Artificial Intelligence Applications in Decision Making for Disease Management

doi:10.21203/rs.3.rs-3110088/v1

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Artificial Intelligence Applications in Decision Making for Disease Management

https://doi.org/10.21203/rs.3.rs-3110088/v1

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Background: Artificial intelligence (AI) has several potential applications in medicine, creating opportunities for reliable and evidence based decision making in disease management. Thus, the practical aspects of AI in decision-making should be identified. This study was conducted to identify AI applications in decision making for disease management.

Method: This study was a systematic review using the PRISMA-ScR checklist. Data collection was carried out by searching the related keywords in WOS and Scopus in May 2023.

Results: Regarding the AI applications in decision making for disease management, we found 80 sub-themes which were categorized into six themes, i.e. 1) Processing and managing data, 2) Characterization and analysis, 3) Prediction and risk stratification, 4) Screening, 5) Prognosis, and 6) Diagnosis.

Conclusion: AI has considerable capability in disease treatment and would be an integral part of medicine in the future. This study clearly identified six main themes that addressed AI capability in decision making for disease management. The use of AI can help in making medical decisions with more trust and confidence and thus make medical interventions more accurate and effective.

Artificial Intelligence (AI)

Decision Making

Diseases

Disease Management

Screening

Prognosis

Diagnosis.

Recently, there has been great discussion on the impact of artificial intelligence (AI) and many are fascinated by how its capabilities and affordances are revolutionizing a part of human life every day. Artificial intelligence has modernized traditional ways of doing things and offers easier, more efficient and cost-effective solutions.

AI generally refers to utilizing a computer to model intelligent behavior with minimum human intervention (1). While AI has proven to have many potential applications in medicine, it still remains unknown which of these will have a greater impact in the field. Nevertheless, it is certain that change is inevitable. As a result, it is essential for all physicians to become knowledgeable of the latest developments of AI, as it most probably will impact the future practice of healthcare (2).

Over the past five decades, there have been dramatic advancements in AI and the field of medicine. With the advent of Machine Learning (ML) and Deep Learning (DL), there has been an expansion of AI applications in medicine, paving the way for personalized medicine compared to algorithm-only–based medicine. The future holds the possibility of using predictive models for disease diagnosis, predication of therapeutic response, and ultimately preventative medicine. AI has the potential to enhance diagnostic accuracy, effectiveness in provider workflow and clinical operations, enhance disease and therapeutic monitoring, and improve procedural accuracy and overall patient outcomes (3).

Medicine as an application domain is among the greatest challenges of AI/ML/DL. Medical decision support leaves us with an ample uncertainty, unknown, incomplete, imbalanced, heterogeneous, erroneous, inaccurate and missing data sets in arbitrarily high-dimensional spaces(4).

AI in medicine can be divided into two subgroups, including virtual and physical. The virtual group extends from applications such as electronic health record systems to neural networkbased guidance in treatment decisions. The physical group encompasses those associated with robotic-assisted surgeries, intelligent prostheses for handicapped people, and elderly care (5). In the near future, AI will be evolving into the public health sector and will have a significant impact on every feature of primary care. Computer applications enabled with AI will assist primary care physicians to more readily identify patients who require extra care and provide personalized protocols for each patient. They can use AI to take notes, evaluate their consultation with patients, and directly register required information into EHR systems. By using these applications, primary care physicians will be facilitated in collecting and analyzing patient data, while gaining insight into patient’s medical needs(5).

As AI will be an essential aspect of medicine in the near future, the new generation of medical trainees will need to be trained on AI concepts and applicability. They will need to get acquainted with how these applications function productively in a workspace together with machines for better efficiency, while cultivating soft skills such as empathy (5). The future of AI and its implementation in clinical practice anticipates a promising area of development, one which will rapidly evolve along with the other modern fields such as precision medicine, genomics and teleconsultation(6).

It is well known that decision-making ability includes different aspects, i.e., understanding the information and input received, recognizing its significance, reasoning about the costs and advantages of various courses of action, and communicating the decision made. Although researchers use terms such as understand, recognize, and reason in different ways, this is the definition generally acknowledged by the medical community(7). In the event a clinician does correctly identify a patient without decision-making capacity, evidence indicates that they are often unsuccessful in matching their treatment plan to the patient’s preferences. Possible reasons for this nonfulfillment are multifaceted and include clinicians’ inability to synthesize information about the patient and cognitive biases existing in the hospital environment(7).

AI provides abundant applications for decision making in disease management. For example, Loftus et al. (2020) found that integrating artificial intelligence with surgical decision-making has the ability to revolutionize care by augmenting operating decisions, the informed consent process, identifying and mitigating modifiable risk factors, postoperative management decision-making, and shared decisions on resource use (8).

A number of studies have addressed the application of AI in decision making for disease management. However, it seems that there is considerable gap in the literature concerning AI’s ability for decision making in disease management. One way to find the AI application in this field and present a comprehensive scenario regarding AIs ability for decision-making in disease management is a survey of the literature. Thus, this systematic review was conducted to identify the AI applications for decision-making in disease management.

The study was a systematic review in which the PRISMA-ScR checklist was used. The search was conducted in Web of Sciences (WOS) and Scopus databases, without any time limitation during the search. The following search strategies were used:

“Artificial intelligence” AND “Decision Making” AND (Disease OR Illness)
“AI” AND “Decision Making” AND (Disease OR Illness)

A total of 1900 articles were retrieved after the initial search from databases. There were 393 duplicates which were removed. Among the 1507 remaining articles, 617 articles did not have abstracts and were excluded. Also, 27 articles were excluded because the full texts were not available in the databases. Afterwards, 863 articles remained for further screening and were assessed for eligibility. From 863 articles, 786 articles were excluded due to irrelevant topics, insufficient information and not providing specific applications of AI in decision-making for disease. Ultimately, 77 articles remained to be included for further analysis.

The inclusion criteria were articles that: 1) discussed AI application in decision making for disease detection, prognosis, screening, and diagnosis 2) discussed AI in processing and managing data, i.e. understanding and integration of data. The exclusion criteria were: 1) articles related to theoretical discussions about AI in decision-making and 2) articles related to the other topics in the process of disease treatment besides decision-making, such as treatment, follow-up, etc.

The search strategy and inclusion process are shown in Figure 1.

Figure 2 shows the trend of publications during these years. As this figure shows, the number of publications addressing AI application in decision making for disease management in this period of publication has increased. The figure also shows that between 2011 and April 2023, the highest number of publications was related to the year 2022. It is anticipated that this rising trend will continue through 2023 and onwards.

The results concerning the countries where the studies were conducted show that articles were published in 26 countries including the USA (18 articles), China (10 articles), India (8 articles), Saudi Arabia and England (each with 5 articles), Egypt (3 articles) and other countries (each with one or two articles).

Based on the latest classification of the World Trade Bank in 2019-2020, 39 studies were conducted in high-income countries, 22 studies were conducted in average-to-high income countries, and 16 studies were conducted in average-to-low income countries. These findings suggest that there has been a higher intention to investigate AI applications in decision-making for disease management in high-income countries.

Table 1 shows the themes and sub-themes of AI applications in decision making for disease management. As this table shows, these themes can be categorized in six main themes: 1) Processing and managing data, 2) Characterization and analysis, 3) Prediction and risk stratification, 4) Screening, 5) Prognosis, and 6) Diagnosis. This table also shows that 80 sub-themes were extracted from the studies, which were categorized in seven themes.

Table 1. Artificial Intelligence applications in Decision Making for Diseases Management

Theme	Subtheme
Processing and managing data	Analysis and integration of large datasets Analysis of biofluid biomarkers in retinal vein Analysis of large-volume, unstructured medical data Analysis of raw image data from cardiac imaging techniques Big data analysis Data capture/acquisition, data processing and, data analytics Effective medical data classification models Enhance our understanding of data Extract valuable information Insights from the massive amounts of available data Integration of big multimodal data to generate models Integration of different types of data from various sources Lung ultrasound (LUS) image reading Manage and analyses of enormous biomedical datasets Mining of vital data Process and use the information in urine Process huge amounts of data Quantitative interpretation of patients' information Scaling up the information processing
Prediction and risk stratification	Accurate prediction of Cardiovascular disease Classification of coronavirus disease 2019 (COVID-19) medical images Deep learning-based decision-tree classifier Develop predictive models from large datasets Disease prediction and risk stratification Forecast the occurrence of chronic kidney disease Identification of bleeding to predict outcomes Prediction of infectious diseases Predict aging-related diseases and issues Predict disease risk for anomaly detection Predict the onset of gastroesophageal reflux disease (GERD) Predict the risk of mortality Predict events in the future Predict the spread of disease Predict disease risk, diagnosis, prognosis Prediction of eye diseases Predictive model for eye diseases Stratification strategies for each stroke patient
Screening	Improve the screening, diagnosis and classification of the severity of PAD Supports screening process and enhances the detection efficiency Discriminating scalp psoriasis and seborrheic dermatitis Colorectal cancer screening Screening and staging of colorectal cancer Screening Optical Coherence Tomography (OCT)
Prognosis	Precise prognosis predictions Prognosis analysis of genetics and genomics Prognosis of Optical Coherence Tomography (OCT) Prognosis prediction of common liver diseases Prognostic assumptions Prognostic decision-making in a clinical setting Rapid progression of pneumonia lesions
Diagnosis	Diagnosing myocardial infarction (MI) Diagnosing periapical lesions Diagnosing thyroid nodules Diagnosis and staging of prostate cancer (PCA) Diagnosis of oesophageal cancer Diagnosis of outcomes for heart failure (HF) Diagnosis and treatment evaluation Diagnostic assumptions Diagnostic decision-making in a clinical setting Endodontic diagnosis and therapy Improve diagnosis, advance understanding of disease pathogenesis Neurological diagnostic support and novel image recognition technologies
Detection and quantification	Automated detection and quantification Automatic detection of thyroid diseases Covid-19 detection and prevention Detect and classify different classes of colorectal cancer (CC) Detecting and diagnosing periapical lesions Detection to minimize incomplete colon capsule endoscopy (CCE) Detection of coronavirus disease 2019 (covid-19) medical images Detection of disease and differentiation of pathology Detection of malignant arrhythmias Detection, determination, and disease prediction Early detection of cardiac events Early disease detection Early fertility detection Non-invasive detection of atherosclerotic coronary artery aneurysms (CAA) Proper detection and treatment of oral cancer (OC) Recognition discrete

This systematic review was conducted to identify the AI application in decision making for disease management. Based on the findings of the current study and the literature, the important and progressive role of AI in prediction, screening, prognosis, diagnosis, detection, processing and managing data of diseases, and an array of reliable suggestions for disease management, should be addressed.

The role of AI in “processing and managing data” is the first theme that was extracted based on an analysis of the studies. Big data analysis, data capturing /data acquisition, integration, classification, processing, understanding and mining are the prominent AI applications in processing and managing data of diseases (9–28). Data and data capacity analysis have a considerable role in decision making in disease management by clinicians. Also, clinicians can improve the accuracy of their decisions and interventions by having access to more reliable data and greater ability to conduct precise analysis on this data. Thus, based on the results of this study (Table 1), AI has a vital role and capability in managing data of diseases.

“Prediction and risk stratification” of disease was the second theme that was extracted from studies on AI applications in decision-making. Disease prediction and risk stratification, forecast of disease occurrence, prediction of infectious diseases, prediction of aging-related diseases, prediction of disease risk for anomaly detection, prediction of mortality risk, prediction of future events, prediction of the spread of disease, and predictive models for diseases are some AI applications in disease prediction and their risk stratification (29–42). Therefore, AI has considerable capacity in disease prediction and risk stratification that should be considered in para clinical centers.

The results of this study also revealed that AI has important applications in disease screening. AI can have a major role in the screening process, by supporting the screening process and staging, as categorized as the third theme entitled “screening” (43–48). Screening is an important stage in decision making for disease management and future stages of intervention and treatment. The fact that AI and its tools can play a role in screening diseases, and provide the desired outputs with more accuracy and confidence, is very good news for the treatment staff, patients and stakeholders. Therefore, the capabilities of AI in disease screening should not be neglected.

“Prognosis” was the fourth theme that was extracted from studies as the main application of AI in decision-making for disease management. Prognosis is a prediction of the course of a disease following its onset. It refers to the possible outcomes of a disease (e.g. death, chance of recovery, recurrence) and the frequency with which these outcomes can be expected to occur (49). Precise prognostic predictions, prognostic analysis, prognostic assumptions, and prognostic decision-making in a clinical setting are the practical aspects of artificial intelligence in the prognosis of diseases (48, 50–54). This ability of AI can help clinicians to make better decisions on the possible outcome of diseases.

Another capability of AI in decision making for disease management was “diagnosis”, which refers to identifying the conditions that explain a person's symptoms and signs, which are normally obtained from the patient's history and physical examination. For example, we found that AI has considerable ability in diagnosing myocardial infarction (MI), diagnosing periapical lesions, diagnosing thyroid nodules, diagnosis and staging of prostate cancer (PCA), diagnosis of oesophageal cancer, diagnosis of outcomes for heart failure (HF), endodontic diagnosis and therapy, and neurological diagnostic support and novel image recognition technologies (32, 46, 55–59). Thus, AI can serve as a reliable assistance for clinicians in disease diagnosis.

“Detection and quantification” was the sixth theme that was extracted after analysis of the studies. We found that AI is applied in automated detection and quantification, automatic detection of thyroid diseases, Covid-19 detection and prevention, detection and classification of different classes of colorectal cancer (CC), detection to minimize incomplete colon capsule endoscopy (CCE), detection of disease and differentiation of pathology, detection of malignant arrhythmias, early detection of cardiac events, early fertility detection, non-invasive detection of atherosclerotic coronary artery aneurysms (CAA), proper detection and treatment of oral cancer (OC), and discrete recognition (29, 56, 60–70). Therefore, AI’s ability in detection and quantification of disease should not be underestimated; rather, this feature should be optimized.

Decision-making is a critical aspect in the field of medicine, and the duration and quality of life, time of treatment, cost of treatment, effectiveness of interventions and continuation of the patient's life depends on it. Evidence suggests that when clinicians attempt to diagnose a patient without decision-making capacity, they are often unsuccessful in matching their treatment plan to the patient’s preferences. Therefore, this stage of treatment should be followed with great care and sensitivity. Evidence based and reliable decisions need strong tools and proper mechanisms.

AI has considerable capability in disease treatment and would be an integral part of medicine in the future. One of the aspects of AI in medicine is helping clinicians to make proper decisions in disease management. This study clearly identified six main themes, i.e. 1) processing and managing data, 2) characterization and analysis, 3) prediction and risk stratification, 4) screening, 5) prognosis, and 6) diagnosis, that addressed AI capability in decision making for disease management. As it is known, AI has a significant and growing capacity in the management and treatment of diseases and decision-making in this field, and this issue should be considered by planners, policy makers, and medical staff. Making use of this capacity can result in medical decisions with more trust and confidence and thus make medical interventions more accurate and effective and reduce the burden of disease and pain.

Ethics approval and consent to participate:

Different ethical aspects of the present research were approved by the Ethics Council of Tabriz University of Medical Sciences.

Consent for publication:

Not Applicable.

Availability of data and material:

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests:

The authors declare that there is no conflict of interest.

Funding:

This study was funded by Tabriz University of Medical Sciences (Grant Number: 1234).

Authors' contributions:

Mohammadhiwa Abdekhoda, contributed to the current study by acting as the corresponding author, supervisor and final reviewer of the manuscript. Fatemeh Ranjbaran, as the counselor of the research, conducted the study and gave technical advice during the research process.

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