Introduction
Great number of technological advances have occurred in the
field of data science during the past decade. Artificial
intelligence (AI) has revolutionized the healthcare system
[1]. AI tools can support healthcare personnel in
administrative workflow, clinical documentation, image
analysis and patient monitoring [2]. Among the several
applications of AI in healthcare, generative AI, capable of
generating new data such as text and images, has emerged as a
transformative tool in healthcare. Generative AI
technologies offer the promise of revolutionizing medical
research, disease diagnosis and patient care leading to
improved health outcomes. Generative AI applications can make
healthcare delivery more efficient and effective. Generative
AI is a class of machine learning technology that can generate
new synthetic data by learning the data distributions from
real data [3]. Generative AI-based models facilitate
streamlining of the clinical workflow, which leads to
improvement in health care [4].
Generative AI Models
Generative adversarial networks and large language models are
the two generative AI models, which are popular in the
healthcare industry.
Generative Adversarial Networks
Generative adversarial networks consist of two neural
networks, a generator and a discriminator, that compete
against each other. The generator creates data that looks
real, while the discriminator evaluates the authenticity of
the data. This competition drives the generator to produce
increasingly accurate data instances. Synthetic data, created
by neural networks, can retain the statistical relationships
of real data and it will offer privacy protection. Generative
Adversarial Neural Networks have the capability to model
complex real-world image data [5].
Large Language Models
Large language models are powerful generative AI models that
are important in various natural language processing tasks. It
is a powerful tool for sequence prediction tasks. The model
learns to predict the next word in a sentence, if the previous
words are given. Large language models can perform many
language processing tasks and these models can be used to
generate summaries of text. Large language models enhance data
management, information retrieval and decision-making
processes in healthcare industry [6]. The integration of large
language models into medical education can elevate the
knowledge and competence of the students [7].
Recent Advancements in Generative AI
The field of generative AI has witnessed remarkable
advancements, driven by methodological innovations. Deep
transfer learning merges the superiority of deep learning in
feature representation with the merits of transfer learning in
knowledge transference. This synergistic integration propels
deep transfer learning to the forefront of research [8].
Applications in the Healthcare Industry
Technological innovation has become an integral aspect of
healthcare industry. Generative AI helps in prioritizing
administrative tasks based on urgency. Generative AI can
streamline the appointment scheduling process by automating
the booking and optimizing available time slots. Generative AI
helps in automating documentation tasks, such as updating
patient records. It will reduce the administrative burden on
healthcare professionals. Generative AI can extract relevant
information from various sources. It will minimize manual data
input and enhance the accuracy of healthcare databases.
AI-powered chat bots can handle routine inquiries, appointment
reminders, and follow-ups. This will allow healthcare staff to
focus on more important aspects of patient care.
Deep Learning and Medical Image Analysis
Machine learning has witnessed significant attention all over
the world during the last decade, when deep artificial neural
networks began outperforming other established models in many
important benchmarks. Developments in deep learning have a
huge potential for medical image analysis. Deep neural
networks are now the state-of-the-art machine learning models
for image analysis [9]. Novel deep learning techniques are
predicted to be the core of future analysis of medical images
[10]. In radiology, artificial intelligence has important role
in case interpretation [11]. Deep learning is increasingly
used in radiology. Deep learning methods are effective in
classifying images and detecting pathological lesions [12].
Deep convolutional networks are actively used for medical
image analysis which includes abnormality detection, disease
classification and computer aided diagnosis [13]. Generative
AI helps to enhance the quality of medical images and aid in
more accurate diagnoses. Generative models can be trained to
generate high-resolution images from lower- resolution inputs.
Generative adversarial networks have opened several ways to
solve challenging histopathological image processing problems
such as colour normalization and image enhancement [14].
Generative AI can contribute to identifying pathological
conditions, supporting early diagnosis and intervention.
Generative models can synthesize images of organs, which help
in training medical professionals. By generating insights from
medical imaging data, generative AI can support healthcare
professionals in the decision-making processes.
Generative AI in Precision Medicine
Precision medicine uses information about an individual's
genomic, environmental, and lifestyle information to guide
decisions related to their treatment [15]. Precision medicine
provides healthcare interventions to patients based on their
disease profile, diagnostic information and treatment
response. This method of treatment takes into consideration
the genomic variations and other contributing factors such as
demographic data, immune profile and metabolic profile.
Machine learning algorithms are used with large datasets such
as electronic health records to provide the correct treatment
strategy. AI models are used for integrating multi-omics data
and electronic health records for precision medicine [16]. By
analysing patient-specific data, including genomics and
proteomics, generative AI can support the development of
personalized medicine approaches, tailoring treatments to
individual patients. Generative AI can assist in identifying
biomarkers associated with specific diseases, aiding in the
development of targeted therapies.
Generative AI in Medical Research
Generative artificial intelligence has the ability to generate
summaries of highly technical scientific texts. The
information obtained from generative AI should be verified for
accuracy before use [17]. Generative AI can quickly analyse
large amounts of medical data, automating data extraction.
This will help the researchers to concentrate on more critical
aspects of their work. Generative AI can summarize medical
documents, providing concise data for researchers. This aids
in faster decision-making, while dealing with extensive
medical literature. Generative AI utilizes the analysis of
extensive datasets to examine trends within medical research.
This capability will enable researchers to remain up-to-date
with the most recent advancements.
Conclusion
Artificial Intelligence is rapidly transforming the healthcare
industry by bringing unprecedented tools for patient care. The
time-saving measures provided by generative AI allow
healthcare professionals to focus more on providing better
patient care. New healthcare technology trends are constantly
emerging to meet the needs of healthcare organizations. By
accelerating personalized treatment plans, enhancing
diagnostic accuracy and generating synthetic data for
research, generative AI is paving the way for groundbreaking
advancements in healthcare industry.
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