhance the overall patient experience, ensuring that patients receive the care they need as quickly and efficiently as possible.

Machine learning is being used by several pharmaceutical companies, including Pfizer, to find immuno-oncology treatments. They are attempting to identify new combinations of medicinal ingredients for creating novel pharmaceuticals by looking for trends in medical data and examining the effects of current medications on patients. Canadian company BlueDot creates outbreak risk software that mitigates exposure to infectious diseases.[19] BlueDot published the first scientific paper[20] on COVID-19 that accurately predicted the global spread of the virus. Finally, gaining acceptance and trust from medical providers is critical for successful adoption of AI in healthcare. Physicians need to feel confident that the AI system is providing reliable advice and will not lead them astray. This means that transparency is essential – physicians should have insight into how the AI system is making decisions so they can be sure it is using valid, up-to-date medical research.

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Over 13,000 colorectal cancer photos were collected by the researchers from 8,803 participants and 13 separate cancer facilities in China, Germany, and the United States. The researchers then created a machine learning software using photographs that technicians randomly selected. AI is organizing medical data using deep learning and reducing methods to give clinicians and medical researchers a better grasp of the vast repository of medical data. AI is assisting scientists in tracking and advancing medical research by removing redundant methods of data analysis and manual data filtering. This includes processing and analyzing clinical trials to find the effects of vaccines, drugs, and other treatments as well as tracing the origins of virus strains. Side-by-side however, there are unrealistic expectations of what AI can do and what the landscape of the healthcare industry will look like in the future.

• WHO’s vision is to foster digital frontiers and nurture an AI ecosystem for safety, equity, and the advancement of the Sustainable Development Goals, contributing to a healthier world.
• They use AI and cameras built into phones and laptops for remote vital sign monitoring.
• With successful integration, AI is anticipated to revolutionize healthcare, leading to improved patient outcomes, enhanced efficiency, and better access to personalized treatment and quality care.
• One Drop provides a discreet solution for managing chronic conditions like diabetes and high blood pressure, as well as weight management.

With such great potential, it is clear that using artificial intelligence in healthcare holds the promise of a future filled with advancements, improved health outcomes and better patient experiences. AI in the medical field began to gain substantial attention in the early 21st century, with significant advancements in technology and data analysis. This period saw a convergence of increased computational power, the availability of large datasets (Big Data), and significant improvements in AI-powered medical algorithms. The real turning point, however, came with the realization of how AI could address some of the most pressing challenges in healthcare, ranging from diagnostic accuracy to personalized treatment and operational efficiency. For example, NLP can be applied to medical records to accurately diagnose illnesses by extracting useful information from health data. Additionally, it can be used to identify relevant treatments and medications for each patient or even predict potential health risks based on past health data.

Knows how AI works and can design AI models to perform tasks required at a hospital or health system. The explicit rules and knowledge bases help users understand the decision-making process. The ethical framework for AI applications in radiology should consist of biomedical ethics – autonomy, beneficence, justice, nonmaleficence, and explicability[18] [Figure 2]. Considering the volume of health data that can be harvested in an individual’s lifetime, it’s a good idea for tech companies to look for opportunities in wearable health devices. Advanced solutions are fashioned by the use of machine learning to observe and understand unusual network behavior.

The rise of AI in healthcare has been a gradual but steady journey, catalyzed by technological advancements and the increasing demand for improved healthcare delivery. The integration of AI into the medical field has brought about a paradigm shift, making healthcare more efficient, accurate, and personalized. As AI technology continues to evolve, its role in healthcare is set to become even more significant, further solidifying its status as an indispensable tool in modern medicine. This journey of AI from a novel concept to a fundamental aspect of healthcare exemplifies a technological revolution, with the promise of better health outcomes for all.

The time has come to change our mindset from being reactive to being proactive with regard to downfalls of new technology. Here we discuss those challenges focusing more on those that pertain particularly to healthcare. AI algorithms can continuously examine factors such as population demographics, disease prevalence, and geographical distribution. This can identify patients at a higher risk of certain conditions, aiding in prevention or treatment. Edge analytics can also detect irregularities and predict potential healthcare events, ensuring that resources like vaccines are available where most needed.

Where yx is equal to the number of authors producing x articles in each research field. The dominance factor (DF) is a ratio measuring the fraction of multi-authored articles in which an author acts as the first author [53]. The DF is calculated by dividing the number of an author’s multi-authored papers as the first author (Nmf) by the author’s total number of multi-authored papers (Nmt). This is omitted in the single-author case due to the constant value of 1 for single-authored articles. This formulation could lead to some distortions in the results, especially in fields where the first author is entered by surname alphabetical order [55].

PacBio works on everything from solving rare diseases to enhancing the world’s food supply. They aim to create products that let scientists Chat GPT study all genetic variations in any organism. Securonix is changing the game in data security with actionable security intelligence.

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]. Researchers claim that these technologies enhance decision-making, maximize creativity, increase the effectiveness of research and clinical trials, and produce new tools that benefit healthcare providers, patients, insurers, and regulators [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.

Komodo Health has built the “industry’s largest and most complete database of de-identified, real-world patient data,” known as the Healthcare Map. This Map tracks individual patient interactions across the healthcare system, applying AI and machine learning to extract data related to individuals or larger demographics. With this information, healthcare professionals can develop more complete patient profiles while also using categories like race and ethnicity to factor social inequities into a patient’s health history. Once known as a Jeopardy-winning supercomputer, IBM’s Watson now helps healthcare professionals harness their data to optimize hospital efficiency, better engage with patients and improve treatment.

Last year, scientists at Babylon, a worldwide tech business focused on digital health, discovered a novel approach to utilize machine learning to identify the illness. They created new AI symptom checkers in the hopes of reducing diagnosis errors in primary care. The new method solves the limitations of previous versions by incorporating causal reasoning into machine learning. Making crucial health data available via mobile devices can help patients participate in their treatments. Doctors and nurses can be notified of critical changes in patient statuses and crises via mobile notifications. Acquiring reliable information in a timely manner is a vital component in diagnosing and treating medical disorders.

According to the Centers for Disease Control and Prevention, 10.5% of the US population has diabetes. The FreeStyle Libre glucose monitoring system, for instance, allows diabetes sufferers to track glucose levels in real-time, and access reports to manage and review their progress with doctors or support teams. Ai in healthcare is utilized or tested for a variety of objectives, including illness diagnosis, chronic condition management, health service delivery, and drug development. There are presently several applications on the market that employ AI to provide personalized health evaluations and home care recommendations. The app Ada Health Companion uses AI to run a chatbot that integrates the user’s symptoms with other data to suggest a probable diagnosis. Such ML-based early warning systems are helping the general population, healthcare providers, and governments devise countermeasures against the virus.

While there’s still a need for human intervention to make conclusions and give recommendations, it’s highly possible that soon we’ll see these tasks performed autonomously. By developing a comprehensive, AI-enabled digital infrastructure, organizations can speed up and increase the accuracy of diagnostics to provide better personal medical advice. From chronic disease and cancer to radiology and risk assessment, it can be deployed with new AI-based technologies with more precise, efficient, and cost-efficient inventions.

This method allows the function to assume an unlimited distribution; that is, feature can consider values below zero if the data are close to zero. It contributes to a better visual result and highlights the discontinuity in the publication periods [47]. Table 3 shows the information on 288 peer-reviewed articles published between 1992 and January 2021 extracted from the Scopus database. The number of keywords is 946 from 136 sources, and the number of keywords plus, referring to the number of keywords that frequently appear in an article’s title, was 2329. The analysis period covered 28 years and 1 month of scientific production and included an annual growth rate of 5.12%. However, the most significant increase in published articles occurred in the past three years (please see Fig. 2).

WHO’s vision is to foster digital frontiers and nurture an AI ecosystem for safety, equity, and the advancement of the Sustainable Development Goals, contributing to a healthier world. SS and PB, Supervision; Validation, writing, AS and VM; Formal analysis, DC and AS; Methodology, DC; Writing; DC, SS and AS; conceptualization, VM, PB; validation, VM, PB. Additionally, the pink border linking states indicates the extent of collaboration between authors. The primary cooperation between nations is between the USA and China, with two collaborative articles. This mathematical formulation originated in 1926 to describe the publication frequency by authors in a specific research field [61]. In practice, the law states that the number of authors contributing to research in a given period is a fraction of the number who make up a single contribution [14, 61].

The Administration is pulling every lever it has to advance responsible AI in health-related fields. We cannot achieve the bold vision the President has laid out for the country with U.S. government action, alone. However, it’s important to note that specific populations may still be excluded from existing domain knowledge. Although AI has come a long way in the medical world, human surveillance is still essential.

The advancements in AI technology are likely to have a significant impact on the publishing process, offering new and improved ways to manage the peer-review process, enhance the quality of peer review, and enable new forms of publication. One way in which AI is expected to affect the publishing process is by streamlining the peer-review process. With the use of AI algorithms, the publishing process can become more efficient by automating the peer-review process, thereby reducing the workload on human reviewers. This can lead to faster publication times and an improved efficiency in the publishing process. AI algorithms can be employed to analyse large amounts of data and identify patterns that may be missed by human reviewers. This could result in more thorough and accurate peer review and help to identify potential biases in the review process.

AI can predict individual health risks by analyzing patient data and suggesting custom treatment plans. Artificial Intelligence is switching things up in patient care, the development of healthcare applications, drug discovery, and even how we manage costs. The collaborative synergy between AI and medical research holds immense promise for finding innovative treatments, understanding diseases at a deeper level, and ultimately improving healthcare outcomes for patients worldwide. Furthermore, AI can analyze patient data to provide personalized health tips and encourage healthy behaviors. It can also offer insights into potential risks and preventive measures, empowering individuals to take proactive steps toward better health.

These AI-powered applications analyze patient data, medical images, and clinical guidelines to assist healthcare professionals in accurate disease diagnosis and optimal treatment planning. AI is being used to analyze X- ray, CT and MRI scans, to diagnose medical conditions in patients. The computer vision techniques allow programs to detect abnormalities in radiology images, and have reached up to 100% accuracy on test datasets, for several diseases. Not only does AI provide an accurate diagnosis, but also is very time efficient as compared to conservative diagnostic techniques. In the recent COVID-19 outbreak, AI has proved to be a great asset in zero-contact diagnosis, especially for contagious diseases.

In healthcare, they were widely employed for ‘clinical decision support’ purposes over the last couple of decades5 and are still in wide use today. Many electronic health record (EHR) providers furnish a set of rules with their systems today. From predictive and personalized treatment plans to models for early diagnosis, AI is already transforming the healthcare sector in unprecedented ways. However, like any novel and groundbreaking technology, the utilization of AI in healthcare comes with a set of opportunities and trade-offs that keep it both topical and controversial.

Advances in technology have resulted in increased computational and analytic power as well as the ability to store vast amounts of data. Technology such as facial recognition and gene analysis provides a path for an individual to be identified from a pool of people. Patients and the public in general have a right to privacy and the right to choose what data, if any, they would like to share. Data breaches now make it possible for patient data to fall into the hands of the insurance companies resulting in a denial of medical insurance because a patient is deemed more expensive by the insurance provider due to their genetic composition. Patient privacy leads to restricted availability of data, which leads to limited model training and therefore the full potential of a model is not explored. Furthermore, these tools can always be available, making it easier for patients to access healthcare when needed [84].

Several pharmaceutical companies like Pfizer are applying machine learning, in search of immuno-oncology drugs. By finding patterns in medical data, and studying the outcomes of existing drugs on patients, they are trying to discover new combinations of drug ingredients for developing novel drugs. With some very advanced projects like ChemGAN currently available online, researchers and AI engineers are trying unprecedented techniques for discovering new drugs and vaccines to combat chronic and several other illnesses. To enhance the performance of predictive AI models for population health management purposes, it is important that AI systems access and analyze considerably larger and more varied datasets. This could be feasibly achieved through the integration of information gathered from wearable technologies and smart devices. Such devices can continuously monitor and record a wealth of health-related data, offering a more comprehensive view of a patient’s health profile.

However, it is crucial to ensure that AI-based guidelines are transparent, fair, unbiased, and informed by human expertise and ethical considerations [68]. Furthermore, a study utilized deep learning to detect skin cancer which showed that an AI using CNN accurately diagnosed melanoma cases compared to dermatologists and recommended treatment options [13, 14]. Researchers utilized AI technology in many other disease states, such as detecting diabetic retinopathy [15] and EKG abnormality and predicting risk factors for cardiovascular diseases [16, 17]. Furthermore, deep learning algorithms are used to detect pneumonia from chest radiography with sensitivity and specificity of 96% and 64% compared to radiologists 50% and 73%, respectively [18].

By taking off some of these responsibilities from human healthcare providers, virtual assistants can help to reduce their workload and improve patient outcomes. By analyzing large datasets of patient data, these algorithms can identify potential drug interactions. This can help to reduce the risk of adverse drug reactions, and cost and improve patient outcomes [59]. Another application of AI in TDM using predictive analytics to identify patients at high risk of developing adverse drug reactions.

That’s why Mayo Clinic is a member of Health AI Partnership, which is focused on helping healthcare organizations evaluate and implement AI effectively, equitably and safely. A helpful comparison to reiterate the collaborative nature needed between AI and humans for healthcare is that in most cases, a human pilot is still needed to fly a plane. Although technology has enabled quite a bit of automation in flying today, people are needed to make adjustments, interpret the equipment’s data, and take over in cases of emergency. But assessing total kidney volume, though incredibly informative, involves analyzing dozens of kidney images, one slide after another — a laborious process that can take about 45 minutes per patient.

Table 2 indicates the currently known literature elements, uniquely identifying the research focus, motivations and research strategy adopted and the results providing a link with the following points. Additionally, to strengthen the analysis, our investigation benefits from the PRISMA statement methodological article [37]. Although the SLR is a validated method for systematic reviews and meta-analyses, we believe that the workflow provided may benefit the replicability of the results [37,38,39,40]. Figure 1 summarises the researchers’ research steps, indicating that there are no results that can be referred to as a meta-analysis.

Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Each of these AI technologies brings unique capabilities and benefits to the healthcare landscape, revolutionizing patient care, data analysis, decision-making, and administrative processes. These advancements are transforming the healthcare industry and driving better outcomes for both patients and providers. AI in healthcare is the use of machine learning, natural language processing, deep learning and other types of AI technology in the health field.

Additionally, this should happen especially in the poorest countries around the world, where there is a lack of infrastructure and services related to health and medicine [96]. On the other hand, it might be interesting to evaluate additional profits generated by healthcare organisations with AI technologies compared to those that do not use such technologies. This study aims to provide a bibliometric analysis of publications on AI in healthcare, focusing on accounting, business and management, decision sciences and health profession studies.

AI in healthcare can help healthcare providers with various administrative and patient care tasks, enabling them to improve existing solutions and address challenges faster. Although most AI and healthcare technologies are beneficial in the field of healthcare, support tactics for hospitals and other healthcare organizations can differ significantly. These systems often rely on collecting and analyzing personal data to provide accurate diagnoses or personalized treatment recommendations. However, if not appropriately safeguarded, this data could be vulnerable to breaches, misuse, or unauthorized access. Striking the right balance between utilizing patient data to advance healthcare and ensuring robust privacy protections is essential for fostering trust in AI applications. AI, or artificial intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence.