7 Most Powerful Ways Doctors Can Use AI To Increase Clinical Efficiency

About a year and a half ago, a reader of the blog emailed me asking if I used ChatGPT in my clinical practice at all. At the time I responded saying I didn't even know what ChatGPT was. Fast forward to today and AI sure looks like a revolutionary tool in all walks of life, especially for doctors and in clinical medicine.

Also, I just had a reader email me last week asking for tips on the best ways to implement AI for doctors. To be honest, I still haven't used it a ton up to this point…other than just messing around and having some fun to see what it says is the best financial advice for doctors. I also have worked with Doximity's AI tools a bit (more on that later).

Anyway, I figured one of the best ways to learn about the ways that AI can help doctors clinically was to ask AI itself. So, I asked ChatGPT the following, “Please write a 1000 word blog post on the top 7 uses for AI in clinical medicine for doctors to increase efficiency.” And yes, I know this breaks my “No AI generated posts” rule a bit. But I think this can fit in as an exception.

So, below is the response from ChatGPT along with my own thoughts in italics.

Enjoy!

AI in clinical medicine

Artificial Intelligence (AI) is revolutionizing nearly every industry, and healthcare is no exception. In clinical medicine, AI is reshaping diagnostics, treatment planning, patient monitoring, and even the way physicians interact with patients. As the field evolves, clinicians are discovering that AI is not here to replace doctors (This is exactly what AI would say to throw us off the scent!…Just kidding…Kind of…), but to enhance their decision-making, reduce human error, and ultimately improve patient outcomes.

Top 7 uses for AI in clinical medicine that are making the biggest impact for doctors today

1. Medical Imaging and Diagnostics

One of the most transformative uses of AI in clinical medicine is in the field of medical imaging. AI algorithms, particularly deep learning models, are being trained to analyze imaging data—such as X-rays, MRIs, CT scans, and ultrasounds—with exceptional accuracy.

These AI tools can:

Detect tumors, fractures, and lesions with accuracy comparable to, or even exceeding, that of radiologists.
Identify patterns in imaging that may be too subtle for the human eye.
Provide faster results, which is critical in emergency settings.

AI is already being used to detect diseases such as lung cancer, breast cancer, diabetic retinopathy, and stroke with remarkable precision. Tools like Google’s DeepMind and Zebra Medical Vision are leading the way in this space.

This seems to be the place that I hear more clinicians talk about the use of AI. And it makes sense. AI never fatigues etc. I'd love to hear from actual radiologists on this topic so if you are reading this, please comment below. I imagine that AI could be used to sift through the innumerable imaging studies done routinely and pick out one's that need a closer look from a radiologist. Will be interesting to see where this goes…

2. Clinical Decision Support Systems (CDSS)

AI-powered Clinical Decision Support Systems (CDSS) assist healthcare providers by offering evidence-based recommendations at the point of care. These systems analyze large volumes of patient data—including lab results, medical histories, and clinical guidelines—to suggest possible diagnoses or treatments.

Benefits include:

Reducing diagnostic errors and unnecessary testing.
Supporting physicians in managing complex conditions.
Ensuring adherence to best practices and clinical protocols.

For instance, IBM Watson for Health has been used in oncology to recommend treatment options based on a patient’s genetic makeup and the latest medical research.

3. Predictive Analytics for Patient Risk Stratification

AI excels in predictive analytics, making it possible to foresee complications before they occur. Hospitals are leveraging machine learning models to predict:

Which patients are at risk of deteriorating.
The likelihood of hospital readmission.
Patients prone to sepsis or acute kidney injury.

For example, the early detection of sepsis using AI can help clinicians intervene hours earlier than traditional methods, potentially saving lives. AI-driven risk stratification tools also help allocate resources more effectively by identifying high-risk patients who may require intensive care.

This is something that I've always loved in clinical medicine. I even built a risk prediction model for patients undergoing nipple-sparing mastectomy for breast cancer while I was in residency here. However, AI can certainly assist in this aspect more efficiently while using publicly available data and potentially even institution specific data.

4. Natural Language Processing (NLP) in Medical Records

Electronic Health Records (EHRs) contain vast amounts of unstructured data, such as physician notes, discharge summaries, and pathology reports. Natural Language Processing (NLP) allows AI to extract meaningful information from this data.

Uses of NLP in clinical settings include:

Automating clinical documentation.
Identifying critical information buried in notes.
Flagging abnormal findings for review.

This reduces administrative burden on healthcare providers and improves the efficiency and accuracy of charting.

I honestly think this is potentially the biggest area for increasing efficiency for doctors. Charting and administrative duties tasks continue to build for physicians. And AI can help. This is where Doximity has some amazing tools like Doximity GPT and Scribe. These resources allow doctors to use their phone as recorder to record clinical interactions and then form them immediately into customizable notes to be entered into EMR. Truly amazing! The only reason I haven't used this more extensively is because my APPs handle documentation in our clinic. Although I have been encouraging them to use it!

5. Virtual Health Assistants and Chatbots

AI-powered virtual assistants and chatbots are increasingly being integrated into clinical workflows and patient engagement platforms. These tools provide 24/7 support to both patients and providers by handling tasks such as:

Scheduling appointments.
Providing medication reminders.
Answering frequently asked health questions.
Triage and symptom checking.

For example, AI chatbots like Buoy Health or Ada Health can assess symptoms and recommend whether a patient should seek emergency care, visit a doctor, or manage at home—thus reducing unnecessary ER visits.

I think this is huge. Especially for those in private practice. It creates a much more cost efficient and time efficient way to manage your clinical schedule. The biggest concern is obviously steering the patient the wrong way when they contact the office in terms of symptoms. There would need to be some way to alert doctors to the messages. But I see massive benefits.

6. Personalized Treatment and Precision Medicine

AI is enabling a shift toward precision medicine, where treatments are tailored to individual genetic profiles, lifestyles, and environmental factors. By analyzing genomic data, AI can:

Identify genetic mutations linked to disease.
Predict patient response to specific drugs.
Guide targeted therapies, especially in oncology.

This approach is being used to personalize cancer treatment plans, helping clinicians choose the most effective therapy while minimizing side effects. AI also supports pharmacogenomics—analyzing how genetic factors affect drug metabolism—to avoid adverse drug reactions.

I'll be honest. ChatGPT kind of lost me with this one. I suppose this is maybe a futuristic possibility but I'm not sure practically how AI would have access to patient genomics and that kind of thing. Makes me a bit nervous.

7. Operational Efficiency and Workflow Automation

Beyond direct patient care, AI is enhancing the operational efficiency of healthcare institutions. From administrative tasks to resource management, AI is streamlining workflows and reducing overhead.

Some practical applications include:

Automating billing and coding.
Managing staff scheduling based on patient volume forecasts.
Optimizing operating room schedules.
Monitoring inventory and supply chains.

Hospitals using AI for operational tasks report reduced wait times, improved patient flow, and lower costs—all contributing to better patient experiences and more sustainable healthcare systems.

Probably the largest driver of increasing healthcare costs is administrative in nature. Especially as more and more doctors go to an employed model…including myself. One way to manage these costs is becoming more efficient and I think AI has huge applications in this.

Further, billing is a huge bugaboo for me. I know that my billers miss things that could optimize reimbursement all the time. And I get stonewalled trying to learn more. Regardless, I still code all of my own cases to offer what I think we should bill. I used to use a coding software but now I use Google AI which has been more efficient and very accurate.

Final thoughts on AI for doctors to improve clinical efficiency

AI is not a panacea, but its integration into clinical medicine is undeniably transforming healthcare delivery. From imaging and diagnostics to personalized treatments and administrative efficiency, AI is proving to be a powerful partner to clinicians—not a competitor.

As these technologies continue to mature, ethical considerations such as data privacy, algorithmic bias, and transparency must remain at the forefront. Ensuring that AI is explainable and trusted by both providers and patients will be key to its long-term success.

The future of clinical medicine is not just digital—it’s intelligent. And with the responsible use of AI, we’re entering a new era where care is not only faster and more precise, but also more human-centered.

It's funny to me that ChatGPT seems to go out of its way to make sure we don't feel threatened that AI is coming to take over our work. However I do agree with the sentiment. There is still along way to go with AI. And I don't really know any patient who feels comfortable being managed solely through a bot or anyone who would feel like that soon.

What I do see though is a continued pandemic of doctors experiencing burnout like I have in the past. And a lot of that can have to do with increasing demands on our time and efforts. And AI can absolutely help there. Hopefully this post gave you some ideas, even if you just start playing with them and see what works!

Here are additional resources to help you manage your time and efficiency both clinically and personally:

What do you think? How can AI be used by doctors to increase clinical efficiency? How do you use AI? Do you think it will take our jobs? Let me know in the comments below!