Company leaders across various industries are incorporating artificial intelligence (AI) into their daily workflows. Like many technological advancements adopted by organizations within the past 20 years, AI is not a fleeting technology in the workplace. It has arrived, and it’s here to stay.

Healthcare entities are actively exploring the potential of AI-powered software for automation purposes and doctors are beginning to recognize the profound benefits of using it to diagnose patients with diseases and conditions. The collaboration of physicians and AI could significantly augment medical diagnostics and positively influence a patient’s treatment outcome.

As AI continues to permeate the medical landscape, software solutions companies are developing cutting-edge AI-powered imaging and diagnostics so radiologists and their teams can conduct effective patient assessments.

AI diagnostics use innovative algorithms to identify diseases by analyzing patients’ medical data. AI analyzes traditional diagnostic screenings such as X-rays, CT scans, DXA scans, ultrasounds, biopsy procedures, blood tests, vitals, and more. AI diagnostics aim to diagnose a patient with a prevalent or underlying condition accurately.

Leveraging AI and machine learning tools can enhance a facility’s productivity and precision. The AI’s advanced findings and analytical reports support physicians in the decision-making process and predict possible outcomes. It can also alleviate workloads and increase efficiency, but more on that in a moment.

Healthcare Problems That Need Solutions

The COVID-19 pandemic altered the healthcare workforce. Hospitals and other medical facilities around the U.S. experienced high volumes of resignations, resulting in historic staff shortages.

Around 100,000 registered nurses left the medical field from 2021 to 2022, according to a study by the National Council of State Boards of Nursing (NCSBN). Over 610,000 are planning to leave the workforce by 2027 due to stress and fatigue. The Association of American Medical Colleges reports that the U.S. is expected to have a physician shortfall of up to 86,000 by 2036.

Doctors, nurses, and specialized therapists are often assigned more patients than they’re accustomed to handling. These shortages can lead to patient negligence, putting their health at risk.

Misdiagnoses can also jeopardize a patient’s health. If a physician fails to diagnose a patient properly, if at all, then the patient will undergo the wrong treatment. These human errors could result in wasting several weeks, months, or years of inadequate healthcare resources. It could also negatively affect hospitals that are trying to lower readmission rates. Patients who receive the wrong treatment are more likely to return to the emergency room.

Benefits of Using AI Diagnostics

Automation and faster decision making—AI has proven to provide faster screening results compared to traditional methods. Analyzing images and interpreting data is time-consuming for physicians and their staff. It’s an absolutely vital diagnostic step but it can create a bottleneck and stifle patient progress. If patients aren’t progressing due to untimely deliverables, that reflects poorly on hospital staff.

AI can provide around-the-clock comprehensive assessments of patients’ data. One of AI’s advantages that humans will fail to compete with is that it can continuously work nonstop. It is never tired, overwhelmed, or overworked.

AI is capable of evaluating incredibly large datasets in a matter of seconds, which saves physicians time and energy. A simulated radiology study¹ on chest X-rays revealed that AI reduced the time it took to interpret data and deliver results from about 11 days to just under three days. This enables healthcare workers to focus more on amplifying patient care while maintaining an efficient workflow.

Early detection—The optimal time to detect any disease or condition is at its earliest stage of development. Early detection can potentially increase positive outcomes due to less aggressive and more practical treatments. Too often, people are diagnosed with life-threatening ailments after the disease has taken a damaging effect, ruling out specific treatments.

AI diagnostics can even detect underlying changes in someone’s health. By examining CT scans, for example, AI can find traces of a growing brain tumor that might not be visible to the human eye. A physician can determine the next evaluation steps before the cancer evolves and limits a patient’s options.

Improved accuracy—Imaging and other diagnostic tests are the bulk of healthcare data. As these resources advance with the help of AI, hospitals and doctor’s offices will continue to generate more tests at faster rates. Accumulating an abundant amount of data could result in minute details going unnoticed, which poses several risks when diagnosing a patient. Without AI’s ability to analyze huge amounts of data, these risks could threaten their chances of recovery or affect the quality of maintenance of their condition.

Predictive analytics—One of the most attractive elements of AI is forecasting outcomes and courses of action. This applies to any industry’s use of AI, so why should the medical field be any different? Healthcare software developers have always tried to improve methods of predicting health outcomes, and now, AI is a promising solution.

Data from electronic health records, wearable devices, imaging results, and lifestyle factors are filtered through AI to detect harmful patterns that could lead to diseases. With this information, physicians and patients can monitor early signs of health issues and create preventative plans to decrease the likelihood of developing life-altering conditions.

Rare disease detection—AI has the ability to discover signs of a rare disease. According to a case study² published by Rare Disease UK, patients with rare diseases experienced an average of three misdiagnoses, consulted with five doctors, and waited four years before they received a final diagnosis. Some patients remained undiagnosed.

Because rare diseases aren’t prevalent in most individuals, physicians might not consider them when evaluating a patient. AI can recognize rare diseases by analyzing data from thousands of individuals who each have similar symptoms of that rare disease. It can pinpoint the root of the health problems before most doctors consider that the patient has an uncommon disease.

Skepticism and Caveats

Some healthcare professionals are skeptical about relying on computer algorithms to determine a patient’s medical care. The fear of doctors being “replaced” by robots is a recurring concern.

The good news is that AI is designed for augmentation rather than replacement. It helps systems and users gain more autonomy. AI is like a superpower to utilize at one’s discretion. If anything, AI is replacing outdated and less advanced technology.

It’s also important to keep in mind that many people prefer consulting with humans rather than AI. How many times have you tried to contact customer support only to find that you’re speaking to a bot? You practically have to yell “speak to a representative” until you’re connected to a human. People want to interact with people, but that doesn’t mean there isn’t room for AI intervention. AI can lead physicians to quicker conclusions but it can’t impersonate a doctor.

Another notable concern with using AI in diagnostics is biased results. For AI software to perform its tasks, someone must train it to do exactly what it was intended to do. Humans have inherent biases; therefore, they sometimes create AI that implements those biases when generating results. For those who wish to use unbiased AI responsibly rather than trusting it blindly, it’s essential to include ethical principles when developing AI diagnostics to personalize patient care.

Explainable AI (XAI) allows organizations to fully understand how their AI calculates decisions. Incorporating XAI into the backend of a company’s AI development has become a standard practice. According to IBM, XAI ensures that each choice made during the machine learning process is traced and explained.

Conclusion

The healthcare industry is using AI-powered systems to improve the quality of diagnostics. AI solves some of the most prominent concerns that facilities have, like optimizing time and resources with a finite staff. Implementing AI diagnostic methods has proven to be more efficient, accurate, and resourceful. Physicians can now diagnose patients at faster rates so they can begin the treatment process as soon as possible. They can also make decisions and instill preventative measures based on AI’s predictive analysis. All of these efforts ensure patients aren’t misdiagnosed or undiagnosed.

Even though there is some skepticism and caveats about applying this technology to evaluate sensitive data, developers will continue to enhance and reinvent AI diagnostics. Healthcare systems and professionals are inclined to adopt these methods not only for the sake of productivity but also for the benefit of every patient under their care. 

References

1. bit.ly/mposoftware07241
2. bit.ly/mposoftware07242

Pravin Vazirani, assistant vice president of Growth at Chetu, a global award-winning software solutions and support services provider, has extensive experience overseeing medical device projects in a diverse portfolio that included all sectors of healthcare—general, remote, and specialty. He brings more than two decades of progressive experience in the IT industry, where he leverages his expertise to deliver cutting-edge custom solutions, including an innovative approach to Digital Transformation, Robotic Process Automation (RPA), Artificial Intelligence/Machine Learning (AI/ML), Blockchain, and DevOps.