Using AI in Healthcare: An NP’s Perspective

Artificial Intelligence (AI) is a machine’s ability to complete cognitive functions that are associated with human minds. AI has been around since the 1950s, and was defined broadly as a machine’s ability to perform a task that would have previously required human intelligence. AI is associated with using computers and has become widely available today. Using Google voice assistant, Siri, or ChatGPT are everyday uses of AI. Self-driving cars are another form of AI.

AI is not the replacement of human intelligence or social interaction, but it has the ability to use its training to adapt and learn new skills for tasks that it was not explicitly programmed to perform. The emergence of AI in healthcare has been revolutionary. It has reshaped the way patients are diagnosed, treated, and monitored. This technology even improves healthcare research and outcomes by yielding more accurate diagnoses which leads to more personalized treatments. AI analyzes vast amounts of clinical documentation rapidly, which helps medical professionals identify disease markers and trends that may have otherwise been overlooked.

Applications of AI in Healthcare

The potential uses of AI in healthcare are broad and far-reaching. It can provide early disease detection from scanning radiological imaging to predicting outcomes from the electronic health records. By leveraging AI in hospitals and clinics, healthcare systems can become quicker, smarter, and more efficient in providing care to millions of patients. AI transforms how patients receive quality care while alleviating provider costs and improving health outcomes.

AI in healthcare is expected to redefine how healthcare data is processed, how diseases are diagnosed, and develop appropriate treatments. Healthcare professionals can use AI to make more informed decisions based on more accurate information. This can save them time, reduce overall costs, and improve medical records management.

Machine Learning

Machine learning (ML) is one of the most common examples of healthcare and AI working together. ML algorithms can swiftly process great amounts of clinical documentation, identify patterns, and conduct forecasts about medical outcomes with great accuracy. It helps medical professionals improve their treatments and reduce costs by analyzing patient records and medical imaging and discovering new therapies. Using ML in disease diagnosis and drug discovery, allows clinicians to accurately diagnose illnesses and customize treatments to a patient’s needs.

The most common use of ML is precision medicine. This allows providers to predict what treatment procedures will likely be successful for the patient based on their genetic and environmental makeup. Precision medicine is innovative and also considers a patient’s lifestyle in disease prevention.

According to Dr. Taha Kass-Hout, vice president of health AI and CMO at Amazon Web services, “97 percent of healthcare data goes unused because it’s unstructured”. Examples include x-rays and medical records that are attached to slides. ML facilitates the structuring and indexing of this information. The benefits of ML include the prevention of medical errors and assisting providers in detecting diseases earlier. Additionally, consistent analysis of medical data and increased access to care are advantages of ML use.

Natural Language Processing

Natural language processing (NLP) is a type of AI that enables computers to interpret and use human language. It is the ability of computers to understand text and spoken words in a similar way to humans. It is used in a wide range of health data applications. One example is optical character recognition. This is how a computer reads handwritten text and converts it into a digital format. This has been very useful in the transition from paper charting to electronic health records.

NLP helps healthcare professionals understand the meaning of clinical data and improves clinical documentation overall. It advances patient care through better diagnosis accuracy, streamlining clinical practices, and offering more personalized services. It can be applied to medical records to identify illnesses by extracting useful information from health data. NLP can also predict potential health risks to patients based on past health information.

NLP is beneficial in research studies as well. It helps physicians review thousands of medical charts per hour to narrow down patient profiles with the right inclusion criteria. This eliminates a lot of the tedious work researchers have to do.

Deep Learning

Deep learning (DL) is when data is analyzed and interpreted with the help of extended knowledge by computers. It is a method that teaches computers to process data in a way inspired by the human brain. DL can recognize complex patterns in pictures, text, and sounds, to produce accurate perceptions and predictions. DL drives many AI applications that improve automation and perform analytical tasks without human intervention. Examples include voice-enabled TV remotes, credit card fraud detection, and digital assistants.

Administrative Applications

AI is impacting many of the administrative aspects of healthcare. It automates mundane tasks like data entry, claims processing, and scheduling appointments. This frees up time for providers and supportive staff, which allows them to focus more on patient care. AI also provides a faster way to review medical imaging, lab results, and health records. As a result, human error is reduced.

ChatGPT could be used to draft insurance approvals, freeing the time of nurses and physicians. It can also aid healthcare professionals by summarizing scientific literature. This same feature could provide educational material for patients.

Challenges for AI in Healthcare

There may be several ethical and regulatory issues in AI in healthcare. Some examples include data privacy, patient safety and accuracy, and gaining clinician acceptance. AI systems collect large amounts of personal health information which could easily be misused if not handled correctly. There needs to be better surveillance systems in place to prevent hacking or breach events.

Other challenges are training algorithms to recognize patterns in medical data, integrating AI with existing electronic health records, and guaranteeing compliance with federal regulations. AI systems must be trained to understand relationships between diagnoses and treatments and provide precise recommendations tailored to each patient. Failure to do so will impact patient safety.

Integrating AI with other IT systems can cause additional complexity for clinicians as it requires understanding how technology works to guarantee seamless operation. Healthcare organizations should require their clinicians to obtain continuing education on the benefits of AI tools and models. This can allow providers to place more trust in AI technologies.

The Future of AI in Healthcare

AI has a lot in store for healthcare. Virtual assistants may become more commonly used in healthcare in the near future. They can be utilized to triage patients and verify symptoms. This can narrow down which patients have emergency needs versus those that a primary care provider can see. Conversational AI can be implemented to advise patients on whether they need to fast before an exam, what to wear, or how to prepare for an appointment.

Wearable devices should increase in popularity which will provide more health data. Wearables can provide data related to a patient’s genomics and phenotypes. AI can allow clinicians to develop targeted diagnostics and personalize care based on the data from wearables.

AI-assisted drug discovery is an opportunity for companies to find new drugs to treat diseases in a quicker and less complex process. Researchers can use AI to assess large amounts of patient outcome data to identify substances that are more likely to be effective treatments. AI can analyze vast amounts of data from clinical trials and patient records, to help researchers identify which patients might most likely respond to a specific treatment. It can help clinicians prioritize which compounds to test in the lab, speeding up the new medication’s development process. With a consolidated platform in drug discovery, the overall costs of drugs will likely decrease.

The combination of AI with robotic surgery can drastically impact healthcare. AI systems can absorb tons of information in mere seconds. Surgical robots with AI-based systems can be given thousands of surgeries in seconds. This can be utilized as a learning tool for surgeons at all stages of their careers. AI can educate surgeons on different methodologies while also centralizing access to surgeons in rural or undeveloped areas. It can provide surgeons with a new perspective, which can introduce new methodologies to existing surgical procedures.

AI can enhance robotic surgery by alleviating surgeons’ stress. It can highlight tools, monitor operations and send alerts, ensuring a more streamlined process. It can map out the best steps for the procedure based on individual patient’s needs, saving crucial operating time and relieving surgeons’ cognitive stress. In return, this leads to more favorable patient outcomes. 

AI has grown in its capabilities to impact healthcare and improve medical practices. Machine learning allows for clinical data to be processed expeditiously. Natural learning helps providers interpret this data. And deep learning can help detect diseases more quickly, and provide personalized treatment plans for patients. AI helps increase patient safety and reduces costs associated with healthcare delivery.

The future is bright for using artificial intelligence in healthcare. It can become an invaluable asset that could reshape how providers treat patients and deliver medical care. It can improve the quality of care for patients by reducing human error and preventing physician fatigue from performing routine clinical tasks.

Sophia Khawly, MSN

Sophia Khawly, MSN


Sophia Khawly is a traveling nurse practitioner from Miami, Florida. She has been a nurse for 14 years and has worked in nine different states. She likes to travel in her spare time and has visited over 40 countries.

Being a traveling nurse practitioner allows her to combine her love of learning, travel, and serving others. Learn more about Sophia at