In the current era, Artificial Intelligence (AI) is dramatically more prevalent in various fields such as business and society, especially in healthcare. In the multitudinous medical domain services are empowered by the wide response of AI owing to the development of information advancements, including adequacy size, computational energy, and information fastness.
Artificial Intelligence employs various computer-based algorithms to discover essential information from data precisely and create clinical decisions for medicine. A subclass of AI, medical researchers, and clinical professionals widely utilize machine learning to develop new ways of studying diseases, discovering medicines, and treating patients.
Machine learning automatically detects the patterns without the need for an explicit program. Machine learning imparts techniques, tools, and methods as supporting aid for solving diagnostic and prognostic difficulties in varied medical fields.
Machine learning effectively analyzes the significance of clinical conditions and the prediction of disease progression, the extraction of medical knowledge, treatment planning and support, and overall patient management.
Integrating machine learning for the computer-based system in the medical environment contributes to opportunities to expedite and facilitate the work of healthcare experts and conclusively enhance the quality of medical care.
Machine learning for medicine provides many opportunities for healthcare institutions to focus on patient care, increase diagnosing accuracy, and develop a more accurate treatment plan.
Over the last few years, machine learning has increasingly been applied in health care for the fine understanding of data and superior clinical decision-making processes.
Although the applicability of machine learning in the medical sector is significantly higher, advanced machine learning called deep learning has emerged into the medical domain.
Deep learning techniques are popularly utilized for medical image analysis by utilizing various imaging modalities for increased prediction rate of disease state instead of requiring radiologists to diagnose the afflicted condition.
In the medical domain, numerous research works and real-time applications are developed successfully with the potential support of machine learning and deep learning frameworks. Some of the remarkable research scopes are briefly described below:
Disease Diagnosis and Identification:
In present times, Artificial Intelligence (AI) techniques such as machine learning and deep learning are effectively applied for diagnosing numerous diseases such as Alzheimer's, epilepsy, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, liver, and covid-19 disorder. Deep learning imparts effective planning and treatment in terms of accuracy in disease diagnosis. For a more accurate diagnosis, its research scope is still expanding with the help of algorithms, analytics, deep learning, and neural networks to attain the needs of the healthcare sector.
Clinical Decision Support Systems:
Clinical Decision Support System (CDSS) assists in analyzing huge volumes of health care data to recognize a disease, plan for the next treatment stage, figure out any inherent problems, and all-inclusive improvement of patient care efficacy. CDSS depletes the chances of deciding the erroneous diagnosis or prescribing unproductive treatment. It is an influential tool that remains an aid for healthcare experts to do their job effectively and rapidly. In recent years applying machine learning in healthcare to implement CDSS is developing. Electronic Health Record (EHR) and various forms of digitized medical data are utilized to develop CDSS.
Smart Health Records:
Managing and analyzing patient health records in smart worlds is challenging. Though, it is very critical for efficient decision support and patient care. Machine learning facilitates smart health care services by managing health records in a real-world scenario. Recently, Optical Character Recognition (OCR) technology has been utilized by machine learning methods to manage data entry of smart health services to boost clinical decision making and patient care.
Presently, medical images are analyzed using deep learning technology to improve disease research. Several key tasks are applied in deep learning-based medical imaging, such as medical image reconstruction, enhancement, segmentation, registration, medical image classification, and Computer-Aided Detection(CAD) to discriminate between healthy cells and tumors. Various clinical practices of medical imaging enhanced by deep learning involve the chest, neurology, mammography, cardiovascular, gastroenterology, dermatology, abdominal, pulmonary, and microscopy imaging. Advanced deep learning methods include adversarial, attention mechanisms, Neural Architecture Search (NAS), Transfer Learning (TL), self-supervised, and federated learning significantly help the advancements.
Personalized Medicine and Treatment:
Characterizing some specific medical conditions with medicine is complex and resource-heavy in health care systems. Complex decisions should be determined to build an efficient treatment scheme, considering drug interactions with inherent side effects. The machine learning paradigm helps achieve precise, personalized medicine with the best possible outcome to deliver patient-specific treatments. Machine learning also utilizes a multitude of data to understand disease conditions better to provide effective personalized medicine.
Disease prediction helps identify the early stages of the disorder, and the possibility of any potential worsening of the patient’s condition before the onset of disease raises the chances of prosperous treatment remarkable. Machine learning in healthcare is utilized to successfully predict the most threatening diseases at the risk level of patients. The recognition of signs of disorders such as diabetes, liver, heart and kidney diseases, epilepsy, and cancer are predicted using machine learning. More recently, infectious disease outbreak prediction has been implemented with the help of machine learning concepts to detect the signs of an epidemic early on for Covid-19. This outbreak prediction analyzes various data, such as satellite data, news, and social media reports, to predict the spread of disease.
Health Care Data Analysis:
Machine learning greatly helps healthcare professionals determine the therapy and treatment for various disease conditions using health data in healthcare management. Machine learning utilizes several health data, including demographic data, medical images, laboratory outcomes, genomic data, medical records, and data from sensors. Currently, machine learning techniques have invented opportunities to improve the healthcare system by accessing the health data with its applicability toward the independent, upgrading, and improving available medical structures.
Aged and Low-Mobility Groups Care:
Machine learning-enabled medicine assists low-mobility groups, including the elderly, disabled people, and wheelchair users, which is beneficial to enhancing their everyday lives. This technology is utilized in smart reminders to predict and avoid potential injuries affecting such groups by recognizing the obstacles and determining the ideal paths. However, this medical care with machine learning is still not widespread, even with its effectiveness. Thus, several healthcare companies and research centers are currently focusing on developing machine learning-assisted support systems for elderly and low-mobility people.
Surgical procedures need great precision, adaptability to modifying conditions, and a stable approach for an expanded period. One of the opportunities in machine learning for healthcare is surgical robots. Possible applications of machine learning in the surgical area are divergent and contend multiple ways along with the surgical scope, including training, operations, and clinical data administration. Modern technology in the surgical field that proves its worth by consistently reducing surgery time and hospitals funds will be very successful. Robotic surgery is efficaciously utilized as an aid for human surgeons. Currently, machine learning techniques are used for finer surgery modeling and planning, estimating the surgeon’s expertise, and facilitating various surgical tasks such as neurosurgery.
Drug Discovery and Manufacturing:
Machine learning techniques boost clinical decision-making in the pharmaceutical industry across various applications, such as discovering drug architectures for specific conditions to retrieve precise outcomes. Target validation, prognostic biomarkers, and digital pathology are the area of drug discovery where machine learning takes place. In medical trials, machine learning enhances decision-making and extrapolates risk failures in drug discovery. Machine learning approaches also assist in developing a personalized medication for patients with a peculiar set of ailments or particular special requirements. The future scope of machine learning in drug discovery and production is combined with nanotechnology for superior drug finding and delivery.
Machine learning in clinical research imparts the potential to enhance the quality and efficiency of biomedical evidence, increasing its positive impact on all clinical stakeholders. Realizing the potency of machine learning overcomes the problems with data structure and access, interpretation of outcomes, transparency of development and validation processes, and the possibility of preference. In clinical trials, machine learning algorithms are used to analyze the ongoing data from the trial participants and deplete the data-based errors. Recently, data collection and management, clinical trial participant management, preclinical drug discovery, and development research are the roles of machine learning in clinical research.
Various deep learning techniques have evolved for healthcare applications to support the medical professionals conducive to yielding a better outcome. In the medical field, from information extraction and medical documents to the prediction and detection of disease, machine learning, and deep learning have been utilized. With the investigated knowledge, it was indisputable that neural network-based deep learning techniques have executed extremely well in computational biology with the assistance of the consistent processing power of advanced technology and intensively exploited due to their high predicting precision and dependability. Some impressive deep learning strategies are described below with effectual applications.
Convolutional Neural Network (CNN):
CNN architecture is a supervised learning framework extensively applied for image analysis applications. In medical image analysis, CNN algorithms assist in categorizing, classifying, and specifying disease patterns using image processing. Novel deep learning models such as end-to-end and convolutional neural nets are developed to handle medical records. In medical image analysis, tasks such as brain tumor segmentation, knee cartilage segmentation, prediction of semantic descriptions from medical images, segmentation of MR brain images, and coronary artery calcium scoring in CT images are implemented using deep learning. Predicting protein disorder regions, predicting protein secondary structures, and prediction of protein structure properties are the protein structure prediction works supported by deep learning.
AE is an artificial neural network that maps the input through an inter-related neural network for feature reduction or network initialization. AE is classified as unsupervised learning, and it involves Sparse Auto-Encoder (SAE), Variational Auto-Encoder (VAE), and Denoising Auto-Encoder(DAE). In medical diagnosis, SAE is highly employed for medical image analysis such as organ detection in four-dimensional patient data, hippocampus segmentation from infant's brains, histological characterization of healthy skin and healing wounds, and protein structure prediction such as sequence-based prediction backbone Cα angles and dihedrals.
Deep Belief Network(DBN):
DBN is a hybrid multi-layer neural network consisting of directed and undirected interconnections and learning high-dimensional multifarious data. Segmentation of the left ventricle of the heart from magneto resonance data and discrimination of retinal-based diseases are applications of DBN in medical image investigations. Protein structure prediction contributions of DBN are a prediction of protein disorder, prediction of secondary structures, and local backbone angles. In the genomic sequencing and gene expression analysis, modeling structural binding preferences, predicting binding sites of RNA-binding proteins, and predicting splice junctions at the DNA level are the applicative tasks of DBN.
Deep Neural Network (DNN):
DNN showed better performance than clinicians in many healthcare applicative tasks due to the quick rise in the available data and computational power. Owing to the transparency of medical image analysis DNNs are applied for brain tumor segmentation in MR images, prostate MR segmentation, and gland instance segmentation. In genomic sequencing and gene expression investigation, gene expression inference, prediction of enhancers, splicing patterns in individual tissues, and differences in splicing patterns across tissues are performed using DNN models.
Recurrent Neural Network(RNN):
RNN is used for pattern recognition of stream or sequential data. RNN is highly recommended to handle time series physiological signals to diagnose the disorder and classify the clinical data in the medical domain. Some RNN-based medical image analysis tasks include classifying patterns of Electroencephalogram (EEG)synchronization for seizure prediction and EEG-based lapse detection. Prediction of protein secondary structure and prediction of protein contact map are applications of RNN in protein structure forecast. Genomic sequencing and gene expression analysis, such as predicting miRNA precursor and miRNA targets and detecting splice junctions from DNA sequences, use RNN as a learning model.
Healthcare sectors are concentrated on enhancing the scope of machine learning techniques due to their effective data analyzing capabilities. However, machine learning models face some challenges that are discussed below:
Machine learning models require huge databases for training to improve performance with low error. Thus, it is mandatory to develop new techniques for electronically recording and analyzing hugemedical data.
Data quality is a very important complication due to the accidental and advertent occurrence of an error, which increases the error rate. Data quality issue occurs while ascertaining the labels of data, owing to the carelessness of physicians and specialists. Data preprocessing methods are significantly utilized to improve the quality of medical datasets.
The healthcare datasets in real-time applications are high dimensional, which increases the model complexity, raises the learning time, and over-fitting machine learning. Machine learning has some effective techniques for depleting dimensionality by considering such issues. Feature selection and feature extraction are efficient solutions for resolving this problem. This area needs more research focus to impart more effective techniques for reducing dimensionality.
In the medical domain, data privacy is more important owing to the confidential information of patients. When designing machine learning-based models for healthcare applications, privacy should be considered. Privacy is a serious issue thereby, and the researchers should contribute more research for contending this problem.
Due to the progress and changes over time in a non-detected way of diseases in the medical domain, many existing deep learning models consider static vector-based inputs that cannot handle the time aspect in general. In the future, developing deep learning techniques to deal with temporal health care data is regarded as an essential aspect that requires more research focus to develop new solutions.
The problems in biomedicine and health care are more intricate than in other domains. Complete knowledge about the causes and progression of most highly heterogeneous diseases is unknown. More research concentration on machine learning techniques is needed to handle the complexity of the diseases.
In recent times, deep learning techniques have been applied to many real-world applications. However, the interpretability of the model is essential for convincing the medical professionals and experts about the works recommended by the predictive and detection system. Thus the interpretability of deep learning needs high focus in the medical domain.
With the above challenges into consideration, some of the future research scopes and possibilities are discussed below:
The potent integration of highly heterogeneous medical data in a deep learning model will be decisive and challenging research direction. The potential opportunity of this research direction utilizes the hierarchical architecture such as layers of AEs or deep Bayesian networks of deep learning and stacked representations in a joint model toward a holistic abstraction of the patient data.
Every clinical organization possesses its patient population. Constructing a deep learning model by supporting the patients from varied sites without leakage of sensitive information becomes a critical problem in this framework. Consequently, learning deep learning with a federated strategy will be another important research scope for more secured data handling.
Privacy is an important concern in scaling up medical applications with deep learning models. Preserving the privacy of deep learning models is a challenging concept. Research work on considering all the personal information for applying deep learning models in clinical applications is needed. Federated learning and deep learning with differential privacy standards are the new research scopes for deploying intelligent tools for next-generation health care.
Incorporating expert knowledge:
The available expert knowledge for medical issues is indispensable to utilize. Due to the limited amount of health care data and their several quality problems, instigating the expert knowledge into the deep learning models is regarded as the right research scope. Semi-supervised learning is also a productive method to learn from the huge amount of unlabeled data samples with only a few labeled samples. It will be of great potential research due to its ability to support labeled and unlabeled data samples.
Contemplating the time aspect is important for healthcare-related issues; a time-sensitive deep learning model will be developed for finer understanding of the patient condition and impart timely clinical decision assistance. Thereby, temporal deep learning is critical for resolving health care problems. In particular, Recurrent Neural Network (RNN) and attention mechanism play a more notable role in superior clinical deep architectures.
Model performance and interpretability are equally crucial for health care issues. Clinicians are unlikely to adopt a system they cannot understand. The interpretable modeling enables more understandable the development of trustable and reliable systems. Research directions of deep learning will focus on developing novel algorithms with interpretable modeling to support the predictions of data-driven systems.