AI and Machine learning in Healthcare

AI and Machine learning in Healthcare

Sujit Murumkar

Director, Data & AI Practise, Axtria.

300 Connell Dr 6th Floor Berkley Heights NJ 07922, USA

Abstract – The increasing adoption of electronic health records, IoT wearable products, imaging systems, and mobile health platforms have resulted in producing unwelcome amounts of clinical and behavioural data. Such a big-data setting has increased the pace at which artificial intelligence (AI) and machine learning (ML) are integrated into health care decision-making. Even with their potential, healthcare organisations have also been confronted with severe issues, such as the heterogeneity of data, privacy concerns, a lack of interpretability of black-box models, disparities in the performance of algorithms on disparate populations, and infrastructure barriers to real-time deployment. These constraints lower clinical trust and limit predictive analytics and remote monitoring systems based on AI. This study is intended to critically discuss the current application of AI and ML to healthcare, in particular, predictive analytics, IoT-based monitoring, smart hospital systems, big-data infrastructure, explainable AI models, and mobile health ecosystems, which is supported by evidence only of current research. This critical review is a flexible synthesis of empirical data to assess the quantifiable effects of AI on diagnostic accuracy, early detection, treatment optimisation, cost reduction and efficacy. It also explores the architectural enablers, including distributed learning frameworks and big-data pipelines, and deals with ongoing ethical, technical, and clinical issues. The article is a brief but detailed evaluation of the transformative effect of AI and its limitations in existing healthcare systems

Keywords – Artificial intelligence, predictive analytics, healthcare big data, machine learning

1. INTRODUCTION

   Artificial intelligence (AI) has become a key feature in modern healthcare ecosystems, mostly due to the radical expansion of clinical, behavioural, imaging, and physiological information that is gathered on digital platforms. The advent of mass datasets due to electronic health records (EHRs), wearable Internet of Things IoT sensors, radiology systems, and genomic sequencing systems, as well as mobile health applications, has opened up opportunities to analytics that the traditional decision frameworks cannot avail. In such circumstances, machine learning (ML) models and enhanced data analytics can present the computational base needed to detect deep, latent, and nonlinear patterns across heterogeneous data, therefore, informing diagnostics, population health surveillance, and treatment optimisation.

   Nevertheless, AI development in healthcare is not merely a technical process but a change of structure that has significant implications on clinical practise, hospital organisation, and patient safety. Although it is proven that it leads to significant improvements in accuracy, early-stage detection, and

   operational efficiency, they are accompanied by unanswered issues related to data quality, interoperability, bias in algorithms, and ethical use. Predictive intelligence systems have enhanced monitoring, risk evaluation, and resource allocation but the quality and consistency of the systems are still subject to the integrity of the underlying data, computational scalability, and clinical validation in the real world.

   These developments are critically analysed in the following sections based on quantitative and qualitative observations made based on the given literature alone.

2. GROWTH OF BIG DATA IN HEALTHCARE AND FOUNDATIONAL PRECONDITIONS FOR AI

   INTEGRATION.

   The very basis of AI implementation in healthcare is the presence of large and heterogeneous datasets. The analytical load imposed on computational systems by the growing amount and complexity of healthcare data is captured by the conceptualisation of big data in terms of the 5Vs: volume, velocity, variety, veracity, and value [12]. Systemically, these dimensions can inform the message that AI does not only complement the current analytic strategies but is also stipulated by the structural constraints of the traditional statistical tools in viability to process real-time, multimodal clinical data.

   Fig 1: Growth of big data in healthcare

   (Source: self-developed)

   The world data growth is also quantitative in nature, given that the volume of digital information has grown by 59 zettabytes by 2020 and is expected to reach 149 zettabytes by 2024 [13]. This acceleration represents a change in health service provision where perpetual data gathering, both with imaging systems, laboratories, and behavioural surveillance tools, fuels the need to have smart systems, which can classify, predict and detect anomalies on a large scale.

   Wearable IoT devices also change this picture, creating streams of heart rate, oxygen saturation, motion, temperature, and stress indications and sleep measurements in high frequency and continuously. These data make it possible to monitor the conditions like arrhythmias, glucose fluctuations, pulmonary function abnormalities, and symptoms of infection in real time. The usefulness of such data, however, is conditional upon the soundness of the algorithms, the precision of the sensors, and their combination with the clinical decision-making systems.

   Fig 2: Data integration in healthcare

   (Source: self- developed)

   Even though the concept of big data has the potential of creating personalised care, its actual application reveals structural flaws in current healthcare setups. As an example, the EHR systems are full of structured entries and unstructured clinician notes, which provide significant challenges to automated interpretation. Data heterogeneity, which includes imaging modalities, genomic sequences, free-text documentation, and audio/visual outputs, poses barriers to model interoperability as well as increases the disparity between theoretical model performance and clinical applicability.

   These constraints demonstrate that AI integration requires strong data management structures and standard data collection practices as a condition.

3. Machine learning and Clinical uses.

   Machine learning offers the mathematical algorithms needed to derive patterns and make predictions out of non-linear datasets. The literature cites supervised, unsupervised, and reinforcement learning as the three major paradigms that are used in healthcare analytics with sub-symbolic models including artificial neural networks and deep learning architectures [12].

   A. Critical Overview of Model Utilisation in Healthcare Domains.

   CNNs, RNNs, and LSTMs as well as autoencoders have seen significant success in imaging diagnostics, processing physiological signals, and recognising high-dimensional patterns. Their usage in IoT-based systems demonstrates their ability to categorise ECG signals, identify abnormal heart rate, detect gait irregularities and cardiac emotions, as well as analysing complex behavioural cues [14]. Nevertheless, these

   successes are dependent on the presence of large annotated datasets, which are not always available in most clinical areas. SVMs, random forests, gradient boosting, and ensemble are still considered to be useful tools of predictive modelling, in the situations when the dimensionality of the data is moderate, and the interpretability is still a major concern. These algorithms have revealed their expertise in the detection of cardiac anomalies, prediction of diabetes-related diseases, detection of cancer biomarkers, and tracking the progression of infectious diseases with the help of high-dimensional sensor and clinical data [2].

   TABLE I: Machine Learning Model Utilisation in Healthcare Big Data Analytics

   Machine Learning Technique	Reported Usage Share (%)	Healthcare Application Domains
   Deep Neural Networks (DNN)	15%	Imaging diagnostics, ECG analysis, IoT monitoring
   Support Vector Machines (SVM)	15%	Chronic disease prediction, symptom detection
   Artificial Neural Networks	14%	Treatment response modelling, risk scoring
   Decision Trees	12%	Early diagnosis, classification tasks
   Ensemble Models	11%	Anomaly detection, clinical forecasting

   Source: Data extracted from Nti et al. (2022) [13].

   Patterns of algorithmic utilisation continuum also show quantitatively the distribution of techniques in big data analytics studies: deep neural networks (approximately 15%), SVMs (approximately 15%), artificial neural networks (approximately

   14%), decision trees (approximately 12%), and ensemble models (approximately 11) [13]. Those numbers are an example of not only the superiority of neural architectures but also of the enduring usefulness of classical ML models, especially in resources-heavy conditions where computing costs are a major priority.

   More importantly, though, such patterns of utilisation reveal systemic dependencies: deep learning models need a great deal of computational resources and large labels, and classical algorithms can do badly, when faced with highly unstructured multimodal inputs. Such a choice of algorithms is thus a trade- off between accuracy, interpretability, computational feasibility and data availability.

4. Predictive Analytics and Clinical Outcome Improvement.

   One of the most influential ways in which AI can be used in clinical practise is predictive analytics. It goes beyond the descriptive observations, facilitating the early diagnosis of the disease, stratifying the risks, and individualised therapeutic procedures.

   1. Critical Evaluation of Patient Outcomes and Risk Reduction Evidence.

      Predictive analytics has proven to achieve measurable patient outcomes. It is proven that the utilisation of AI-powered predictive models has decreased readmissions by 15 to 20 percent and operational expenses by 25 percent [1]. The implication of these data is that there are high economic and clinical returns, but the extent of said benefits is often determined by the accuracy of the model, the level of data faithfulness and the inclusion into clinical practise. Lack of integration of predictive systems is associated with false alarms, alert fatigue, and a lack of engagement of clinicians in the system, reducing their effectiveness.

      As demonstrated by Dixon et al., AI models can enhance the quality of prognosis and reflect better treatment outcomes in chronic conditions, cancer, cardiology, and acute care conditions [3]. Though these results clearly demonstrate the clinical importance of predictive modelling, they also demonstrate some limitations: it will only be improved with strong validation, a wide training data and constant performance evaluation. The predictive models, which are trained on homogenous populations, can incorrectly classify the underrepresented demographic groups, thus increasing the inequities.

   2. Wearable Analytics and Real-Time Detection Critical Appraisal.

   AI-based wearable analytics is an extension of the clinical gaze to the outside of the hospital walls, which provides the ability to detect abnormalities continuously. The gradient boosting tree, SVM and random forest models have demonstrated good performance in detecting anomalies in several fields [2]. Nonetheless, wearable data come with such issues as sensor drift, motion artefacts, and environmental noise that can negatively impact the model performance.

   TABLE II: Quantified Impact of AI Predictive Analytics on Clinical and Operational Outcomes

   Outcome Area	Measured Impact	Evidence Summary
   Hospital Readmission Reduction	1520% decrease	Predictive models identified risk early, enabling targeted intervention
   Operational Cost Reduction	25% reduction	Forecasting and optimisation models improved resource allocation
   Early Adverse- Event Detection	Significant improvement reported	Models enhanced early identification of deterioration events
   Prognostic Accuracy	Improved across multiple diseases	Narrative review shows enhanced outcome forecasting

   Source: Findings based on Hossain et al. (2024) [1] and Dixon et al. (2024) [3].

   An IoT-based health tracking presented by Kajornkasirat et al. proves to be very performance-intensive on the random forest classifier to detect physiological and emotional states [4]. Although this confirms the technical capability of the system, there is a severe reliance on the fact that the network connexion is stable, that the battery has enough capacity, and that the sensor calibration is dependable. Moreover, real-time analytics demand sustained power use and this might not be practical in the low-energy wearable devices.

   In such a way, even with good performance indicators, wearable-based models should be assessed in regard to operating limitations and user compliance trends.

5. BIG DATA FRAMEWORKS AND ANALYTICAL ARCHITECTURES OF AI DEPLOYMENT.

   The usefulness of AI models depends upon architecture in which data is gathered, processed, stored, and analysed. Big data frameworks have multi-layered pipelines consisting of data sources, ingestion mechanisms, distributed storage systems and analytics engines [12]. Lambda architectures allow real-time and batch processing concurrently, allowing responsive clinical alert systems without losing long-term analysis.

   Clinical decision support systems (CDSS) are based on these architectures and combine real-time analytics and evidence- based recommendations. Data-driven support helps to improve diagnostic precision and eliminate adverse events due to the reduction of unwarranted testing in CDSS frameworks [12]. Nevertheless, the effect of them is moderated by workflow integration: inadequately created interfaces can interfere with clinical processes or create cognitive loads.

   Tulli identifies Spark-based analytics, ontology-based classification, and multimedia retrieval as the key elements of the new AI frameworks [5]. The weaknesses that are most critical for model interpretability are dealt with through ontology integration, which interprets the concepts in relation to structured semantic representations. This is especially essential in the clinical environment where interpretability is related to patient safety and regulatory compliance.

   Most importantly, big data infrastructures are however complicated, expensive and consume resources. Their implementation requires a lot of organisational preparedness, qualified staff, and maturity of governance.

6. EXPLAINABLE AI AND ETHICAL INTEGRATION IN HEALTHCARE 5.0.

   Explainable AI (XAI) is a key to the preservation of trust, transparency, and accountability in high-stakes clinical decision settings. onventional black-box models, especially neural networks of deep type, do not provide much visibility of the decision-making process, which poses a clinical adoption challenge.

   Interpretability can be offered by XAI methods, such as feature attribution, saliency mapping, rule extraction, and model distillation, which promote more clinician trust and adherence to regulations. It has been demonstrated that federated learning with XAI is better than centralised models in the ECG monitoring and data privacy is preserved [8]. Federated learning reduces the threat of privacy because it is decentralised in training its models, yet there are issues of variability in data distribution, model synchronisation, and communication efficiency.

   TABLE III: Comparison of IoT-Driven Wearable Machine Learning Models

   Model Type	Reported Performance Insight	Application Context
   Random Forest	Highest classification accuracy	Emotion and physiological state detection in IoT wearables
   Support Vector Machine	Strong anomaly- detection ability	Cardiology and real-time health monitoring
   Multilayer Perceptron	Moderately high accuracy	Integrated wearable sensor pipelines

   Source: Data extracted from Kajornkasirat et al. (2025) [4] and Etli

   et al. (2024) [2].

   Healthcare 5.0 lays stress on cyber-physical systems, intelligent automation, and hyperconnectivity. The literature shows that statistical evidence exists that the 5G-enabled healthcare environment can reach a throughput of 5Gbps and a latency under 10ms, which increases the viability of real-time deployment of computationally intensive models [8]. Nevertheless, these performance levels demand a strong network infrastructure which is distributed unevenly across the health systems of the world, which may lead to increased digital health disparities.

   Therefore, even though XAI and federated models resolve major ethical issues, their combination requires major technical and organisational investments.

7. ARTIFICIAL INTELLIGENCE IN MOBILE AND REMOTE HEALTH SYSTEMS.

   Mobile health systems have taken centre stage in improving access to clinical care to remote, underserved and chronically diseased groups. The fact that the number of mobile health applications is more than 40,000 shows a fast-growing ecosystem which takes advantage of behavioural, physiological, and contextual data streams [7]. Such applications help in the detection of early symptoms, compliance tracking, and behavioural prompts.

   The quality of m-health analytics, however, is subjected to the interaction of the user, the quality of the device used, and the stability of the network. Even though sensor data, including

   heart rate, blood pressure, and sleep statistics, provide predictive information, they can be affected by inconsistencies due to poor placement of devices, software and unpredictable environmental factors.

   Although m-health platforms help to relieve the burden on hospitals and create more convenience, there is a danger of flooding the clinicians with low-quality or noisy data unless they are filtered and contextualised properly. This highlights the significance of on-strong preprocessing algorithms, anomaly rejection systems and clinically tested AI architectures.

8. INTELLIGENT HEALTHCARE SYSTEMS AND SMART HOSPITALS.

   Smart hospital ecosystems combine AI and robotics, IoT devices, augmented reality, and data science to enhance operational efficiency and the quality of clinical care. Systems like MYCIN in 1970s and AESOP in 1994 were some of the first adoptions in history, and they proved that AI could be useful in infection diagnosis and precision in the surgical operation [11]. The ZEUS (2001) and the Da Vinci (2003) systems broadened the scope of robotic-assisted surgery, and formed the basis of intelligent surgical systems.

   Most smart hospitals today use predictive analytics in scheduling of operation rooms, allocating resources and scheduling staff. It has been demonstrated that predictive models enhance the utilisation of an operating room because of the increase in accuracy of schedules and forecasting of the procedure [11]. This kind of optimisation decreases the delays associated with processes, enhances flow of patients and helps to manage costs.

   According to market data, the pace of the AI adoption has been increasing rapidly, with the global AI healthcare market growth rising to 55% in 2021 and the investment in AI diagnosis growing by 505 million in 2020 and to 387.87 billion in 2025 [11]. These are encouraging signs of a high level of institutional support and, at the same time, show that there can be a very dangerous issue in such a situation: The pace of implementation may exceed regulation tools, cybersecurity preparation, and training of clinicians.

   Therefore, the smart hospital systems can bring a great amount of efficiency to a company but also demand organisational change and a high level of technological maturity.

9. DIFFICULTIES, ETHICAL ISSUES AND LIMITATIONS OF THE SYSTEM.

   1. Data quality and heterogeneity

      Quality and consistency of underlying datasets is a critical requirement of AI systems. Unstructured clinical notes, imaging, genomics, and wearable outputs are heterogeneous data that make it difficult to train and deploy the model. These inconsistencies decrease the generalisability and reliability particularly in high-risk diagnostic applications [15].

   2. Privacy and Security Risks

      Wearables, mobile platforms and IoT devices store sensitive health information that can be hacked. Any attack on real-time health monitoring systems has the possibility of interfering with patient safety, clinical care, and trust. Privacy-preserving mechanisms such as federated learning involve a lot of computation and add new complexities in the operation [2].

   3. Computational Constraints

      Deep learning and IoT-based systems demand a large amount of computation power, memory, and energy. These conditions are prohibitive to the deployment of advanced AI models in the context of many healthcare systems, especially the low- resource setting [14].

   4. Algorithms: Fairness and Algorithmic Bias.

      Bias in clinical decision-making can be perpetuated by using training datasets that are biassed against certain groups of people. The Explainable AI literature also raises issues with regard to the possibility of models having discriminatory behaviour and especially predictive risk scoring and diagnostic classification [8].

10. CONCLUSION

    AI and machine learning have already turned into disruptive technologies in healthcare as it allows developing superior predictive analytics, customised clinical decision support, tele- monitoring platforms, and smart hospital management systems. It has been demonstrated through empirical evidence that there were quantifiable increases in diagnostic accuracy, treatment planning, operational efficiency, cost reduction, and early detection. These advantages however, can be attained only with stringent data management, ethical model construction, quality of data pipeline, and constant monitoring of performance.

    Heterogeneity of data, computation needs, fairness of algorithms, privacy, and trust between clinicians are significantly limited in the real-world implementation. The shift to Healthcare 5.0 does not just require technological savvy, but also a strong organisational presence, regulatory maturity, as well as interdisciplinary cooperation.

    This analysis has shown that the role of AI in healthcare is both huge an conditional in that it has the capacity to change the current state of affairs, but its use is constrained by the limitations of data, ethical and technical infrastructure.

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