June 25, 2024

How AI in the Medical Field Is Enhancing Patient Outcomes and Saving Millions of Lives

Millions of lives are lost annually due to late disease detection and medical errors. AI in the medical field transforms healthcare, offering technologies that can enhance patient outcomes and save lives.

5 min read

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  • AI in the medical field is predicted to grow by 37% YOY over the next six years.

  • AI diagnosis tools can allow doctors to identify diseases earlier, improving survival rates for diseases like lung cancer from 10% to 70%.

  • AI-enhanced surgery tools can improve patient outcomes by 16.3%.

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From AI to FinOps, our team's collective brainpower fuels this blog.

Across the globe, nearly 3 million deaths occur each year due to diagnostic errors, unsafe surgical procedures, and poor decision-making. 

With millions of lives on the line, practitioners in the medical field are constantly searching for ways to improve patient care and enhance positive patient outcomes. 

In this life-or-death industry, artificial intelligence represents a new frontier that can enhance patient outcomes, improve early disease detection, and decrease surgical errors.

The value of AI in the medical field is predicted to grow at an annual rate of 37% from 2022 to 2030, adding over $170 billion USD to the sector. This rapidly growing area of medical technology could mark a shift in patient outcomes.

Source. AI in the healthcare market size worldwide from 2021 to 2030 in billion US dollars.

AI in the Medical Field: How Artificial Intelligence Is Enhancing Patient Outcomes in All Medical Disciplines

The medical industry remains at the forefront of technical innovation. Technological advancements in medicine can have a direct real-world impact, enhancing patient outcomes and saving lives.

Artificial intelligence is spearheading this innovation, providing doctors with methods of streamlining medical services and enhancing patient care. According to a study by IBM, AI in the medical field is valued to reach a total market cap of around $187 billion in 2023. Much of this value comes from the problems that AI can solve in this industry.

Researchers are pinpointing problems or medical practices and using artificial intelligence to overcome them. Here are three examples of problems that AI is solving in the medical field.

  • Late Disease Detection – According to the National Center for Biotechnology Information, diagnostic delays significantly contribute to morbidity. Late diagnosis gives doctors less time to implement treatment plans, severely decreasing the likelihood of a successful outcome. In cancer diagnosis, even a four-week delay can create a significant increase in mortality risk for patients. Radiologists are using artificial intelligence tools to detect and identify disease states from scans more efficiently, leading to earlier diagnosis.
  • Reducing Human Error in Surgery  – John Hopkins University research suggests that at least 4,000 mistakes occur in surgery each year, leading to worse patient outcomes. These mistakes can range from surgery technique inaccuracies to even operating on the wrong body part. Artificial intelligence surgery robots are becoming increasingly common in US hospitals. AI surgery robots exert higher accuracy than human surgeons, boosting positive outcomes and eliminating the possibility of human errors.
  • Errors in Clinical Decision-Making – Trauma and first-responder doctors have to constantly survey patients, make critical clinical decisions, and implement a treatment plan of action within moments of meeting a patient. Under immense pressure and working long hours, many primary care physicians don’t have enough time to spend on each patient, leading to mistakes or misdiagnosis. Artificial intelligence clinical diagnosis tools help streamline clinical decision-making, enhance productivity, and ensure doctors can maximize precision and accuracy. 

AI in the medical field is creating valuable opportunities to enhance patient care, improve surgery outcomes, and reduce the pressure placed on our front-line workers. Let’s explore each of these AI solutions in more detail.

Mass General Uses AI in the Medical Field to Accelerate Disease Detection

"Humans couldn’t quite see where the cancer was, but the model could still have some predictive power as to which lung would eventually develop cancer.”  – Jeremy Wohlwend

Researchers from Mass General and MIT have collaborated to produce an AI model that is highly accurate in detecting lung cancer on patient scans. Named Sybil, this AI model can analyze lung scans without the assistance of a radiologist and then predict the patient’s risk of developing lung cancer over the next six years.

Considering lung cancer is the deadliest cancer in the world, killing over 1.7 million people in 2022, the ability of Sybil to diagnose this form of cancer early means that doctors may have years of extra time to develop a treatment plan for patients.

If you detect lung cancer early, the five-year survival rate is nearly 70%, while detecting at an advanced stage only offers around a 10% survival rate. By using the AI model Sybil, doctors could radically increase the survival rate of lung cancer.

Source. Two lung scans across two years. The left red area is from Sybil's detection, and the right is two years later via the doctor's diagnosis. 

The Chief Data Science Officer at Mass General Brigham, Dr. Keith Dreyer, states that around 30% of all radiologists are now using AI diagnosis tools in some capacity. Dreyer comments on the future of AI in diagnostics: "Over the next 10-15 years, we'll see more models become widely available and adopted, with the average radiologists practicing with 20-40 algorithms each depending on their subspecialties. These models will be better able to detect and identify rapidly declining disease states, quantify lesions on previous and current scans, and predict morbidity and mortality from a series of images.”

Doctors who leverage AI in diagnostics will enhance disease detection, allowing them to implement care strategies as early as possible and boost the rate of successful treatment plans.

How Da Vinci Systems Reduces Human Surgery Errors with AI-Assisted Surgical Robots

Even one small mistake in surgery can lead to poor outcomes, reducing a patient’s quality of life or leading to more perilous consequences. Despite layers of medical protocol and years of training, mistakes still happen occasionally, with around 4,000 surgery accidents occurring annually.

The use of robotics in surgery to enhance surgeon precision and improve patient outcomes has been a practice in the healthcare industry since the late 1980s. Modern advancements in artificial intelligence in the medical field have radically improved the utility and skill of these surgical robots.

The Da Vinci surgery robot system is one of the most advanced AI surgery systems. Surgeons can use the system to perform precise operations, with its ability to bend and rotate far beyond the human hand without any shaking, making it a powerful surgical tool.

According to a study, robot surgery systems like Da Vinci can increase surgery accuracy by around 16.3%, altogether avoiding human errors and helping to reduce the number of mistakes in surgery radically. Equally, their enhanced dexterity can reduce surgery times in 57.1% of cases, ensuring that patients can be under anesthesia for less time, decreasing the likelihood of complications.

Surgery robots are rapidly becoming vital technology in hospitals worldwide, and the rapid growth of this market reflects the rising demand.

Source: The surgical robots market from 2022 to 2030 in USD billion.

IBM and TidalHealth Mitigate Errors in Clinical Decision-Making 

Trauma response doctors and clinicians have become busier than ever before in recent years. With growing patient volumes, increasing documentation requirements, and challenging daily workflows, it’s difficult for doctors to focus directly on the patient. 

IBM has partnered with TidalHealth Penisula Regional, a level III trauma center, to reduce the manual time spent on each patient when looking for treatment plans or finding the correct drug recommendations. On average, doctors in TidalHealth would spend 3-4 minutes per clinical patient looking for information.

After IBM introduced IBM Micromedex, an artificial intelligence system that would streamline the diagnostic workflow and recommend decisions to doctors, this time dropped to under one minute per patient. The introductory period of using IBM Micromedex resulted in 92% of doctors approving of the clinical decision support tool, highlighting its ability to save them time and enhance the speed with which they can attend to patients. 

TidalHealth Penisula serves nearly 500,000 patients each year. This innovative introduction of an AI system supports enhancing the efficiency of patient care and streamlining hospital visits. 

Dr. Rachel Cordrey, PharmD, Supervisor of Inpatient Pharmacy Operations, comments that “Clinical decision support is a valuable tool for pharmacists and other clinicians. Having this kind of natural language solution embedded into the EHR so it can be accessed right at the point of care is much more efficient for end users.”

IBM’s AI solution enhances patient care and ensures that every new patient gets the very best from their doctors. 

AI In the Medical Field Is A Force For Better Patient Outcomes

Artificial intelligence can integrate into almost every area of the patient experience. From streamlining administrative hospital duties to enhancing surgery outcomes, AI offers an innovative way of improving patient care and creating a safer hospital experience.

Major hospitals and medical technology providers in the US and worldwide are turning to artificial intelligence to streamline care and create a more effective method of diagnosing, treating, and curing disease. 

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