Personalized
health care is a framework that provides individuals with a personal health
plan to maximize their health and minimize disease.
Personalized
medicine is revolutionizing the healthcare industry, and the integration of
artificial intelligence is fuelling this transformation. AI technologies have
significantly advanced our ability to analyse vast amounts of complex data,
leading to improved diagnosis. AI
algorithms can also predict drug efficacy, identify biomarkers linked to
response, and reveal optimal patient profiles for drug therapies.
How AI
can help in personalized healthcare
- Disease
prediction: AI-based disease detection and machine learning for medical
diagnosis are used to identify outbreaks and predict disease spread based on
data from various sources. Also, it can also analyse genetic sequences of
viruses to predict their virulence or resistance patterns.
- Diagnosis:
By leveraging machine learning and deep learning algorithms, AI can process
vast amounts of data accurately. These advancements are improving the precision
of diagnoses and also enabling early detection.
- Treatment:
nowadays, robotic surgeries are used because they can do complex procedures
with more precision. Robotic surgery is done mainly parts like knee, heart,
kidney, gallbladder, spine, etc.
How AI helps
in analysing genetic data
Pattern
recognition agent is used in which ai agents analyse and identify hidden
patterns. They detect genetic variations linked to diseases by using machine
learning. This helps in making diagnosis more accurate. AI with ML helps also
in transforming how the Next-Generation Sequence (NGS) which is a big parallel sequence
which gives high speed and scalability is analysed to improve its accuracy.
In which
diseases is AI used for personalized healthcare
- Cancer: Although doctors are already doing treatment
of cancer, AI can speed up the process. It can also help in detecting cancer
drug discovery which without AI is a very costly process.
- Cardiovascular diseases: By using deep learning (a type of machine learning
that uses artificial neutral network) AI can identify coronary atherosclerotic plaques
(build-up of fats, cholesterol and other substances in and on the artery walls)
more accurately than clinicians. Ai can also help in treating heart failure, atrial
fibrillation, valvular heart disease, etc.
- Diabetes:
AI helps in customizing diabetes treatment plans for better patient outcomes.
It can also predict risks in treatment making it easier for doctors.
Benefits
of using AI in personalized healthcare
- More
accurate screenings. I can easily pick out dark spots which takes hours to do
without AI.
- Removing
boring work like clinical notes, form filling, etc.
- Optimizing dozes
- Easily
analysing complex data
Disadvantages
of using AI in personalized healthcare
- Acquiring
enough data to train precise algorithms
- Risks with
data sharing.
- High costs of
development and implementation.
- Accountability concerns to identify what or who
is responsible in the event of an error.
AI wearables
used in personalized healthcare
- Oura Ring:
This ring tracks various health metrics, including sleep quality, heart rate,
body temperature, and activity levels using sensors. The Oura Ring uses AI to
analyse this data such as heart rate variability, body temperature, and
movement data and provide users with personalised recommendations.
- WHOOP: WHOOP focuses on optimising athletic
performance and recovery. The WHOOP Strap continuously monitors heart rate,
sleep, and activity levels, providing users with good understanding of their
body’s needs
- Muse 2
headband: The Muse 2 is a smart headband
that acts as your personal meditation coach. Using advanced EEG brain
sensors, it can detect your brain activity and provide you with real-time
feedback in the form of gentle audio sounds through headphones.
- Smart
watches: smart watches can be used for tracking heartbeat, good sleep and even oxygen
levels.
Future of
AI in personalized healthcare
The future
of AI-powered personalized medicine looks promising, with many potential
advancements in healthcare. However, some challenges must be overcome to make
the most of it. These include:
- Ensuring different healthcare
data systems can work together.
- Integrating AI smoothly into
medical processes.
- Creating clear rules for
AI-based medical tools.
- Making sure everyone has fair
access to personalized treatments.
Bottom
line
AI is
changing healthcare by making diagnosis and treatment more accurate and
personalized. While there are challenges like data privacy, its benefits are
huge. With ongoing progress, AI will help create better and more efficient
healthcare for everyone. |
