AI and machine learning technologies are revolutionizing the future of
healthcare as we know it, and for a good reason. Both techniques
essentially consist of a family of technologies that enable machines
(like computers) to ‘think’ like humans. AI broadly covers the
technologies that enable humans and machines to undertake tasks. In
contrast, machine learning is a subset of AI technologies that allow
us to program algorithms or specific instructions that perform tasks
so that they can adjust and ‘learn’ as data sets change over time. AI
and machine learning are being explored in some ways to improve
healthcare, as more attempts than ever before are made to drive
efficiencies and diagnose conditions or symptoms to improve patient
care. AI and machine learning are enablers for a future where targeted
and more personalized healthcare is met, especially as utilization
increases and the need for cost-effective and efficient solutions
soars.
These moves work in tandem with the profound impact of
mobile apps in the healthcare sector. It is undeniable that the level
of mobile usage for various social, business, and other online
activities is increasing with the rise of smartphone usage and the
popularity of wearable devices. Consequently, numerous healthcare
companies provide their users unprecedented access to medical
information and resources, doctor’s consultations, health management,
helpful and informative content, etc. All of this helps promulgate the
idea that healthcare facilities and services are now increasingly
becoming available to users – thanks in part to AI-powered mobile apps
helping to drive the concept of healthcare consumerisation wherein
patients are encouraged to take ownership of their health destiny from
their mobile devices. The combination of mobile technology with AI and
machine learning is poised to drastically improve healthcare services,
allowing for unprecedented personalized user experience. This makes it
possible to cater healthcare services to the needs of individual
users. This article takes a closer look at AI and machine learning’s
impact on healthcare mobile apps – to explore how advancing
technologies are shaping the future of patient care and engagement.
Artificial Intelligence (AI) describes machines that a human brain can
do. This includes technologies such as natural-language processing,
computer vision, and robotics. AI is often a broad-brush term covering
many other technology areas. A subset of AI is the discipline known as
machine learning, the area of study in which algorithms enable
computers to learn from data and make predictions. This involves an
iterative process during which a computer knows how to find patterns,
adapt to new information, and constantly improve its performance
without being explicitly programmed to do so for specific tasks.
The
principal benefit conferred by Artificial Intelligence (AI) rather
than traditional software solutions is that the latter are static in
use and functionality and work according to rules and logic that
enable solutions to specific problems but can’t be learned by
experience or data. AI applications are tailored to constantly receive
incoming data, thus allowing them to be more dynamic as conditions
change. The applications of such technologies to healthcare are
widening across diagnostics, personalized medicine, and operational
processes. For instance, AI algorithms can be applied to interpret
medical images that could indicate the presence of disease or for
diagnosing conditions in patients, and machine learning models applied
to data obtained from patients in previous years can be used for
predicting the outcome of a patient’s condition and treatment. AI and
machine learning, therefore, provide health providers with the means
to improve decision-making and, consequently, their care of patients
while enhancing the efficient use of operational processes.
AI algorithms can facilitate the personalization of healthcare mobile
apps by learning from users’ data to create more individualized app
experiences. These algorithms, sometimes training machine-learning
models like the ones outlined above, can process and analyze diverse
information about users, which can help them understand their health
needs. This information may include medical history, vital signs, user
activity, preferences, and demographics. Over time, an app designed
for a chronic illness might require patients to enter information
about their blood glucose levels, the types of foods they eat, whether
they exercise, how often and the intensity, what they’re doing at that
time of day, and many more data points. These data valences can be
integrated to make actionable inferences about health needs, such as
recommending personalized meal plans and activities for a user.
Alongside
the obvious benefits of more personalized and targeted health
recommendations, comprehensive and detailed guidance improves patient
engagement and adherence to treatment plans. User engagement is a
pathway to better patient outcomes. When patients receive health
advice that speaks to their specific priorities or clinical
challenges, they experience greater motivation and empowerment to take
control of their health. For instance, tailored recommendations to
take drugs or schedule checkups can lead to greater adherence and
improved health outcomes. Overall, tailored user experience increases
the bond between patients and healthcare providers by allowing
patients to feel heard and supported on their health journeys. This
can be especially critical when managing chronic disease, as
consistent monitoring and intervention is necessary for successful
outcomes.
AI is also revolutionizing diagnostics by improving the performance of
symptom checkers and early diagnostic tools. Using an immense amount
of historical data – including entries of patient histories, clinical
guidelines, and research – these AI-assisted platforms utilize the
symptom checker’s input, score the combination of symptoms against
datasets, and offer preliminary assessments and initial diagnoses to
allow users to access the appropriate level of treatment. For example,
an AI symptom checker may correlate a high fever with a cough and
fatigue and recommend a diagnosis of either the flu or COVID-19 for
the user’s review and potential involvement of their doctor. An early
warning system facilitated by AI would help individuals achieve the
right kind of attention for quicker and more accurate responses from
the healthcare system.
This is especially true for integrating AI
into diagnostic processes, which can guide healthcare professionals to
assess the most critical and nuanced aspects of care. When AI-powered
tools can make preliminary, they can focus on more complex elements of
examining their patients. Just another physician or a checklist of
considerations, technologies can be designed to provide valuable
support to healthcare providers by reducing the risk of missing key
details when making diagnoses. Simultaneously, by the short time spent
analyzing patient information, they can have more time to dedicate to
face-to-face interactions, potentially addressing more aspects of
care. AI diagnostics and healthcare professionals’ skills can guide
richer and more comprehensive decisions, providing better patient
outcomes in clinical settings.
Predictive analytics systems are one of the most important emerging
applications of machine learning models in healthcare today. By
examining millions of individual patients’ data over time, machine
learning models can identify predictors and trends that might
correlate with the development of illness and its early triggers. For
instance, individual patients’ lifestyles, genetics, and previous
medical histories may be analyzed to predict the onset of diabetes or
heart disease. This data processing allows healthcare professionals to
understand trends in population health better and enables forethought
in reducing the risk and proactively treating rising health
concerns.
Early intervention drawn from predictive data will be
critical. When predictive analytics help healthcare providers
recognize forthcoming health conditions, that provider can better stop
the damage and disease of those conditions – or better yet, prevent
them altogether. For instance, if a machine learning model predicts
that a patient has a high probability of developing a chronic
condition in the coming months, a provider can step regular screenings
or medication adjustments – an approach that adds years to that
patient’s life, reduces her emergency treatment and hospitalization
and saves money on total healthcare costs. In this way, predictive
analytics through machine learning works through forward-thinking
means to improve health and reduce strain on the healthcare system.
Because of predictive physical, psychological, and behavioral health
insights that can be gained through learning machines, we’ll be better
able to focus on proactive care instead of reactive care, making us
healthier and vastly improving our experiences in healthcare.
Features such as AI-managed chatbots with automated reminders are
highly attractive and engaging for users because of their
instantaneous support and personalization capabilities that help users
navigate healthcare apps, answer common queries, share about treatment
plans or medication schedules, and more. Giving access to health
management anytime within an app and making healthcare easily
accessible increases the chances that the user would engage regularly
with the app. Other attractive features to users can be automated
medication reminders for future appointments or health check-ins.
These features consistently keep the patient engaged with their
healthcare journey, which builds tremendous responsibility towards
their adherence to treatment plans and gives them a sense of ownership
over their healthcare.
Gamification, which uses game mechanics
such as points, badges, quests, and leaderboards, is another
beneficial motivational tool. By creating a rewarding experience for a
game, patients are much more likely to remain active within the app.
For example, ‘currency’ in the form of points or badges can be awarded
to users when they perform health-related tasks, such as checking
important vitals, completing a wellness challenge, or achieving a
fitness goal. By rewarding users for doing these things and by
encouraging friendly competition among the users via leaderboards and
friendly competition, healthcare apps can become far more engaging
and, in turn, motivating. According to success stories from these
healthcare institutions, an increasing retention rate of mostly
popular AI-driven features has been seen due to patients feeling more
connected to their health-related lives and involved in their
self-care journey. All of this engagement leads to better health
outcomes as there are more long-term interventions, which means that
patients are more likely to stick with their treatment plan and
receive timely preventive care, which is the main pillar of good
health.
One of the emerging trends currently reshaping the future of
healthcare mobile app development is to embrace the vast potential of
AI and machine learning technologies. From now on, we will see more
healthcare applications integrating more AR and VR features that can
improve training for healthcare providers, offer immersive therapeutic
experiences for patients, and assist remote consultations by allowing
the providers to see the patient data in context. Natural language
processing (NLP) will also be one of the key technologies that will
help improve the interaction between users and the apps. This approach
will allow more conversational and intuitive voice commands and
text-based conversations between mobile users and healthcare apps.
AI
will make even deeper inroads in telemedicine and remote patient
monitoring in the next decade. Becoming increasingly popular,
telemedicine will likely benefit from AI, as algorithms could be able
to analyze patient data in real-time, helping providers make quick
decisions while performing virtual consultations. For example, AI
could be useful in triaging incoming patients based on their priority
by reviewing symptoms and histories during a virtual visit. In
addition, remote patient monitoring devices that carry AI algorithms
can continuously collect and analyze health data, alerting patients to
take more timely actions when there are any clear signs of
abnormalities. This would allow for higher quality of care and
dramatically improve patient autonomy.
In summary, AI/machine learning is creating groundbreaking new applications for healthcare mobile apps that enable more accurate and efficient diagnostics, interpretations and health management. However, as these technologies continue to evolve, we will see a higher level of personalization for enhanced patient outcomes and care and greater accuracy in diagnostics and predictive analysis for more proactive health-maintenance approaches to the industry. In the end, by utilizing these emerging technologies and continued learning systems, these mobile applications will become more user-friendly and effective when meeting people's unique needs.
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