All Resources

6 Game-Changing Applications of AI for Doctors [+ Case Studies]

Ankit Vora
Published in
 
AI in Healthcare
  • 
7
 Min Read
  • 
January 17, 2025
Download Now
Try our AI scribe
Reviewed by
 
Lauren Funaro

Table of Contents

Remember the last time you made it home for dinner, or spent a whole weekend sans laptop?

If those moments feel rare, you're not alone. 

Between complex cases, packed schedules, and a mountain of documentation, finding time for life beyond the clinic is getting harder. (And we don't have to tell you that.)

Luckily, artificial intelligence tools are making a real difference in every clinician’s hectic life. 

Artificial intelligence (AI) for doctors can help you make evidence-based decisions, generate accurate SOAP notes, review medical images, and more. 

We’ll explore six key ways that AI makes making life easier for clinicians, helping you win back time for life beyond the clinic.

6 Transformative applications and examples of AI for doctors

Let’s learn about six ways AI can change your medical practice and free up your time—on and off the clock.

Task automation and documentation

There's nothing like a bottomless to-do list.

A study of health care systems across Canada found that physicians spend 18.5 million hours each year on unnecessary administrative tasks. 

Put another way: If this administrative time was eliminated, it would be like giving each physician in Canada an extra 3-4 weeks of vacation every year to spend with their families, pursue hobbies, and enjoy life outside the clinic. And it's the same across the U.S.

AI-powered task automation and documentation can slash the time spent on administrative task workflows

These AI systems, powered by large language models, use natural language processing and machine learning to do the busywork that takes up a huge portion of patient visits, not to mention the rest of a clinician’s day.

For example, ‎AI-powered scribes can listen to patient encounters and document every detail within minutes.

Ambient AI scribes will automatically generate crisp and accurate SOAP notes with all the key information from a patient conversation.

Here’s an example of a SOAP note generated by Freed:

Ambient AI scribe generated SOAP note

               

             
         

So, instead of typing away on their laptops, clinicians can give patients their undivided attention and have a meaningful interaction. 

This also means you don’t have to stay back in your clinic till late hours to chart notes from patient visits. AI virtual scribes will do all the heavy lifting on your behalf so you can sign off on time and be home for dinner. 

Besides, AI tools can improve operational efficiency by optimizing your scheduling workflows.

AI systems can prepare your daily schedule based on factors like patient preferences, room availability, staff shifts, and more. This schedule will prioritize urgent cases, optimize resource allocation, and reduce patient wait times. 

Impact of AI on task automation and documentation

  • Improves work-life balance and saves time using AI-powered documentation
  • Reduces errors in paperwork and enhances regulatory compliance
  • Increases efficiency in scheduling and use of resources

Real-life case study: Mount Sinai’s operations

Researchers at Mount Sinai Hospital developed an AI system that predicts whether ER patients need hospital admission. 

This study aimed to test the effectiveness of AI tools in determining hospital admissions, which is crucial for planning the hospital’s operations and resource allocation. It showed that tools like GPT-4 could support human physicians in making decisions about ER admissions. 

Doctors can also use these AI tools to:

  • Manage appointment rescheduling
  • Send patient reminders
  • Adjust their schedules based on real-time changes

Clinical decision support systems

A clinical decision support system (CDSS) processes data to offer evidence-based medical diagnosis for patient care. 

These systems analyze patient information from various sources, such as:

  • Lab results
  • The electronic health record (EHR)
  • Imaging tests

Then, they compare each case against a large database of medical condition records to help clinicians make better decisions.

The integration of AI into CDSS significantly improves the accuracy of medical diagnostics.

AI-powered systems can:

  • Analyze subtle patterns that current technology can miss
  • Provide probability average scores for different diagnoses
  • Minimize diagnostic errors to enhance precision

As a result, clinicians can evaluate multiple potential diagnosis possibilities—so you can make informed decisions about a patient’s condition.

More importantly, AI-driven CDSS offers personalized recommendations for patient care.

These tools can study patient-specific factors like medical history, current medication, comorbidities, and other details to suggest treatment options.

Impact of AI on clinical decision support systems

  • Enhances diagnostic precision to support evidence-based decision-making
  • Prevents medical errors through automated checks and real-time alerts 
  • Reduces time spent researching treatment options for each patient

Real-life case study: UpToDate’s AI-driven CDS 

UpToDate’s collaboration with Wolters Kluwer’s AI Labs is a great example of AI-powered CDS systems

This platform makes it easy for health care providers to find relevant insights and information across 12,400 topics. It also provides trusted, evidence-based recommendations using UpToDate’s database of medical literature.

A few studies have already demonstrated that this system can improve decision-making and the quality of patient care.

Medical imaging and diagnostics

Studies show that low resolution, high noise levels, and lack of proper contrast have been some of the biggest challenges in medical imaging.

Every radiologist knows the feeling: that moment of uncertainty when examining a difficult image or the pressure of working through a growing stack of scans that need review. 

That’s another key area where AI technology is making life easier for doctors. 

AI-powered medical imaging systems use deep learning algorithms to:

  • Enhance image quality
  • Pre-screen all images 
  • Flag potential concerns

Instead of struggling to decipher grainy, noisy images, clinicians can use AI technology to enhance the quality of images across different formats. 

Research proves that AI-enhanced images allow clinicians to detect minor anomalies or discrepancies. This brings a greater degree of accuracy and prevents the risk of oversight, especially during high-volume periods.

Besides quality assurance, AI-powered imaging systems can automatically identify potential abnormalities. They also highlight areas of interest that require a radiologist’s review. 

This would reduce cases of false positives or negatives, allowing clinicians to prioritize patients who really need their attention.

Healthcare providers can also automate routine image analysis workflows. That means you won’t be welcomed into your lab by a pile of reports waiting for review and diagnoses. 

Impact of AI on medical imaging and diagnostics

  • Accelerates workflows and reduces review time through automated pre-screening
  • Offers greater diagnostic confidence with AI validation and standardized reporting
  • Reduces turnaround time for imaging results and report creation

Real-life case study: Qure.ai’s qXR

Qure.ai developed a deep learning system, qXR. This system analyzes chest X-rays using AI to detect lung nodules and malignancies. 

The system is trained on 3.5 million X-rays and tested on 13,000+ X-ray images. It's shown exceptional accuracy, outperforming human radiologists in nodule detection. 

A study focused on qXR proposes the use of this AI system as a screening tool to detect incidental cases of lung cancer. Clinicians can also use this as a triage tool to expedite treatment for potential cancer cases.

Predictive analytics for patient care

Predictive analytics is one of the most powerful applications of AI for doctors. 

Think about it: What if you knew which of your diabetic patients might need intervention before their next scheduled visit? 

AI tools can process large streams of patient data to provide actionable clinical insights as an early warning system. They can analyze and identify patterns across historical records, lab results, lifestyle factors, and real-time vital signs.

As a result, clinicians can detect potential health concerns before they escalate into bigger risks and serious complications.

Predictive analytics system

               

             
         

AI-powered predictive analytics technology can:

  • Monitor subtle changes in vital signs 
  • Predict potential cardiac events or other risks
  • Alert care teams before conditions become critical

These AI tools go beyond traditional monitoring to focus on subtle patterns and signs often missed by traditional monitoring methods. 

In other words: these systems continuously monitor patient data from multiple sources to find at-risk patients in advance. It gives medical practitioners crucial time to diagnose the problem and deliver proper treatment.

Predictive analytics also allow organizations to group patients based on risk factors. This segmentation allows doctors to arrange proper measures and resources for preventive care.

Impact of AI on predictive analytics 

  • Enables earlier intervention to offer timely treatment and care 
  • Allows proactive staff and resource allocation to work more efficiently 
  • Promotes personalized care planning and follow-up schedules for each patient 

Real-life case study: ClosedLoop.ai

ClosedLoop uses data science and machine learning to provide health care practitioners with predictive analytics. 

This platform can analyze patient data to predict hospital readmission risks. It can also identify patients with potential health risks like diabetes, kidney disease, or heart failure. 

ClosedLoop uses 2000 variables from clinical, claims, labs, and demographic data to create customized risk scores for each patient.

Drug discovery and treatment planning

Traditionally, the drug discovery and development process takes 10-15 years. 

This new technology is accelerating this process to shorten the time for new drug development to a few days or months. 

Case in point: Insilico Medicine used AI to discover a novel drug target in just 18 months. This project was completed at 1/10th the cost of a conventional drug discovery program.

This is possible because of deep learning algorithms. These algorithms can simulate millions of interactions at a molecular level and screen potential compounds in a few days rather than years. 

AI technology can essentially eliminate the years of trial-and-error experiments to identify the most promising compounds. As a result, medical researchers can design, validate, and bring a new drug to market at a much faster pace during times of crises. 

Besides, AI tools give doctors quick access to potential drug interactions and clinical guidelines.

This means, clinicians can plan treatments tailored to each patient. AI tools consider multiple factors like genetic markers, lifestyle factors, and medical history to recommend personalized treatment plans. 

Impact of AI on drug discovery and treatment planning

  • Accelerates drug development timeline from 10-15 years down to months/days
  • Eliminates wasteful experiments and promotes more targeted drug development 
  • Allows personalized treatment planning based on each patient’s specific factors 

Real-life case study: Novartis 

Novartis scientists, in collaboration with Microsoft, are using AI to shorten drug development from years to weeks (or days).

These researchers use AI tools to sift through a vast database containing decades of lab experiment results. Then, they identify molecules with the desired characteristics for the drug under development. 

These tools can also predict the probability of success for different drug candidates before they undergo costly clinical trials.

Remote patient monitoring

Remember the last time you worried about a high-risk patient over the weekend? Or received an urgent call about a condition that could have been caught earlier?

With AI-powered monitoring tools, clinicians can continuously and proactively monitor patients’ health remotely and offer early intervention if and when necessary. 

This virtual setup simplifies chronic disease management and improves patient outcomes by preventing the need to visit health care facilities. 

These AI systems collect vital health data through wearable devices, smartphones, and home monitoring tools. They can also detect potential concerns or anomalies for immediate clinical support.

Advanced AI algorithms can also identify subtle changes in patient behavior, such as sleep patterns or activity levels. They flag any indication of deteriorating health conditions. 

Clinicians will receive alerts whenever these AI algorithms detect significant deviations from a patient’s baseline data. This way, doctors can prioritize urgent cases instead of getting overwhelmed with too many notifications. 

Impact of AI on remote patient monitoring 

  • Offers proactive and continued health tracking for remote patients 
  • Collects and analyzes patient data to flag any anomalies that need attention
  • Improves clinical workflows by using smart alerts to notify doctors about urgent cases

Real-life case study: Cardea SOLO

Cardio SOLO is a wearable ECG system. It includes a single-use sensor to monitor a patient’s heart rhythms. 

The device uses an AI-powered analysis tool to process all the ECG data in real time and generate a report to identify cardiac arrhythmias.

While traditional remote monitoring systems require patients to manually send data for analysis, this AI-powered device automatically processes ECG data. It gives doctors insights to quickly diagnose a patient’s symptoms.

Using AI to balance patient care with personal life

In the future, you won’t have to choose between patient care and personal well-being. You can use AI to deliver exceptional care while preserving time for your life beyond the clinic.

AI for doctors promises significant improvements in their day-to-day work—from reducing administrative burdens to enhancing diagnostic accuracy. 

As demonstrated by the success of UpToDate, ClosedLoop, and other case studies in our article, AI technology will empower doctors to improve patient outcomes. It’ll also enable clinicians to focus on their lives beyond the clinic and prioritize personal plans.

Freed is the most clinician-focused company in the world. Try our AI scribe for free.

FAQs

Frequently asked questions from clinicians and medical practitioners.

Can AI write clinical notes?

Q. How do AI medical Scribes work?

What are the risks of ambient intelligence?

Author Image
Published in
 
AI in Healthcare
  • 
7
 Min Read
  • 
January 17, 2025
Reviewed by
 
Lauren Funaro

Free yourself for better things.