Artificial intelligence is in virtually every aspect of our daily lives. In medicine, it’s helping small clinics and solo providers reclaim their time, provide excellent patient care, and run a successful practice.
AI-powered EHRs are easing the administrative burden, assisting with time-intensive documentation so doctors can focus on their patients.
AI tools are also revolutionizing patient care, helping clinicians make faster, better diagnosis and treatment decisions.
Ophthalmology has tremendous potential to benefit from AI. Because of its image-based and data-rich nature, the National Institutes of Health has suggested ophthalmology might be the specialty with the widest range of AI applications.
How Ophthalmologists are Using AI
In 2024, ophthalmologists are using artificial intelligence to run their practices, engage patients, and screen for diseases. With rapid advancements in technology, more uses are right around the corner.
Using AI to Run an Efficient Practice
An aging population is increasing patient volumes at the same time healthcare is experiencing a global staffing shortage. For a small to midsize ophthalmology practice, this means every minute saved by automation counts toward the bottom line and prevents staff burnout.
- AI Virtual Assistant technology can automate 85% of routine conversations between staff and patients. Patients get quick, convenient answers to their questions, increasing satisfaction. Meanwhile, staff have more time to devote to high-value tasks and interactions.
- AI-enhanced claim scrubbing cuts down on the time billing staff spend on each claim while improving accuracy and first-pass clean claims rates.
- Specialty-specific EHRs are tailored to collect and analyze exactly the right data from each patient encounter. Ophthalmologists can move smoothly from one patient to the next without taking time out for tedious reporting paperwork.
- AI-powered predictive analytics help practices build toward business success. These models analyze vast quantities of data to forecast revenue, optimize staff allocation, and improve cash flow.
Using AI to Engage Patients
Chatbots were only the first step in AI-powered patient engagement. Today, ophthalmologists are developing algorithms to make it easier for patients to follow doctor’s orders and play an active role in their own healthcare.
For example, eye surgeon Ken Y. Lin, M.D., Ph.D., at the University of California Irvine developed an AI-powered smartphone app to prevent dosing errors. The free app helps visually impaired patients tell the difference between their various eye drop bottles.
Another tool aims to make treatment check-ins less burdensome for patients. To know if a treatment is working, doctors often rely on an ophthalmological exam with refraction to measure best corrected visual acuity (BCVA). Scientists at Johns Hopkins are constructing an algorithm that can estimate BCVA based on fundus photographs.
Using AI to Screen for Eye Diseases
Artificial intelligence is helping to identify retinopathy in its early stages, allowing patients to seek sight-saving treatment sooner.
Using FDA-approved devices, primary care physicians can screen their diabetic patients in-office for diabetic retinopathy. This eliminates the need for patients to schedule separate screening exams with their PCP and their ophthalmologist.
In addition, studies have shown AI-powered tools are as reliable as a human expert in identifying features of retinopathy of prematurity.
The characteristics of a glaucoma diagnosis make it a prime candidate for AI identification. Algorithms have been trained to detect visual field loss and recognize glaucoma progression earlier than conventional screenings.
In addition, AI-based tools help ophthalmologists project trajectories of clinical scenarios, assisting them in choosing an appropriate treatment plan for each patient.
The Future of AI in Ophthalmology
In the near future, ophthalmologists can expect to see AI changing the way we detect cataracts, take 3D images, and train surgeons. Generative AI may eventually contribute to training algorithms to diagnose rare diseases.
- AI-based cataract detection tools like ResNet promise to identify referable cataracts, while a system called the CC-Cruiser is learning to identify the region, density, and degree of congenital cataract formation.
- AI-based 3D imaging will allow you to build 3D models of the eye without an MRI or CT scan. Tools being developed at the University of California Irvine aim to make in-office 3D modeling possible using eye imagery already available to ophthalmologists.
- AI-enhanced surgical training hopes to help surgeons improve their skills faster. An algorithm in development will analyze surgeons’ performance and provide immediate, tailored feedback.
- AI-generated fundus photographs may help develop the next generation of diagnostic tools. Accurate diagnostic algorithms require tremendous datasets. For some rare disorders, there are simply not enough images to train the model. AI-generated fundus photographs could fill the gaps in data, helping future algorithms to recognize rare diseases.
Incorporating AI Into Your Ophthalmology Practice
Artificial intelligence is here to stay. In the very near future, practices not using some degree of AI technology will struggle to keep up with their more efficient competitors.
Here are some ways to begin introducing AI in your practice:
Use AI to Validate Clinical Decisions
A small to midsize ophthalmology clinic may have only one or two ophthalmologists on staff. In a high-pressure diagnostic or treatment scenario, you have only your own judgment to rely on. AI tools can’t make medical decisions, but they can validate and support physician decision making.
A tool might identify an anomaly too small to be easily detectable by a human. When faced with a list of symptoms, the tool can search its dataset to propose potential diagnoses or validate your ideas.
After you’ve arrived at a diagnosis, AI can calculate the likely rate of disease progression. It can also run hypothetical treatment scenarios to help you arrive at a treatment with the best likelihood of success.
Use AI to Manage Your Practice
AI-enabled EHR and practice management software helps your practice operate at peak efficiency. From scheduling to marketing to billing, AI tools help your staff work faster, more accurately, and with less burnout.
AI Scribe technology documents your patient interactions in real time, filling in forms and records for you. Dr. Neel Vaidya of Chicago Cornea Consultants beta-tested Nextech’s AI Scribe and found it helped both him and his staff spend less time at the keyboard.
“It’s a huge time saver for my technicians to have discrete elements like the patient’s vision, intraocular pressure, and refraction all go into the chart correctly so they can save clicks,” he said. "If AI Scribe can pick up my conversation when I'm seeing a patient for an ulcer which is getting better, and generate a plan for me to taper their antibiotic drops and see them in two weeks based on that conversation, I will save a ton of time using this system.”
Besides helping your practice to operate more smoothly day to day, AI-powered practice management can position your clinic for future growth. Automated systems with smart analytics optimize marketing efforts, revenue cycle management, and retail integrations.
Use AI to Engage Your Patients
It may seem counterintuitive, but AI can help you build better human relationships with your patients.
An AI-powered chatbot on your website answers questions any time, day or night. Meta analysis of chat transcripts can give you insight into patterns in patient inquiries to make your business more accessible. For example, a high number of inquiries about weekend hours might lead you to explore expanding the clinic schedule.
Intelligent tools can also segment patients for marketing communications. Automated texts, phone calls, and emails can be triggered by specific patient behaviors. Patient responses are recorded in the EHR, giving you and your staff rich information about their preferences.
Challenges of AI in Ophthalmology
While AI has tremendous promise in ophthalmology, it’s not without its challenges. While developers strive to prevent bias, industry stakeholders debate standards and clinicians endeavor to apply lab-tested models in the real world.
- Artificial intelligence was once expected to remove human bias from clinical decisions. But when bias is present in the data used to train AI, the tool internalizes it. To achieve truly fair and accurate results, developers need to train their models on data representing the full diversity of the patient population.
- Healthcare experts have yet to reach a consensus on developing AI standards. Until standard thresholds have been set for diagnosis, referral, and triage, individual clinicians will be challenged to make those determinations for themselves.
- Getting results in the controlled environment of the lab is one thing. The challenge is developing validated AI models that can deliver reliable results based on the less-than-ideal images available in the standard clinic setting.
The Limits of AI in Healthcare
Artificial intelligence has been studied for decades, yet practical applications of the technology are still in their early stages. While the possibilities unlocked by AI are exciting, ophthalmologists who use it need to be aware of its limitations.
- “Garbage in, garbage out” is a term well known to computer scientists. It means low-quality inputs can’t yield high-quality results. If a model’s training data is incomplete, erroneous, or biased, those errors will be incorporated into its output.
- Artificial intelligence lacks human judgment. Not even the “smartest” AI can replace the ability of a trained physician to make a judgment call. AI is a great partner to confirm decision-making processes, or to brainstorm ideas that can be validated and verified by traditional means. But clinicians who rely on AI recommendations without validating them open themselves up to liability.
- An opaque decision-making process makes some clinicians uncomfortable. Programmers develop AI models and input the training dataset. However, not even their programmers have visibility into how the machines actually “learn” – how they interpret training data and come to conclusions. This lack of transparency is called “the black box dilemma.”
Ethical Considerations of AI in Ophthalmology
As artificial intelligence becomes commonplace in healthcare, ophthalmologists and other medical professionals must make its ethical application a priority.
The first ethical consideration is data privacy. When investing in AI-powered tools like an EHR or practice management software, take care to fully understand the data privacy protections offered by the vendor.
AI systems train on massive datasets. This data can include personal health information like medical history, diagnostic images, and treatment records. Unless it is properly anonymized, it may also include identifying information that can be used to match records with patients.
It’s critical that before patient data is collected, the patient gives informed consent to how their information may be used.
Another ethical consideration is algorithmic bias. Researchers developing new AI models diligently seek to detect, mitigate, and prevent prejudice in the training data. But it’s possible for unconscious bias to escape detection.
Ophthalmologists using AI tools to aid in diagnosis and treatment plans should be aware of this possibility. Watch for inconsistencies in how the tool approaches diagnosis and treatments in different patients.
Finally, beware of relying too heavily on AI assistance. No machine can replace the decision-making ability of a trained ophthalmologist.
Artificial intelligence can be a valuable supplement to your practice – streamlining productivity, enhancing diagnostic accuracy, and validating hypotheses. Still, clinicians should be trained in how to interpret and critically evaluate its recommendations, rather than blindly following all the tool’s suggestions.
Use Artificial Intelligence to Improve Human Connection
The purpose of artificial intelligence in healthcare is not to come between providers and patients. It’s to remove obstacles and make those connections easier.
An AI-powered EHR and practice management system allows providers to spend less time behind a screen and more time in front of their patients. The software alleviates the burden of manual inputs and documentation. Providers and their staff can leave data entry to the machines, focusing their energy on making decisions and helping patients.
Nextech’s ophthalmology-specific EHR and practice management software are designed to help ophthalmologists deliver outstanding care, grow their practice, and still leave work on time.
Schedule a demo to see our ophthalmology tools in action.
Frequently Asked Questions
What is AI?
Artificial intelligence, or AI, is computer software that learns from experience. It adjusts its behavior and responses based on learning and new inputs.
The three main types of AI are simple automated detectors, machine learning, and deep learning.
- Simple automated detectors do not learn or adapt based on experience. They are rules-based algorithms. They look for features they are programmed to identify – such as the contours of a lesion – and recommend a diagnosis or action.
- Machine learning algorithms are trained on a dataset. Unlike a simple automated detector, a machine learning algorithm is not bound to its training. Instead, it uses its training data to predict an outcome. It tracks whether the outcome was validated or invalidated and adds that experience to its data. This allows it to learn from its mistakes and expand on its successes.
- Deep learning is the most advanced artificial intelligence. These algorithms are based on artificial neural networks, similar to a natural brain. Between the input layer and output layer of a neural network are multiple analysis layers. Each layer processes and analyzes data at a higher level. Some deep learning networks have hundreds of analysis layers.
What artificial intelligence ophthalmological devices have been approved by the FDA?
The U.S. Food and Drug Administration has approved several AI-powered screening tools for diabetic retinopathy, including Luminetics-Core (formerly IDx-DR, the first FDA-approved autonomous AI device in medicine), EyeArt, and AEYE-DS.
How is AI helping the visually impaired?
AI-powered advancements in ophthalmology are helping doctors diagnose and treat eye diseases that lead to blindness.
In addition, there are a number of AI-powered tools to help people with visual impairments to navigate and understand their surroundings, including smart screen readers and wearable devices that give wearers audible information about items in their environment.
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