ARTIFICIAL INTELLIGENCE IN HEALTHCARE: IMPROVING DIAGNOSTIC AND TREATMENT

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By: Rida Jamal and Khadeeja Qadeer

In this modern era, Artificial intelligence is emerging as a global phenomenon with far-reaching impacts on various spheres. We are witnessing AI-driven technologies and the role of machine learning in many of our daily activities. So, it goes without saying that AI has become an integral part of our everyday existence. Just as we use AI models for our assistance in various tasks, Artificial Intelligence has also revolutionized the field of healthcare, offering innovative solutions. Its applications are diverse, ranging from diagnostics to personalized treatment plans. From improved medical imaging analysis to predictive analytics for personalized treatment plans, AI's capabilities have led to more accurate and efficient healthcare practices. 

   Within the healthcare sector, Artificial Intelligence offers a valuable asset. Artificial intelligence can come in handy in the analysis of medical data, which can be categorized into structured data (which is obtained through research presented in a well-organized format) and unstructured data (which involves human language, imaging, etc.). AI tools are implemented to process, interpret, and synthesize a range of data, enabling seamless integration and efficient utilization in the healthcare domain. Machine learning stands as the prevalent technology that falls under the area of Artificial Intelligence, employing statistical techniques to develop models from data.

    Neural networks, a subset of machine learning, find applications in various domains, including predicting a patient’s likelihood of developing a particular disease. They excel at categorization tasks, allowing healthcare professionals to make informed decisions based on data-driven insights[1]. Neural networks play an ever-growing role in speech recognition, making them a vital component of Natural Language Processing (NLP). In healthcare, NLP finds extensive use in tasks like generating, comprehending, and categorizing clinical documentation and research articles. NLP systems are excellent at transcribing patient interactions, creating reports, analyzing unstructured clinical notes, and powering conversational Artificial Intelligence. This comprehensive strategy improves the effectiveness of healthcare providers and enables smooth communication between patients and Artificial Intelligence technologies [2].

  Early detection is something that everyone looks forward to when it comes to diagnosis. In the domain of diagnostics, the integration of AI holds immense potential. By harnessing the power of AI, healthcare systems can enhance diagnostic capabilities, leading to early detection of diseases and personalized treatment plans. In the context of diseases like cancer, AI is particularly important for early identification and diagnosis. It allows for more precise, dependable, and swift disease detection through various approaches. AI systems are able to analyze and spot patterns indicating diseases by analyzing enormous amounts of patient data, including medical photographs. The independent learning capabilities of these systems allow them to detect associations and propose potential diagnoses, aiding healthcare professionals in making well-informed decisions for improved patient outcomes [3,4].

     Primary care physicians (PCPs) and their patients stand to gain much from the application of AI algorithms in disease diagnosis. PCPs can broaden the services they provide and produce more thorough and precise diagnoses by utilizing these technologies. Furthermore, AI-driven diagnostic technologies eliminate the need for pointless specialist referrals, maximizing healthcare resources and reducing patient wait times. The innovative approach also addresses the issue of limited access to specialty care in underserved regions, ensuring that patients can receive healthcare services both timely and effectively [4].

    The field of precision medicine has also seen profound implications due to Artificial Intelligence. Ongoing advancements and revolutions in the healthcare sector have resulted in the development of numerous diagnostic instruments, leading to advancements in disease diagnosis and treatment. In these modern times, the healthcare field demands transformation through disruptive technologies like bioinformatics, which make use of Artificial Intelligence to analyze vast amounts of biological data. This advantage of AI not only improves the accuracy of diagnoses but also creates new opportunities for personalized and targeted therapies [5].     

     Artificial Intelligence has facilitated coherent connectivity between doctors, nurses, and patients across geographical and time zone barriers. This makes it possible for remote diagnostics and care administration, facilitating both patients’ and healthcare providers’ participation whenever and wherever they wish. This could link rural populations with knowledgeable consultants, provide advanced medical diagnosis or treatment techniques to remote areas or other countries, and possibly accelerate them [6]. Data-driven techniques also play a vital role in strengthening risk prediction performance by uncovering new risk predictors and understanding their intricate interactions. The development of risk prediction algorithms typically involves the use of multivariate methods that take into account a variety of outcome-related factors. For example, in cardiovascular disease (CVD), the link between risk factors and outcomes is examined in a linear fashion [1,4].

   Just as every coin has two sides, so too do all things come with both advantages and disadvantages. Artificial Intelligence has a few limitations as well. Collection of massive data sets is one of them. The primary challenge lies in accessing relevant data. AI models rely on massive datasets to effectively classify and predict a wide array of tasks. However, the healthcare industry faces a complex issue concerning data accessibility, which hinders the full potential of AI advancements in healthcare. The availability of insufficient data also leads to data bias, which is unacceptable in the healthcare domain [3,7]. In addition to that, ethical concerns are yet another issue. Patients often prioritize keeping their medical data private. Healthcare practitioners find it ethically important to respect their patients’ identities, personal data, and sensitive medical data [3,7].

   In conclusion, the potential of Artificial Intelligence as a powerful tool in healthcare and diagnosis is undeniable. Artificial intelligence has become a major force for change in the healthcare industry, providing ground-breaking solutions and previously unheard-of chances for better patient care. With proper implementation and refined data collection, we can make use of AI models in an upgraded manner to take the field of medical care to new heights.

 

REFERENCES:

1. Butryna. B, Chomiak-Orsaa. I, Haukea. K, Pondela. M, Siennicka. A. (Nov 4, 2021). Application of Machine Learning in medical data analysis. (Poland). Elsevier. http://dx.doi.org/10.1016/j.procs.2021.09.086

 

2. Davenport. T, Kalakota. R. (Jun 2019). The potential for artificial intelligence in healthcare. (USA). Future Healthcare Journal.

doi: 10.7861/futurehosp.6-2-94

 

3. Sunarti. S, Fadzlul Rahman. F, Naufal. M, Risky. M, Febriyanto. K, Masnina. R. (2021). Artificial intelligence in healthcare: opportunities and risk for future. (Indonesia). Elsevier

DOI: 10.1016/j.gaceta.2020.12.019

 

4. Lin. S. (16 Jan 2022). A Clinician’s Guide to Artificial Intelligence (AI). (USA) Journal of the American Board of Family Medicine

doi: 10.3122/jabfm.2022.01.210226

 

5. Gupta. N. S, Kumar. P. (Aug 2023). Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. (India). Science Direct.

https://doi.org/10.1016/j.compbiomed.2023.107051

 

6. Tursunbayeva. A, Renkema. M. (27 April 2022), Artificial Intelligence in health-care: implications for the job design of healthcare professionals. (Italy). John Wiley & Sons.

doi:10.1111/1744-7941.12325

 

7. Khan. B, Fatima. H, Qureshi. A, Kumar. S, Hanan. A, Hussain. J, Abdullah. S. (Jan 2023). Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. (Pakistan). Springers.

doi: 10.1007/s44174-023-00063-2

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