Tanmay Kasbe · Advances in computational intelligence and robotics book series · 2023
Applications of machine learning techniques (ML) are having a significant impact on a number of expanding industries, including the healthcare sector. ML is a part of artificial intelligence (AI) technology with the primary objective to increase the efficiency and precision of medical professionals' work. AI presents a significant opportunity in the healthcare sector as most countries are now struggling with overburdened healthcare systems and a dearth of competent medical personnel. Healthcare data is expanding daily and can be utilised to choose the best study sample, gather additional data points, and assess participant data from ongoing studies. ML-based methods/algorithms are employed in the early detection and diagnosis of numerous diseases. For a very long time, early disease prediction and detection have been crucial areas of research for the diagnosis of all diseases. Machine‐learning (ML) algorithms have proven to be quite efficient in disease detection and decision making in healthcare.
Cited by 4
Shruti Dambhare, Sanjay Kumar · 2022
Healthcare as a sector has seen a phenomenal overhaul in the past centuries, and has been one of the fastest growing sector. With the enormous amount of data generated and used in healthcare, machine learning, which at its core is a data-driven domain, provides myriad technological innovations and applications. This chapter aims to give an overview of the basics of machine learning, evaluates its importance of use over the statistical methods and provides brief information about different machine learning techniques. It demonstrates the key segments of healthcare, which in itself is a multidisciplinary industry. Further, the chapter gives a broad coverage about the numerous application of machine learning in prognosis and medical diagnosis. Electronic health records (EHRs), which are an important application in a smart healthcare setup, have the potential to aid better management of medical systems is cited and the proliferation of EHRs in various clinical applications can provide better clinical decision support. This chapter encapsulates application of machine learning in medical image analysis and natural language processing (NLP) of medical documents and literature. Machine learning in healthcare can be used for combatting the pandemic at both levels of primary prediction and for post-pandemic control. The main objective of this chapter is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. This chapter also highlights the integration of machine learning with cutting-edge technologies like big data and Internet of Things “IoT”, biometrics and cloud computing, which have the potential to provide remarkable results and carve a new path for smart healthcare.
Cited by 2
Lana I. S. Hamad, Elmustafa Sayed Ali, Rashid A. Saeed · Advances in medical diagnosis, treatment, and care (AMDTC) book series · 2022
Due to the increase in healthcare data provided by IoT, there is a need to use new methods for data analysis. Machine learning (ML) techniques promise solutions for many challenges facing the IoT-based healthcare services. MLs provide significant improvement in different IoT aspects related to storage size, computational power, and data transfer speeds. In addition, MLs provide a number of solutions for medical imaging, resources, medical data processing, detection, diagnosis, and prediction. Recently, many applications have appeared in the field of medicine and healthcare, which are closely related to the IoT. This chapter presents basic concepts related to the use of ML techniques, in addition to some algorithms used in the medical field and healthcare technology based on IoT devices and systems. Moreover, the chapter will discuss the ML opportunities and challenges in healthcare and future trends as well. The chapter gives the reader full perception of the possibility of using ML techniques in the medical and healthcare fields, with a systematic description of their applications.
Cited by 5
2025
This book explores the applications of machine learning techniques in the healthcare industry to optimize decision-making processes. The book delves into the ways in which machine learning can be used to analyze large and complex healthcare data sets, such as electronic health records, medical imaging, and wearable device data, to extract valuable insights and improve patient care.
Cited by 0
Savitesh Kushwaha, Rachana Srivastava, Harsh Vats et al. · Advances in human and social aspects of technology book series · 2022
Machine learning approaches are utilized in healthcare for computational decision-making in cases where critical medical data analysis is required to identify hidden linkages or anomalies that are not evident to humans. Artificial intelligence (AI) tools can assess a wide range of health data; patient data from multi-omics methods; clinical, behavioural, environmental, pharmacological data; and data from the biomedical literature to respond to research issues that necessitate a big sample size on a difficult-to-reach population. In healthcare, digitising health data has eased the development of computational models and AI systems to extract insights from the data. This chapter initially addressed the prospectus of machine learning in public health with significant focus areas. The medical devices and equipment section contain device-based modelling approaches to various diseases. The chapter also includes brief details on chatbots, wearable technologies, drug distribution systems, vending machines, and text recognition from prescriptions and medicine boxes are addressed.
Cited by 1
Alison Callahan, Nigam H. Shah · Elsevier eBooks · 2017
Cited by 162
Suril Gohel · Current Genomics · 2021
Cited by 1
Rama Bhardwaj, S.C. Jain, Sejal Pokharna · 2025
advanced data-driven approaches to address complex healthcare challenges,fromdiagnosticstotreatmentplanning andbeyond.Despitethediverseandinterconnected nature of this ecosystem, healthcare practices remain governed by regulatory frameworks and ethical standards specific to individual countries and regions.This alignment is achievable because healthcare data-whether represented as electronic health records(EHRs),diagnosticimaging,genomicdata, orwearabledeviceoutputs-canbesystematically linked to individuals or institutions that generate, manage,orprocessthem.Suchtraceabilityensures that machine learning applications adhere to compliance requirements and ethical principles.The integration of ML in healthcare has opened avenues for innovation in disease prediction, patient monitoring, treatment personalization, and operational efficiencies.These advancements rely on the accountability, security, and reliability of data management systems, which enable ML models to process vast and sensitive datasets effectively while maintaining trust in the ecosystem.AsMLcontinuestoreshapehealthcare,itnotonly enhances medical outcomes but also underscores the importance of robust frameworks for data integrity, privacy, and ethical use in realizing its full potential
Cited by 2Open access
Hafsa Habehh, Suril Gohel · Current Genomics · 2021
Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.
Cited by 445Open access
Muhammad Aurangzeb Ahmad, Ankur Teredesai, Carly Eckert · 2018
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
Cited by 228
Sarkar Siddique, James C. L. Chow · Encyclopedia · 2021
Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.
Cited by 172Open access
Dattatray G. Takale, Parikshit N. Mahalle, Sachin Bere et al. · 2025
Cited by 0
Ana Luiza Dallora Moraes · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2020
Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare. The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts. In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment. In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.
Cited by 5Open access
A. Hency Juliet, K. Sathya · 2024
Machine learning (ML) has materialized as a valuable tool in the healthcare sector, empowering medical professionals to provide better patient care and manage clinical data efficiently. As an application of artificial intelligence (AI), ML algorithms analyze vast datasets, recognize patterns, and autonomously execute tasks without explicit instructions. This technology finds diverse applications in healthcare, such as collecting and organizing patient data, identifying healthcare trends, and suggesting personalized treatments. Recognizing its potential to enhance decision-making and reduce risks, hospitals and healthcare companies have embraced ML, leading to exciting career opportunities in the field. The accessibility of commanding hardware and cloud computing has further facilitated the widespread embracing of ML across various domains, including healthcare. With the copious amount of patient data generated, ML's integration in healthcare holds immense promise, enabling successful applications in the industry. ML algorithms in healthcare play a crucial role in transforming the way medical data is processed, analyzed and utilized to improve patient outcomes. These algorithms leverage large datasets and employ various techniques to extract meaningful patterns and insights. Some of the key areas where ML is applied in healthcare are as follows: electronic health record (EHR) analysis, drug discovery and development, personalized medicine, healthcare resource management, disease progression modeling, remote patient monitoring and predictive analytics for healthcare operations
Cited by 1Open access
Dallora Moraes, Ana Luiza · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2020
Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare. The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts. In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment. In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.
Cited by 0Open access
Kushagra Krishnan, Sumit Kumar, Pinki Dey · 2023
In recent years, huge healthcare data generated all over the world has opened new directions for a better understanding of the complex disease mechanism and pathways at a molecular level. These data range from high throughput gene expression profiling to imaging data such as CAT scans, MRIs, etc. However, the challenge is to identify embedded patterns within the available datasets. This calls for a need for robust data analysis techniques to efficiently detect with high interpretability disease-associated patterns by studying the raw healthcare records of numerous subjects around the globe. Over time, Artificial Intelligence (AI) based technologies are increasingly employed in the healthcare sector. Machine learning being the most frequently used AI technology in healthcare has proven to be very effective to develop and advance robust predictive models for identifying the onset and prognosis of complex diseases. Machine learning has been primarily used in the field of precision medicine in the healthcare sector. In addition, it has been extensively studied to improve the diagnosis and treatment of complex diseases such as various cancer and neurodegenerative diseases. Moreover, machine learning and deep learning techniques have also been efficiently employed in handling imaging data to improve the accuracy of identifying abnormalities such as cardiovascular diseases, skeletal injuries, etc. These advancements led to the use of these AI-based technologies by various life sciences companies, pharmaceutical companies, and private care providers. Despite the entire efficacy, AI technologies come with many ethical, occupational, and technological challenges. As a result, we see limited use of AI-based healthcare technologies in recent years, whereas their use can be expected to grow substantially in the coming years. These changes are expected to bring tectonic shifts in healthcare service with AI supplementing patient care and clinicians’ jobs mostly based on human qualities of empathy, persuasion, companionship, etc. This chapter will address the potential that Machine Learning and AI methods offer to automate and improve the efficiency of various aspects of health care as well as the barriers to rapid implementation of these technologies. Several issues will be discussed, ranging from data quality and access of data, machine learning algorithms that are used, clinical workflow, current barriers to implementation of AI technologies and prospects of Machine Learning in healthcare, among other topics.
Cited by 0
Irene Y. Chen, Emma Pierson, Sherri Rose et al. · Annual Review of Biomedical Data Science · 2021
The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
Cited by 418Open access
Abhishek Purohit, YUVRAJ KARARWAL · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025
Machine learning (ML) is transforming healthcare by improving diagnosis accuracy, customizing treatment regimens, and reducing administrative procedures. Its capacity to analyze large datasets and detect complex patterns has resulted in major improvements in patient care and operational efficiency. In diagnostics, ML algorithms have shown extraordinary skill in analyzing medical pictures such as X-rays and MRIs, frequently outperforming humans. For example, ML models in radiology may detect abnormalities with amazing precision, aiding early illness identification and increasing patient outcomes. Beyond diagnostics, ML helps to customize medicine by assessing patient data and tailoring treatment strategies. By taking into account individual features and reactions, ML-driven models may prescribe medicines that improve efficacy while reducing negative effects. This tailored approach is especially useful for controlling chronic illnesses and complicated situations. Operationally, machine learning optimizes healthcare management by anticipating patient admissions, controlling hospital resources, and enhancing supply chain logistics. Predictive analytics enables healthcare institutions to predict patient demands, assuring proper staffing and resource allocation, so improving patient care while lowering operating expenses. Despite these advances, incorporating machine learning into healthcare offers a number of hurdles, including data privacy issues, the requirement for big, high-quality datasets, and the need for openness in algorithmic decisions. Addressing these concerns is critical to the ethical and successful use of ML technology in healthcare contexts. In conclusion, machine learning has enormous potential to improve healthcare by improving diagnoses, customizing treatments, and increasing operational efficiency. Continuous research and development are required to overcome current hurdles and fully exploit the benefits of ML in healthcare systems. Keywords: Machine Learning, Artificial Intelligence, SMEs, healthcare- sector, Ecommerce, E- commerce.
Cited by 2Open access
Neha Gupta, Suneet Kumar Gupta, Vanita Jain · 2023
Background: The usage of devices such as video camera, smartphones, etc. enables the usage of human activity recognition (HAR) in multidimensional domain like healthcare, surveillance, and monitoring. The data collected from the camera and smartphone sensors can be used to capture the daily activities performed by a person which is beneficial for smart healthcare solutions. These smart healthcare applications can be used in fall detection, heath monitoring of patient suffering from diseases like Parkinson, and exercise monitoring remotely. The advancement in artificial intelligence (AI) techniques have transformed the capability to extract deep activity information for precise detection and its analysis. The secure communication means offered by 6G framework can be used for providing privacy with HAR-based smart healthcare applications. Proposal: The numerous research works published on the general characteristics of HAR, and some have proposed various smart healthcare applications but very few have discussed the role of 6G for providing secure HAR applications. In the proposed research work, a detailed narration on the three domains including HAR, smart healthcare, and 6G is presented under the one roof covering the period from 2011 to 2022. Findings: Five findings were (i) HAR constitutes three sources of data acquisition such as video camera, device sensors, and RFID; (ii) role of AI techniques in evolution of HAR-based smart healthcare applications; (iii) HAR role in smart healthcare domain; (iv) role of 6G in securing smart health applications. Conclusion: We conclude that (a) HAR has diverse data-capturing devices and its multimodality with AI techniques, (b) HAR-based smart healthcare applications, and (c) secure smart healthcare applications with 6G framework.
Cited by 0
Dipanwita Thakur, Suparna Biswas · 2022
To promote sustainable development in society, smart healthcare has come into existence. In India, there is a huge crisis of medical facilities according to the population. An increasing number of elderly people and people who live alone require frequent medical care. Otherwise, in the future, they might be in a hypercritical condition because of their ignorance of their health condition. The cost of medical treatment is also very high. So, preventive healthcare, early disease prediction, and online patient consultation are necessary. Using various sensors, we are able to gather information about human activities and their vital signs so as to analyze their health condition using machine learning (ML) and take preventive action, ensure early disease prediction, and provide online patient consultation. Nearly every healthcare system is influenced by a fundamental problem: the trade-off between the need to provide quality healthcare services and the need to manage costs and ensure the system’s global sustainability. ML plays a significant role in achieving this sustainable healthcare. In this chapter, we discuss the role of ML in sustainable healthcare.
Cited by 1