Machine learning is one of the powerful tools that when used gives us a high and accurate result, especially in vital fields.

Especially complex issues that need high speed and accuracy. Machine learning relies on the data provided to it as an essential element to solve any problem. In view of the rapid growth of data in the current era, it required finding a new approach of organization and processing in order to make it effective for using data in building these models [1]. The healthcare industry has been facing ongoing difficulties in the implementation and feasibility of digital technologies. The integration of different health systems has been progressing slowly, and achieving a completely unified healthcare system worldwide has not been successful. The intricate and diverse nature of human biology and the individual differences between patients emphasize the significance of the human element in disease diagnosis and treatment. Nevertheless, healthcare professionals are increasingly relying on digital technologies as essential resources to offer optimal care to patients [2].

The broad adoption of machine learning in many industries, including healthcare, has been made possible by developments in data technologies such as higher storage capacity, better data transport speeds, and improved computer power. The complexity of providing high-quality healthcare has caused current medical trends to change in favor of customized treatment, sometimes known as "precision medicine." Utilizing extensive healthcare data to identify, predict, and analyze diagnostic decisions that doctors can then apply to each unique patient is the main goal of personalized medicine. These data cover a wide range of information, including genetic or family history, imaging data from medical procedures, drug interactions, patient health outcomes based on populations, and natural language processing of pre-existing medical records [3].

Our main focus will be on three major implementations of machine learning (ML) in the medical and biomedical domains [4]. Given the constantly evolving nature of this field, there are numerous possible applications of machine learning in healthcare that could involve peripheral aspects such as staff management, insurance protocols, regulatory compliance, and other related areas. Therefore, we have narrowed down the scope of this article to three prevalent applications of machine learning:

The first thing is Analysis of medical pictures, such as those from positron emission tomography (PET) scans, ultrasound imaging, computerized axial tomography (CAT) scans, and magnetic resonance imaging (MRI), is one of the principal uses of machine learning. A set or series of images generated by these imaging modalities typically require the interpretation and diagnosis of a radiologist. The process of anticipating and detecting images that could indicate the existence of a sickness or a critical condition has evolved greatly with the help of ML algorithms.

The second application deals with medical document using natural language processing (NLP). The use of electronic medical records (EMR) has been encouraged in a number of nations, however many healthcare professionals have found the procedure to be slow, onerous, and occasionally ineffective, which could lead to subpar patient care. The voluminous physical medical records and paperwork that are present in various hospitals and clinics is one of the main challenges. The adoption of electronic medical records has been impeded by a variety of formats, handwritten comments, and an abundance of incomplete or dispersed information, leading to inefficiencies in the system.

The third application of machine learning involves utilizing human genetics to forecast and identify causes of diseases. Next-generation sequencing (NGS) techniques and the proliferation of genetic data, which includes comprehensive databases of population-wide genetic information, have enabled researchers to explore how genetics may influence human health. Uncovering meaningful insights into the manifestation of complex diseases and how genetics may augment or diminish the likelihood of disease onset is now at the forefront of many scientific investigations. By comprehending these dynamics, preventative healthcare measures can be established. This approach could equip physicians with greater knowledge on how to customize a patient's care plan to minimize the risk of contracting complex diseases.

In conclusion, the integration of machine learning in healthcare has the potential to revolutionize the way healthcare is delivered to patients. The three major applications discussed in this article demonstrate the potential of machine learning in addressing some of the challenges faced by the healthcare industry, such as improving the accuracy and efficiency of medical imaging interpretation, automating the process of extracting information from medical records, and utilizing genetics to forecast and identify the causes of diseases. While there are still challenges to overcome in the implementation and feasibility of digital technologies in healthcare, the advancements in data technologies and machine learning hold promise for improving patient care and advancing medical research. As the field of machine learning continues to evolve, it is likely that we will see even more innovative applications of this technology in the healthcare industry in the future.

References:

1. H. Houssein, Z. Abohashima, M. Elhoseny, and W. M. J. E. S. w. A. Mohamed, "Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision," p. 116512, 2022.
2. Shailaja, K., Seetharamulu, B., & Jabbar, M. A. (2018, March). Machine learning in healthcare: A review. In 2018 Second international conference on electronics, communication and aerospace technology (ICECA) (pp. 910-914). IEEE.
3. Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., & Li, X. (2021). A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mobile networks and applications, 26, 234-252.
4. Chen, P. H. C., Liu, Y., & Peng, L. (2019). How to develop machine learning models for healthcare. Nature materials, 18(5), 410-414.