By: Syed Raiyan Ali – syedraiyanali@gmail.com, Department of computer science and Engineering( Data Science ), Student of computer science and Engineering( Data Science ), Madanapalle Institute Of Technology and Science, 517325, Angallu , Andhra Pradesh.
ABSTRACT –
Artificial Intelligence (AI) is becoming better in different areas, including healthcare, by making it possible to detect and predict diseases at an early stage. This paper explores an extensive study on predictive medicine that is based on AI and discusses its likelihood to transform medical practice by using advanced algorithms and learning machines that will help us in predict and detect illnesses hence taking quick decisions faster. The objective of this text is to analyze pertinent studies regarding current state of AIs role in healthcare as well as to offer insight into how they can enhance early diagnosis, drawbacks experienced and possible directions for AI based health systems ultimately increasing understanding of some components of this intricate process.
Keywords: Artificial Intelligence, Predictive Healthcare, Early Disease Detection, Machine Learning, Health care Systems
INTRODUCTION-
The integration of Artificial Intelligence (AI) in health care has given positive results in improving patient diagnostic accuracy, prediction of which disease the patient might have and improving the way patients are taken care of in the health care system. AI can study and handle huge datasets and identify the different patterns in that data which might be difficult for the health care experts which makes it a powerful tool in early disease detection. [1]This article also showes the current applications of AI in the field of predictive health care, focusing on the important benefits of it, the challenges faced, and the future pathway of AI in medicine.
THE ROLE OF AI IN PREDICTIVE HEALTH CARE-
The implementation of AI algorithms to assist or automate decision making processes In the field of health care may be the future way to assist doctors. The algorithms are capable of analyze complex medical data to predict disease onset, progression, and potential outcomes. For instance, Machine Learning have been developed to predict and classify the chronic diseases such as diabetes, cardiovascular diseases, and cancer. By understanding and using the patient historical data these models can identify high risk individuals and recommend early interventions.[2]
APPLICATIONS OF AI IN EARLY DISEASE DETECTION
- Chronic Disease Prediction: Chronic diseases have been predicted extensively using machine learning models. It was discovered through a review of current techniques that support vector machines (SVM), clustering, as well as logistic regression (LR), are some of the widely adopted ones for the diagnosis. The use of these models yields facilitation in disease classification and predicting chances of having an illness, thus promoting pro activity in medical help provision.
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- Infectious Disease Management: Also, AI has potential to manage infectious diseases. Predictive models analyze epidemiological data predicting outbreaks and spread of illness thereby assisting health care providers in their planning and allocation of resources. This is very useful in the management of diseases like influenza and COVID-19.
- Cancer Detection: There are AI algorithms for recognizing patterns and in the early detection of different kinds of cancer using their images for analysis. For instance, deep learning models should be able to assess some specific medical pictures like mammograms or CT scans so that their potential risks are correctly recognized at an early stage.[4]
The below Table shows the disease type and AI models used to direct them and the use of algorithms
Disease Type | AI Model | Application |
Chronic Diseases | SVM, Logistic Regression | Classification, Prediction |
Infectious Diseases | Deep Learning | Outbreak Prediction |
Cancer | Image Analysis | Early Detection |
Table 1 AI models used to detect the disease and there application type
EXPLAINABLE AI IN HEALTHCARE
In the adoption of AI in health care, one of the major problems that need to be addressed is the lack of interpretability in certain AI models. Explainable AI (XAI) has emerged as an approach to address this problem by making the decision process of AI models accessible to the health care community. It also helps clinicians know how predictions are made, which fosters acceptance of AI-generated recommendations. The application of AI in practical clinical settings largely depends on this level of openness[5].
CHALLENGES AND ETHICAL CONSIDERATIONS
Even though AI has a great potential, the implementation of AI in health care comes with many challenges. The below mentioned are the possible challenges:
- Data Quality and Bias: The AI model’s accuracy is determined by the kind of information or data we give to the model during its training phase. Incorrect forecasts can emerge when wrong or inadequate data are involved in the learning process, thus maintaining the existing disparities in health care services[6].
- Regulatory and Ethical Issues: In the area of medicine, there are concerns about confidentiality, safekeeping of data and the recognition of expertise in regards to artificial intelligence applications. Alterations to legal frameworks need to be addressed in order to manage these issues and ensure that AI is utilized responsibly.
- Implementation and Scalability: Incorporating artificial intelligence into current healthcare frameworks calls for major transformations in both technology and processes[7]. One of the obstacles that has to be overcome is ensuring that AI models are adjustable and can fit smoothly in clinical settings.
FUTURE PROSPECTS
Emerging technology and data science advancements make AI future in preventive healthcare look good. With time, the accuracy and speed at which these AI predictions get made will improve. This will eventually result in improved patient outcomes. This can only be achieved through collaboration among AI developers, healthcare practitioners, and policy-makers who are interested in enhancing innovation within their fields of work.
CONCLUSION
For the future of healthcare, AI promises to transform predictive medicine through early disease identification and better patient outcomes. However, it has its challenges, particularly concerning data quality, explain ability and ethical concerns. The use of artificial intelligence will enable a shift from reactive to preventive health care systems thus improving the quality of лечениеand patients’ well-being.
REFERENCES
- M. Y. Shaheen, “Applications of Artificial Intelligence (AI) in healthcare: A review,” Sci. Prepr., Sep. 2021, doi: 10.14293/S2199-1006.1.SOR-.PPVRY8K.v1.
- C. C. Yang, “Explainable Artificial Intelligence for Predictive Modeling in Healthcare,” J. Healthc. Inform. Res., vol. 6, no. 2, pp. 228–239, Jun. 2022, doi: 10.1007/s41666-022-00114-1.
- D. Jain and V. Singh, “Feature selection and classification systems for chronic disease prediction: A review,” Egypt. Inform. J., vol. 19, no. 3, pp. 179–189, Nov. 2018, doi: 10.1016/j.eij.2018.03.002.
- T. Saba, “Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges,” J. Infect. Public Health, vol. 13, no. 9, pp. 1274–1289, Sep. 2020, doi: 10.1016/j.jiph.2020.06.033.
- D. Saraswat et al., “Explainable AI for Healthcare 5.0: Opportunities and Challenges,” IEEE Access, vol. 10, pp. 84486–84517, 2022, doi: 10.1109/ACCESS.2022.3197671.
- M. Rahaman, C.-Y. Lin, P. Pappachan, B. B. Gupta, and C.-H. Hsu, “Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control,” Sensors, vol. 24, no. 13, Art. no. 13, Jan. 2024, doi: 10.3390/s24134157.
- M. Rahaman, F. Tabassum, V. Arya, and R. Bansal, “Secure and sustainable food processing supply chain framework based on Hyperledger Fabric technology,” Cyber Secur. Appl., vol. 2, p. 100045, Jan. 2024, doi: 10.1016/j.csa.2024.100045.
- Tewari, A., & Gupta, B. B. (2020). An internet-of-things-based security scheme for healthcare environment for robust location privacy. International Journal of Computational Science and Engineering, 21(2), 298-303.
- Gupta, B. B., Gaurav, A., & Panigrahi, P. K. (2023). Analysis of security and privacy issues of information management of big data in B2B based healthcare systems. Journal of Business Research, 162, 113859.
Cite As
Ali S. R. (2024) AI in Predictive Healthcare: Early Detection of Diseases, Insights2Techinfo, pp.1