AI in healthcare: A narrative review

Introduction

The healthcare industry is undergoing a revolution. The reasons for this revolution are the ever-increasing total spending on health care and the growing shortage of health care professionals. This drives to situation that healthcare industry wants to adopt new Information Technology based solutions and processes with advanced technology which could reduce costs and provide solutions for these emerging problems.

Before 2010, healthcare technology companies were focusing on the innovations provided by medical products providing historic and evidence-based care. Starting from 2010 development has focused on real-time medical platforms and outcome-based care. From 2020 technology is moving towards medical solutions delivering intelligent solutions for evidence- and outcome-based health where focusing is in collaborative and preventative care. These intelligent solutions can be achieved by using robotics, virtual and augmented reality and AI¹. In a recent life science executives survey 69% of life science businesses are already piloting or adopted AI in their solutions and 22% are evaluating or planning to pilot AI solutions². The annual savings potential by using AI in healthcare can be $150 billion by 2026 in US alone, and this should be also one of the factors to speed up the implementation of AI in healthcare sector³. Future studies will give answers if this savings will be achieved. There are multiple subdomains in healthcare domain where AI-based services are utilized.

AI applications in healthcare can be classified to the following domains: surgery, nursing assistant, medical consultation, administration and workflow, treatment design, cybersecurity, machine vision, automatic and preliminary diagnosis, health monitoring, medication management, and clinical trials. All these domains have applications utilizing AI in their operations. We focused on quantitative studies in which we have collected some services using AI in the healthcare sector by collecting results in publications such as PubMed, Nature Biomedical Engineering, Oxford University Press, Food and Drug Administration, and reports reported by PWC and Accenture. From these articles and reports, we made a comparative analysis of the effectiveness and outcome of the use of AI compared to services that do not use AI. As a result, in this paper we introduce AI-based services for healthcare sector that have a high impact in care outcome and propose collection of services which could provide best outcome in preventive healthcare and in clinical work.

Services utilizing AI in healthcare domain

In this section we review healthcare services which are utilizing AI. These services include healthcare services and supporting services which are shown in Figure 1. The reviewed services are collected from research papers and market analysis companies³. Services which are selected for analysis are providing instant care or providing direct support for care. These are robot-assisted surgery, clinical trial participation, virtual assistants for nursing and consultation, image diagnosis, dosage error reduction, medication management, preliminary diagnosis, and health monitoring. Some of the topics which have been studied in my earlier research and which provide indirect services for healthcare processes but are not affecting directly to patient care, such as fraud detection, assistant for administration and workflow, cybersecurity, connected machines, and drug creation are excluded from this study. We also categorized healthcare services and applications based on degree of AI utilization and human involvement in clinical decision making⁴.

Figure 1. Categories of AI applications in Healthcare.

Below we provide a short description of AI-methods which are commonly utilized in healthcare applications and services. Common applications for AI methods are shown in Figure 2.

Figure 2. AI methods and applications.

Machine learning (ML) is a subfield of AI and presents AI solutions that are adaptive. The ML can be roughly categorized to three subareas. Supervised learning algorithms build a mathematical model of a set of data containing both the desired outputs and the inputs⁵. In unsupervised learning algorithms take a data set containing only inputs and find structure in the data, such as grouping or clustering of data points. In unsupervised learning the algorithms learn from the test data that has not been classified, labeled or categorized⁶. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of discrete time stochastic control process. Reinforcement learning is used, for example, in situations where a self-driving car is operating in an environment where feedback about good or bad choices is not available in real time.

Natural language processing (NLP) is a subfield of AI that consist of tools and techniques to enable computers to read, understand, and derive meaning from human languages and enable natural interaction between computers and humans to make computer systems understand and manipulate natural languages to perform desired tasks. In healthcare, NLP is used, for example, to predict diseases based on patient’s own speech and electronic health records⁷.

Neural network (NN) consists of digitized inputs, such as an image or speech, which proceed through several hidden layers of connected artificial neurons, each layer responding to different features progressively detecting features and provide an output. Deep NNs (DNN) require special mention because as a subcategory of NN with its variations, such as recurrent, convolutional, transfer, generative adversarial, reinforcement, representation, and transfer, are used in various AI solutions in field medicine. Typical use case for DNN is when there is need for interpret data to certain patterns from different types of clinical images such as pathology, skin lesions, retinal, and endoscopy images and to find out patterns from datasets, such as medical scans, electrocardiograms, and vital signs.

Deep learning (DL) is a subfield of ML. DL is based on artificial NNs (ANNs), inspired by information processing and distributed communication nodes in biological systems, that use multiple layers to progressively extract higher level features from raw input. DL can be semi-supervised, supervised, or unsupervised. The term 'deep' refers to the number of layers (depth) through which the data is transformed. DL is a form of AI that enables computers to perform tasks based on existing data relationships.

Machine vision/computer vision (MV/CV) include the methods and the technology which is used to automatically extract information from an image. The extracted information can be a i.e. good-part / bad-part signal or a set of data such as the identity, orientation, and position of objects in an image. Extracted information can be later used for applications like automatic inspection, security monitoring, industry robots and process guidance, and vehicle guidance.

In the next sections we provide detailed information about topics in healthcare AI. Summary of these techniques are collected to Table 1 where we provide studied information about AI based services which are in use in healthcare business. Studied applications / services information is limited to Healthcare domain and include the following information: service / application type, category of AI service, application / service type, AI features which have been used in application / service, outcome as a result of AI utilization, and metrics which have been used to present the outcome.

Robotic-assisted surgical systems (RASS) and computer-assisted surgery (CAS)

The RASS have been used since year 1985⁸ in multiple fields of surgery including cardiac surgery, thoracic surgery, gastrointestinal surgery, gynecology, orthopedic surgery, spine surgery, transplant surgery, urology, and general surgery^9–16. RASS have been categorized to tree types based on the level of autonomous activity: supervisory controlled surgery, telesurgical, and shared control surgery.

In traditional surgery, surgeons can operate only on what their eyes can see and basically the only way to see inside a patient is to operate by open surgery. With RASS, surgeon can utilize cameras and tools which can be inserted through a small incision to perform procedures with exquisite precision. The minimally invasive approach is meant to provide faster patient recovery times and reduce postoperative complications. RASS can also lessen the physical burden of surgery staff. With RASS there is also possibility to collect all data and details such as video recording of the surgery, all movements and cutting and sewing actions of ongoing surgery, and use this collected data for further analysis and enhance and lean the surgery process. There are RASS manufacturers which are providing robotic surgery equipment worldwide. In this paper, we provide details about the one of the most used RASS at the time of writing this paper, DaVinci, developed by Intuitive Surgical Inc. DaVinci had 5406 installed bases in September 2019¹⁷.

CAS is a second approach to assisted surgical systems. Where RASS is focusing the physical surgery robot, its controlling and applications, and robot assisted surgery technology, CAS technology use computer technology for surgical planning and enhances surgical guidance to surgeon. CAS involves techniques that can directly participate in surgery or can assist in the navigation or positioning of surgical instruments¹⁸. CAS contain a set of applications used at the surgical workplace preoperatively, intraoperatively, as well as improving surgical efficiency and efficacy postoperative surgery¹⁹. Main CAS features contain creation of virtual image from the patient, diagnostic, image analysis and processing, preoperative planning, surgical simulations, and surgical navigation. CAS have been major factor in the development of RASS.

When evaluating the clinical effectiveness of robotic surgery technology, a systematic review from 95 studies made in Canada in 2011 indicated that RASS in prostatectomy, hysterectomy, nephrectomy, and cardiac surgery compared to open or laparoscopic surgery there are many benefit in clinical outcomes which were: reduction of length of hospital stay, reduction of blood loss and transfusion rates, and reduction of complications²⁰. There are some RASS operations in which operation times are reduced such as laparoscopic prostatectomy and, in some operations, increased such as open prostatectomy and open hysterectomy. In the same review, the economical evidence was also evaluated in prostatectomy, cardiac surgery, nephrectomy, and hysterectomy. Cost analysis showed that shortening the lengths of stay after robotic radical prostatectomy also produced reduction of hospitalization costs relative to laparoscopic surgery and open surgery. In other hand, acquiring and operational costs of surgical robots will lead to situation that approximately 75% of the surgeries in which robot assisted the surgery is more expensive. However, costs can be reduced by higher utilization rate of RASS.

A systematic review was made in 2017 to evaluate patient benefits, cost, and surgeon conditions when using RASS in gynecological oncology²¹. Using RASS as part of treatment in cervical cancer, endometrial cancer, and ovarian cancer were studied by reviewing total of 76 references. Results were that safety in oncological surgery is similar compared with previous surgical methods, but RASS will also increase overall costs because of high RASS equipment application, acquisition, and maintenance costs.

In the research where cost and efficacy of robotic hepatectomy was studied there was indication that in RASS group operative time was 20% longer, length of stay was 35% shorter, perioperative costs were 10% higher, and postoperative costs 36% lower compared to group with normal open surgery. In the study, overall costs of RASS were 22% lower than in open surgery. Complications were similar between RASS and open surgery patient groups⁴¹.

Virtual nurse assistants (VNAs) for healthcare

In modern digitalization, healthcare organizations and actors in healthcare processes have taken in use virtual assistants which have already been emerged to use in other business sectors. With virtual nurse assistants, hospitals can reduce sudden hospital visits and reduce the workload of healthcare professionals. These virtual assistant applications can listen, talk, and give advices/recommendations. Over the last two decades there have been studies of embodied conversational agents (ECAs) use in healthcare which have proven significant improvement in healthcare outcome when chatbots or conversational agents are in use. A majority of these ECAs have allowed user input which is used in common surveys such as multichoice or utterance. With latest advance in AI technologies, such as NLP, machine learning (ML), and neural networks (NN), have made possible to develop virtual assistants or virtual agents which are capable of utilizing conversational systems which can mimic human conversation^42,43.

One widely used platform meeting European Medical Device Directives is Your.MD. This virtual health assistant using AI and machine learning utilizes United Kingdom National Health Service (NHS) data for providing personalized pre-primary care. Pre-primary will act before patient makes decision to access primary care. With Your.MD patients can perform home diagnosis by using mobile app or website. With Your.MD benchmark tests in verified test cases from Harvard University and Royal College of General Practitioners have shown medical accuracy of 85% for 20 most common conditions²².

In another study there was multiple symptom assessment apps studied, where the best results for condition coverage, accuracy of suggested conditions and urgency advice performance was measured with 200 vignettes representing real world scenarios and compared to five general practitioners (GPs). The best results were received by service ADA getting condition suggestion coverage of 99% and top-3 conditions suggestion 70.50% (GPs average 82.10%), 97% accuracy for safe urgency advice (GPs average, 97%)²³.

Another VNA platform is Sensely. Its digital nurse avatar use machine learning algorithms. It utilizes patient’s medical history data, and it can monitor the condition of patient. Additionally, the VNA can keep track of appointments, fill the gap between doctor visits and predict follow-up treatments. Platform was trialed in 2019 with 72 chronic heart failure patients in clinical site. Findings indicated that platform was able to decrease readmission rate by 75% and patient monitoring costs by 66% compared to traditional care process²⁴.

Medication management and medication error reduction (MMMER)

The effective MMMER services can provide remarkable healthcare cost expenditure reduction and can minimize unnecessary injuries and deaths. The estimated annual costs of drug-related mortality and morbidity resulting from nonoptimized medication therapy was $528 Billion in US. This is equivalent to 16% of total US health care expenditures in 2016⁴⁴. Prescription drug errors cause substantial morbidity, mortality, and wasteful health care cost. In a National Audit Commission report there was evaluated that there are 7000 deaths annually in US due to medicine misuse and due to medication mistakes highlighting importance and urgency of preventive measures⁴⁵. AI has multiple use cases in Medication management and dosage error reduction, such as improving medication safety, preventing drug overdoses, predict health risks and outcomes across large populations, reduce time and expenses, and monitor medication adherence.

Improving medical safety. MedAware created system for detecting medication errors. In this ML-based system researchers concluded that it was clinically useful in flagging 75% of potential medication errors or issues, with 18.80% classified as having medium clinical value, and 56.20% of the valid alerts as having high clinical²⁵.

Monitoring medication nonadherence. Medication nonadherence is significant issue in healthcare costs and healthcare outcome. Medication nonadherences contribute between $100 and $300 Billion dollars in US⁴⁶. One study using AI platform on mobile devices in measuring and increasing stroke patients’ medication adherence when ongoing anticoagulation therapy. Study indicated that in the intervention group medical adherence was 100% and in the control groups only 50%. The AI application was visually identifying the patient, the medication, and the confirmed ingestion. Adherence was later measured by plasma sampling and pill counts²⁶. Another adherence monitoring AI platform utilizing neural network algorithm in machine vision was studied in clinical. Machine vision and neural network was used to identify visually patient, the drug, and confirm the ingestion of the drug. Study shows that adherence was 17.90% higher in AI group than standard-of-care modified directly observed therapy protocol²⁷.

Clinical trial participation (CTP)

As described by United States Food and Drug Administration (FDA) and U.S. National Library of Medicine, in a clinical trial, participants are receiving specific interventions based on the research plan or protocol which is created by the investigators or researchers. These interventions may be medical products, such as drugs or devices, procedures, or changes to participants' behavior, such as diet. Clinical trials have three models. 1. Comparing new medical approach to a standard model or approach that is already available. 2. comparison new approach to a placebo containing no active ingredients. 3 comparison to new approach where no intervention is done⁴⁷. It is also notable that on average it takes 10 - 15 years and 1.5 - 2.0 Billion US dollars to develop and bring a new drug to the market. In drug development, approximately 50% of the time and investment is used for the clinical trial phases⁴⁸.

As a background to create more comprehensive clinical trials, for example, in one research done in Mayo Clinic participation for clinical trials of cancer patients was only 3-5% even though up to 20% were eligible²⁸. AI can help in clinical trial design and it can be used in finding patterns of meaning automatically from large and unstructured datasets such as speech, text, or images. Natural language processing NLP understands and correlates content in spoken or written language, and in human-machine interfaces) allowing natural information exchange between humans and computers. These capabilities are used for correlating diverse and large datasets such as medical literature, electronic health records (EHRs), and databases for improving patient-trial match and recruitment or persons before starting actual trial. During actual clinical trial AI can be used monitor patients continuously and automatically. Moreover, AI utilization provide improved adherence, efficient endpoint assessment and increased control and yielding reliability. Based on this background information and need for enhancing the clinical trial participation rate there was a research conducted by IBM. In this research IBM utilized Watson artificial intelligence platform resulting 80% increase in oncology clinical trial enrollment. This increase was observed at Mayo Clinic. This new platform enabled high volume screening very efficiently²⁸.

Mendel.ai research network has created Clinical trial participation pre-screening service for identifying patients which were potentially eligible for clinical trial. In one research Mendel.ai was used to retroactively provide pre-screening two oncology studies, one for breast cancer and one for lung cancer. In trials where Mendel.ai was used it resulted in a 24 - 50% increase compared to standard practices to correctly identify the number of patients as potentially eligible for clinical trials. All patients who were correctly identified by standard practices were also identified by Mendel.ai. With standard pre-screening practice an average of 263 days for lung cancer and 19 days for breast cancer patients elapsed between actual patient eligibility and identification. Respectively, detection of potential eligibility with Mendel.ai took only minutes²⁹.

Preliminary diagnosis and prediction (PDP)

For decades diagnosis services have been using health history data and diagnosis data to provide more accurate diagnosis for the patient and more accurate health prognosis. With current advances in AI research we have found that AI have outperformed physicians in speed and accuracy of medical diagnosis in some fields of healthcare sectors as described in following example studies from various fields of healthcare.

Medical imaging and image diagnostics (MID)

Medical imaging data is one of the best sources of information about patients and at the same time the most complex. Traditionally interpreting medical imaging scans is a highly skilled, manual job requiring many years of training. In the field of medical imaging the AI technologies, such as DNN and DL, can produce remarkable improvement in healthcare outcome and have proven to provide enhancement in speed, accuracy, and cost reduction in interpretation of medical images. MID can be utilized in healthcare for multiple cases, such as for identifying cardiovascular abnormalities, detecting musculoskeletal injuries, identifying neurological diseases, identifying thoracic complications, and common cancers screening. In the business perspective AI have high potential in MID and it is estimated to rise from $21 billion in 2018 to a value of $265 billion by 2026⁴⁹.

Patient health monitoring (PHM)

Continuous PHM can reduce patient length of hospital stay and can increase recovery time and reduce mortality rate. In home use, PHM can provide information, instructions, and reminders for preventive care. In this paper, PHM consist of services and solutions using AI methods for continuous healthcare monitoring, healthcare assessment tools, and symptom checking solutions which can be used by patients and healthcare professionals periodically or continuously. HM services are utilized in hospitals or in home. When evaluating the user acceptance of HM services in our earlier research we found that AI-based health condition monitoring solutions were evaluated as the best of 15 as most considerable technologies for healthcare and the second-best service among 15 services what healthcare professionals are willing to use⁵¹.

Key elements for successful implementation of AI-based services in healthcare

Our research was using only a fraction of healthcare services and applications which has been studied globally. In our review we focused on research projects or healthcare services which are commonly in use by healthcare service providers and from these we also focused to services in areas which are focusing on actual care processes. From these areas we found that there are thousands of research articles from each area of healthcare where AI methods are used to provide enhancement to healthcare. We also selected the services which have been evaluated as most considerable AI services by healthcare professionals⁵¹ and by general public⁵² in earlier studies. Some areas from healthcare administration systems, for example, fraud identification, connected machines, information management, and data security were left out from this review.

Based on this review and our earlier studies we have noticed that there are multiple use cases where AI methods can be used to provide enhancement to healthcare processes. AI utilization can: 1) save time spent in healthcare work 2) give accurate diagnosis 3) make findings from medical images and medical reports 4) monitor health conditions and predict occurrence or progress of diseases 5) provide more quality to care 6) reduce complications in surgical operations 7) control medical adherence and medication misuse and 8) provide help in clinical decision making. Identified benefits are collected in Figure 3.

Figure 3. Benefits of AI methods in healthcare.

When considering the success factors for implementation of AI methods to healthcare services we propose that successful implementation to actual healthcare use require at least:

Conclusion and future work

As a conclusion of our systematic review we found out that AI can have remarkable possibilities in reducing healthcare costs, providing preventive healthcare, ease the work burden of healthcare professionals, and providing more accurate diagnosis faster and easier. The need for AI services arises in the facts that healthcare costs are continuously increasing. Additionally, age structure of population is changing, especially in developed countries, causing that there will be more chronic diseases within aged population needing expensive care. There will be also shortness of trained nurses and healthcare professionals. Moreover, access to modern and effective healthcare services are not available especially to poor and elderly people and for most of the population living in developing countries. When AI methods are used in healthcare research and IT processes in full scale, we can achieve remarkable savings in overall healthcare costs and same time improve health outcome and quality of life. The need for enhancement provided by AI methods can be seen in every studied healthcare service areas.

Moreover as conclusion we have identified and proven that there is very high potential for the state-of-the-art AI solutions in almost every healthcare sector to reduce costs, ease healthcare professionals workload, improve quality of life for patients, provide preventive health and improve overall health outcome. There are also AI based solutions which can be utilized for population in developing countries. These services include preliminary diagnosis, preventive health services, patient health monitoring services and virtual nurse assistants. In addition, it must also be emphasized that artificial intelligence solutions can produce $150 billion savings in global healthcare industry by 2026³. Based on these findings we can express that investing to AI in healthcare will pay for itself. We recommend all healthcare IT service development companies and research organizations to fully adopt scientifically validated AI methods in their research and development projects.

When developing and implementing new applications and services for industrial purposes and especially for healthcare industry safety and quality of service are also key elements for successful new service implementation. To maintain and enhance quality AI method developers need to fulfill standards, regulations and solve possible emerging legal constraints. During the research we found out that United States Food and Drug Administration have proposed a regulatory framework for AI and ML-based technologies that consist of new issues in utilizing AI in healthcare⁵³. This new framework should be utilized when developing new AI based technologies to EU markets until EU Medical Device Regulation (MDR) will create own regulatory framework for AI and ML-based technologies.

Moreover, one recommended to healthcare AI service developers in AI methods implementation is to join into interest group and evaluation framework. There have been established the Focus Group on artificial intelligence for health which is a joint initiative of the World Health Organization and International Telecommunications Union that brings together academia, industry, and governmental stakeholders to drive the application of AI in health by establishing an evaluation framework⁵⁴.

In our future work we design a model for novel state-of-the-art AI platform to be utilized by all healthcare service providers and healthcare IT services developers. Encouraged by the results of this research and as future work we continue the research and design of novel platform where AI methods and pre-trained AI methods can be utilized and adopted to healthcare IT services by an cloud-based, open access, easily integrated platform which use open access and even proprietary health data repositories, national health databases, hospital information systems and imaging databases as learning data and constantly evolving AI methods for providing fast, automated, and accurate diagnosis & prognosis.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.