Abstract
A broad range of medical diagnoses is based on analyzing disease images obtained through high-tech digital devices. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of various diseases. AI techniques based on medical image processing are an essential area of research that uses advanced computer algorithms for prediction, diagnosis, and treatment planning, leading to a remarkable impact on decision-making procedures. Machine Learning (ML) and Deep Learning (DL) as advanced AI techniques are two main subfields applied in the healthcare system to diagnose diseases, discover medication, and identify patient risk factors. The advancement of electronic medical records and big data technologies in recent years has accompanied the success of ML and DL algorithms. ML includes neural networks and fuzzy logic algorithms with various applications in automating forecasting and diagnosis processes. DL algorithm is an ML technique that does not rely on expert feature extraction, unlike classical neural network algorithms. DL algorithms with high-performance calculations give promising results in medical image analysis, such as fusion, segmentation, recording, and classification. Support Vector Machine (SVM) as an ML method and Convolutional Neural Network (CNN) as a DL method is usually the most widely used techniques for analyzing and diagnosing diseases. This review study aims to cover recent AI techniques in diagnosing and predicting numerous diseases such as cancers, heart, lung, skin, genetic, and neural disorders, which perform more precisely compared to specialists without human error. Also, AI's existing challenges and limitations in the medical area are discussed and highlighted.
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1 Introduction
Advances in emerging computer-based technologies are overgrowing. Digital healthcare offers numerous opportunities to reduce human error, improve clinical outcomes, and track data over time. Artificial Intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL) algorithms, are widely used in the prediction and diagnosis of several diseases, especially those whose diagnosis is based on imaging or signaling analysis [1, 2]. AI can also help to identify demographics or environmental areas where disease or high-risk behaviors are prevalent [3, 4]. ML techniques have achieved significant success in medical image analysis due to the advanced algorithms that enable the automated extraction of improved features [5, 14, 15].
Recently, ML and DL techniques have established an advanced approach to emerging techniques in computer-based diagnosis, which have been widely conducted in various medical fields to diagnose or predict multiple diseases [16,17,18,19]. These methods have led to more accurate diagnoses and increased efficiency [16, 17]. This paper reviews the ML and DL models for diagnosing various diseases.
2 Related works
AI has recently undergone significant advances that have achieved much attention from numerous companies and academic fields. The most successful technique is driven by advances in ANNs, called Deep Learning (DL), a set of processes and algorithms that automatically enable computers to detect complex patterns in large datasets. Feeding these advances is increased access to data (“big data”), user-friendly software frameworks, and an explosion of existing computing power that allows deep neural networks to be widely used. DL became prominent in image processing when neural networks performed better than other methods in several high-resolution image analysis criteria.
In the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [12], a CNN model reduced the second-highest error rate in image classification work by 50% in 2012. Before that, computers were thought to be very difficult to detect objects in natural images. So far, CNN has even surpassed human performance in ILSVRC to the point where the task of classifying ILSVRC is essentially solved. DL techniques have become the objective standard solution for various computer vision problems. Numerous studies have suggested the use of DL techniques in the diagnosis of acute human diseases.
Researchers have used multiple scenarios based on ML and DL models to predict conditions such as liver disease, heart disease, Alzheimer's disease, and various types of cancers for which early detection is vital for treating [20,74, 75]. For instance, this technology can detect dangerous tumors in medical images, allowing pathologists to diagnose the disease in the early stage and treat it instead of sending tissues or lesions samples to a lab for long-term investigation [76]. AI-based algorithms are an effective tool for identifying undiagnosed or less-diagnosed patients, unencoded, and rare diseases. Thus, AI models for disease diagnosis provide ample opportunity for early diagnosis of patients [76].
The application of ML and DL techniques to diagnose heart diseases is increasing significantly. Due to the existence of a wide range of medical imaging methods, such as CT, ECG, and echocardiography in cardiology, DL can be used accurately and advanced in the analysis and review of cardiovascular data [77,78,79]. Coronary atherosclerotic heart disease is a common cardiovascular disease that causes disabilities and severe morbidities. Early diagnosis of this disease is highly effective and has an impressive impact on treatment. In this term, ML and DL methods have achieved remarkable progress in coronary atherosclerotic heart disease diagnosis [109]. Researchers used two ML-based strategies, ensemble learning, and DL, to analyze skin cancer lesions [110]. In this work, the DL approach outperformed the ensemble learning, for prediction demonstrated an accuracy of 91.85% and for classification of skin cancer resulted in 90.1% accuracy. The combination of Bayesian DL and an active learning approach has been used to diagnose skin cancer [111]. This approach achieved the best performance in ISIC 2016 with 75% accuracy.
The interaction of digital pathology and AI has led researchers to examine datasets more accurately and provide precise results for prostate cancer diagnoses. A vital treatment for prostate cancer is radiotherapy, but its toxicity recognition is problematic for various individuals [112]. In this case, AI could provide proper insights on predicting how a patient will react to the different therapy methods. Furthermore, AI-based technologies have demonstrated acceptable accuracy in diagnosing prostate lesions and predicting prostate cancer, patient survival rate, and treatment response. Researchers developed a supervised AI-based model for the diagnosis of prostate cancer in the early stage [113]. They used MRI images labeled with histopathology information which resulted in 89% accuracy in classification. Another study proposed a novel DL approach named XmasNet to classify prostate cancer lesions using 3D multiparametric MRI data provided by the PROSTATEx in the training phase [114]. XmasNet outperformed ML classical methods with an AUC of 0.84.
Different techniques have been proposed to detect lung cancer in the early stages; most are based on CT scan images, some utilizing x-ray images. Although it is challenging to catch it in the initial stage, it has been proven that early detection improves the survival rate of lung cancer patients [125]. Other researchers also proposed a new approach for contrast optimization based on HE in cancer diagnosis using ultrasound medical imaging [126].
Even though various researchers have recently addressed the issue of model complexity in DL [127,128,129,130], there is still a need for further investigation and effort in this area [131]. Through DL or ML algorithms, a constraint on the dataset is likely to create a similarly constrained model. For example, outside the pre-defined boundaries, the network may appear useless because it has not been taught how to handle such instances.
Also, more efforts are needed from medical community to convince future perspectives and acceptance of AI technologies in diagnosing and predicting various diseases. Additionally, patient privacy must be taken seriously when entering data into artificial intelligence systems [132]. Therefore, global coordination and monitoring should be done, leading to the widespread and verifiable use of AI in healthcare procedures.
In particular, ML and DL can be used to analyze large amounts of medical data, such as patient records, imaging studies, and laboratory results, in order to identify patterns that might not be obvious to human doctors [133]. This can lead to more accurate and efficient diagnosis and the ability to predict which patients are at the highest risk for certain conditions. Additionally, these techniques can be used to develop personalized treatment plans for individual patients based on their unique characteristics and medical history [134, 135]. Also, it is vital to ensure the data used to train these models is diverse and unbiased to avoid any inaccuracies or discrimination in diagnosis and predictions [136].
In the future, ML and DL algorithms will continue to improve and become more widely adopted in the healthcare industry, leading to better disease prediction and diagnosis for patients. ML and DL techniques can be used to analyze genomic data to identify genetic markers associated with different diseases, which could lead to more precise diagnosis and personalized treatment plans. Another promising area for these techniques in healthcare is in the development of predictive models for disease progression and treatment response. These models could help physicians to identify patients at high risk for complications, or those who are unlikely to respond to certain treatments, allowing for early intervention and more effective care. As these techniques continue to advance, we can expect to see even greater improvements in patient care in the future.
6 Conclusion
DL and ML techniques have strong potential to revolutionize the field of disease diagnosis and prediction. In diagnosing the disease, the accuracy and correctness of the diagnosis is the most critical factor in the treatment process. AI has proven significant accuracy in the detection of image-based diseases as well as in the prediction of treatment outcomes regarding survival rate and treatment response. The enormous quantity of image data requires implementation into processing phases through immediate, reliable, and accurate computing power provided by AI methods. In diagnosing diseases, issues such as accuracy in detection, effective treatment, and ensuring the well-being of patients are critical. AI includes vast and diverse data, algorithms, deep computing methods, various neural networks, and emerging techniques constantly evolving to meet human needs. This study aims to investigate the performance of AI techniques in diagnosing and predicting various diseases. According to the findings of this research, SVM has the best performance for predicting heart diseases. Supervised DL networks, such as CNN-based models, are widely used due to their high accuracy and fast image recognition, especially for diagnosing in respiratory, lung, skin, and brain diseases which have led to significant results. For breast cancer diagnosis, usually combining KNN with other networks, such as SVM, leads to high accuracy in diagnosis. Therefore, DL and ML, with impressive experimental results in detecting and classifying medical images, significantly impact the success of many diseases discussed in this study. In other words, AI-based methods assist medical systems in diagnosing and predicting conditions by optimizing the use of different resources. Also, with the rapid development of AI technologies, the objective diagnosis of various diseases will no longer be an uphill task for doctors in the near future.
Data availability
Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. All authors, Nafiseh Ghaffar Nia, Erkan Kaplanoglu, and Ahad Nasab have approved all the statements in this work.
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NGN wrote the first draft of the manuscript and all authors commented on the manuscript. All authors contributed to the study’s conception. Material preparation and analysis were performed by NGN, EK, and AN. All authors read and approved the final manuscript.
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Ghaffar Nia, N., Kaplanoglu, E. & Nasab, A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discov Artif Intell 3, 5 (2023). https://doi.org/10.1007/s44163-023-00049-5
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DOI: https://doi.org/10.1007/s44163-023-00049-5