Advances in artificial intelligence (AI) technologies are changing the medical and healthcare industries. These developments can potentially eliminate problems like misdiagnosis and the treatment of symptoms rather than the underlying disease.
AI technologies support high-performance and data-driven healthcare. The advantages of AI-powered solutions will continue to change how medical researchers and doctors approach clinical problem-solving.
AI in Healthcare
Recent trends in AI have reshaped how medical professionals diagnose, monitor, and treat patients. AI-powered technologies helped improve healthcare research and outcomes by generating accurate diagnoses and enabling personalized patient treatments.
The ability of AI tools to analyze massive amounts of clinical data quickly helps doctors identify disease markers that they might overlook otherwise. Moreover, the potential applications of AI are far-reaching and broad, from scanning medical imagery for diagnoses to predicting outcomes by consulting electronic health records (EHR).
Most healthcare providers today use AI technologies for various diagnosis, treatment, and administrative applications.
AI Technologies in the Medical Field
Medical institutions worldwide invest a significant amount of their budget in AI technologies. Take a closer look at the different types of AI technologies, and the benefits doctors and patients can get from their use.
Machine learning
Machine learning is one of the most common examples of implementing AI into healthcare. ML is a broad technique at the core of various healthcare and AI technology approaches.
ML tech transformed the healthcare system by allowing medical institutions to use AI in medical diagnosis and treatment. Its algorithms support the following tasks:
- Process large amounts of clinical data
- Make logical predictions about medical outcomes with increased accuracy
- Study medical imagery and patient records
- Support the development of new therapies
- Identify patterns
One widespread use of traditional ML is precision medicine, an innovative approach considering individual differences in patients’ environments, genes, and lifestyles.
This strategy allows doctors to predict what treatment procedures are effective with patients based on their genetic makeup and the treatment framework.
Natural language processing (NLP)
Natural language processing (NLP) is a type of AI technology that enables computer systems to understand and use human language. It has reshaped various fields, including the healthcare industry, where medical professionals use it in a wide range of health data applications, including the following:
- Improving patient care through more accurate diagnosis
- Offering more personalized medical services
- Streamlining clinical processes
For example, medical administrators can use NLP to keep accurate medical records so doctors can provide precise diagnoses. Additionally, doctors can use it to identify potential treatments and medications for a patient or predict potential risks based on the patient’s history.
NLP is an AI technology that provides clinicians with practical tools for managing complex data, which would be challenging to take on manually.
Rule-based expert systems
Expert systems on variations of “if-then” rules were a prevalent AI technology in healthcare in the 80s.
These systems heavily rely on human engineers to develop an extensive set of rules in a specific knowledge area. They work well up to a point and can be straightforward to follow and process.
However, as engineers introduce more complex rules, conflicts within the database arise and undermine system functionality. Moreover, changing the rules can be tedious and laborious if there is a shift in the knowledge area. Because of these disadvantages, most medical institutions are slowly replacing these systems with ML technologies.
How AI Can Transform the Medical Field
AI provides various advantages over traditional clinical decision-making analytics and techniques. Today, doctors can use learning algorithms, which can become more accurate as they receive more training data.
The learning functionality of AI systems can give doctors and medical researchers unprecedented insights into care processes, diagnostics, treatment variability, and patient outcomes.
Developing new radiology systems
Radiological images from magnetic resonance imaging (MRI) machines, computerized tomography (CT) scanners, and X-rays provide non-invasive visibility into the body’s inner workings. Unfortunately, several diagnostic processes still rely on physical tissue samples from biopsies, which poses various risks and complications.
However, AI has the potential to support the development of the next generation of radiology technologies. These AI-powered radiology tools can provide highly accurate and detailed imagery and, in some cases, eliminate the need for tissue samples.
For example, AI-powered technology, such as the PACS radiology imaging system developed by Novarad, can provide highly accurate and detailed imagery and give radiologists more information on which to make informed decisions. This helps lower false negatives in cancer detection and enables radiologists to work more efficiently. One recent example is DAMO RADAR, Alibaba’s open-source model that reads abdominal CT scans for 146 conditions and, in a 2026 study, cut radiologists’ reading time by more than 30 percent.
Experts aim to bring together the diagnostic imaging team with the interventional radiologist, surgeon, and pathologist. Unfortunately, achieving this level of collaboration and aligning goals remains a big challenge for medical groups.
Achieving this feat will allow clinicians to better understand how tumors act and behave as a whole. This process can be more effective than basing treatment therapies on the properties of a small part of a tumor from a biopsy. Moreover, researchers can better define the aggressiveness of specific cancers and develop more effective treatments.
Providing healthcare to patients in underserved regions
There is a lack of expert healthcare providers in some regions of the globe. When the pandemic hit, vulnerable country groups, comprising 91 nations, struggled to respond.
However, AI technologies can help address the gaps involving the severe deficit of qualified healthcare professionals in least-developed countries. Today, AI tools can take over some of the simple diagnostic duties typically tasked to humans.
For instance, AI imaging tools can filter chest X-rays for potential signs of tuberculosis, often demonstrating a level of accuracy comparable to humans. Health agencies can deploy this capability through an app or software available to health experts in low-resource areas, reducing the number of required diagnostic experts on site.
Improving electronic health record systems
EHRs have played a crucial role in the medical industry’s journey toward digitalization. However, transitioning from traditional filing systems to EHRs has introduced vital issues associated with endless documentation, cognitive overload, and user burnout.
To address these problems, EHR developers implement AI in their tools to develop more intuitive interfaces and automate specific routine processes. This way, tedious administrative procedures do not have to consume so much of a person’s time. Users can focus on other tasks like web management, clinical assistance, and other crucial organizational tasks.
Most administrative workers in medical institutions spend much of their time on three tasks: order entry, clinical documentation, and sorting through the in-basket. Using voice recognition and dictation tools can help streamline processes. Video recording clinical encounters can also make it easier for AI tools to index data for convenient information retrieval in the future.
Moreover, AI tools also help workers process routine requests from the inbox, like result notifications and medication refills. The technologies may also help prioritize tasks requiring the user’s attention, making it easier for them to complete.
Elevating the accuracy of pathology image analytics
Pathologists provide one of the most crucial sources of diagnostic data for doctors across the spectrum of patient care. Pathology results impact healthcare professionals’ medical decisions and the treatment strategy they offer their patients.
The more accurate data pathologists obtain, the closer doctors can get to the proper diagnosis. This is where AI technology comes in. Analytics that can provide accurate data down to pixel levels can help healthcare providers identify nuances that may be invisible to the human eye.
AI tools can also increase productivity and efficiency by immediately identifying features of interest in medical imageries before a human researcher reviews the data. They can filter through slides and guide pathologists in the right direction to assess what is essential and what is not. This advantage boosts the pathologist’s efficiency and elevates the value of the time they spend on each case.
Improve Healthcare Through AI Technologies
While it is true that AI technologies can help doctors improve patient care dramatically, it is still crucial to understand their limitations. Knowing the limits of algorithms and gaining a deep understanding of clinical data can be the keys to creating accurate algorithms that are valuable to doctors and patients alike.
Medical companies might need to share the secrets of their algorithm’s functionality so that a wider expert audience can point out sources of error and vet the methods.
Today, researchers still seem far from discovering the algorithms that can independently operate in clinics, primarily because of the lack of a pathway for clinical approval. However, it can be possible for the public to accept the replacement of doctors with algorithms if researchers manage to do the following:
- Define the specific qualities required for an algorithm to be deemed sufficiently accurate
- Address the potential gaps in the algorithm’s decision-making
- Be transparent about where an algorithm thrives and fails
Although these unique challenges can be difficult to overcome, the effort can be worthwhile in elevating the efficiency and accuracy of medical practices in treating various diseases.
