How AI Minimizes Human Error in Diagnostics

Human error causing accidents and costing lives in the healthcare industry is the last thing we want. An incredible amount of mishaps have happened due to manual errors in diagnostics, and they are still happening. Take a look at the gruelling real-world numbers supporting this claim of ours:

• Hopkins Medicine Organization reports that an estimated 795,000 Americans die or are permanently disabled by diagnostic error each year, confirming the pressing nature of the public health problem.
• According to a study from the Armstrong Institute, an estimated 12 million Americans are affected each year, and perhaps one-third are harmed.
• Even the most esteemed entity, WHO stunningly proclaimed that around 1 in every 10 patients is harmed in health care, and more than 3 million deaths occur annually due to unsafe care. In low-to-middle-income countries, as many as 4 in 100 people die from unsafe care.

The Million-Dollar Question Remains: How’d We Stop the Rot?

When humans are out of sorts, they always have turned to science, technology, and engineering. Combining these innovation pillars has always delivered humankind’s most remarkable and life-changing events. It’s still early days to tell if we can completely remove such manual or human errors in healthcare diagnosis. One technology has comprehensively provided excellent and precise results in this landscape.

The Answer Goes by the Name of “Artificial Intelligence”

You must have heard it, right?

Artificial intelligence integration is becoming popular in virtually every field, and medical imaging is no exception. As with most conventional imaging, diagnostic accuracy is most important because it defines the efficacy of the diagnosis or therapy of the disease in the patient.

Misdiagnosis arises mainly in imaging studies such as MRIs, CT scans, and X-rays. Human Factors such as fatigue, multitasking, and subjective judgment can also contribute to a case of misdiagnosis or mismanagement.

AI and ML development services offer a promising solution, significantly improving diagnostic potential and causing few errors compared to traditional human diagnostics. Let us dive a bit deeper by sneaking into the types of human errors in medical diagnosis.

Types of Human Errors In Medical Diagnosis

Human errors in medical diagnosis cause extensive consequences, including misdiagnosis, incorrect treatment, or delayed medical intervention. Several are common in the diagnostic process.

• Diagnostic Errors

It refers to the failure to diagnose patients’ health problems promptly. They could occur at any stage of diagnosis, ranging from misinterpretation of test results to delayed diagnosis, leading to inappropriate or prolonged treatment.

• Categories of Medical Error

Except for a wrong diagnosis, there are several ways medical errors occur:

1. Misdiagnosis: The patient is diagnosed with the wrong kind of illness or disease; this might be caused by mistaken interpretations of symptoms or test results being misinterpreted and cognitive bias.
2. Delayed diagnosis: A proper diagnosis is made only after the patient has already become ill from the effects of the disease process, often because it was not diagnosed in time.
3. Overdiagnosis: It may be some reality where a disease would have never bothered a patient if he had not been diagnosed and treated. The patient gets undue treatment and anxiety.

• Misinterpretation of Test Results

Misinterpretation is the most common cause of medical error of tests’ diagnostic tests:

1. False positive: The test wrongly reports the presence of the condition, thereby overtreating this condition, which again leads to emotional stress and resource wastage.
2. False negatives: The existing condition goes undetected as the test wrongly delays treatment due to the worsening of the disease.

• Human Factors in the Diagnostic Process

Several human factors are involved in causing diagnostic errors:

1. Cognitive overload: Healthcare workers are generally exposed to a fast-paced environment characterized by significant pressure involving complex cases and the likelihood of making errors resulting from information or multi-tasking burdens.
2. Communication failures: This also occurs when health care providers fail to communicate with each other or patients, leading to an oversight of critical information that may lead to diagnostic errors.
3. Time constraints: More time to devote to each patient may force clinicians to make hurried decisions that ultimately lead to omitting other potential diagnostic avenues.

• Lack of Systematic Resources

The diagnostic errors are often caused not only by human mistakes but also by systemic problems in the healthcare system:

1. Adequate diagnostic resources: Lack of access to modern diagnostic equipment or sound laboratory tests may hinder proper diagnosis.
2. Documentation errors: Misinterpretation or omission in EHR can lead to wrong decisions if information is partially omitted or is wrongly written.

How Does AI Work in Diagnosis?

At its core, AI employs algorithms, data analysis, and machine learning models to replicate human cognitive processes and reasoning. In diagnostics, AI processes extensive datasets, including medical records, imaging, and lab results, to uncover patterns that may elude human detection.

AI models are trained on substantial quantities of historical patient data, enabling them to learn to identify recurring symptoms, image characteristics, or laboratory findings linked to specific diseases. AI systems continually refine their predictions as time progresses, assimilating new data and achieving ever-increasing accuracy.

AI Technologies Used In Medical Imaging

AI technologies play a significant role in processing complex images for pattern analysis and providing insights that, perhaps, the human eye misses.

• Machine Learning (ML): It is an algorithm that can learn from prior experiences to identify patterns and trends in data. ML can be trained using extended labeled datasets in medical imaging to detect abnormalities such as tumors, fractures, or infections. A system’s diagnosis improves with its accuracy based on the data being processed.
• Natural Language Processing (NLP): It reads and extracts the right information from unstructured medical texts such as patient notes, doctor reports, or even research articles.
• Computer vision: It automatically detects anomalies, such as details in an X-ray, MRI, or CT scan. It is valuable to radiologists and pathologists, providing more qualified diagnosis.
• Deep learning: It is the sub-type of machine learning specialized for detecting implicit patterns in medical images by using a multilayer neural network. Otherwise, no expert would catch their eye.

AI Performance vs. Human Performance in Diagnostics

Real-Time Uses of Artificial Intelligence in Diagnostics

Some of these applications have previously shown considerable potential in minimizing diagnostic inaccuracies in routine clinical practice.

1. IBM Watson Health

The process of making diagnostic decisions in oncology has implied the use of IBM Watson Health. Oncologists can then feed massive amounts of clinical literature and patient information to Watson for the recommended treatment. This is helpful as it decreases the chances of missing a particular treatment or incorrectly interpreting some info.

2. Google DeepMind AI to diagnose Eye Disease

Google’s DeepMind tool scans the eyes and detects more than fifty types of eye diseases like AMD and diabetic retinopathy. This AI tool diagnoses eye diseases and priority makes them; therefore, patients can reach for the required treatment in time.

3. PathAI

PathAI is a healthcare Artificial Intelligence developed to optimise pathology diagnosis. The system helps pathologists by providing diagnostic support from digital pathology images, focusing on cancerous cell identification.

4. Aidoc: AI in Radiology

Aidoc is an AI-powered technology that helps radiologists interpret radiographs and CT scans. It accurately detects brain haemorrhages, pulmonary embolisms, and spine fractures. The technology sends notifications to the radiologists concerning the critical findings to expedite the diagnostic process and support timely intervention.

These innovative applications show the revolutionary potential of healthcare software application development to improve diagnostic accuracy and patient outcomes.

Challenges While Adopting AI Implementation

Though AI has much promise in medical diagnostics, it comes with challenges and limitations. These will create failures and limit the application of AI-driven diagnostic tools.

• Quality and Quantity of Data: The performance of AI models varies depending on how well and how much data they have been trained on. If the data sets are adequate or biased, wrong predictions are highly probable, and models are more generalizable.
• Explainability and Transparency: AI models are little more than black boxes. Indeed, tracing their output on how they arrived at a given conclusion is tough. The lack of transparency creates problems concerning accountability and trust in AI-based diagnostics.
• Ethical Concerns: AI diagnostics raises particular ethical issues to be concerned about, such as privacy, bias, and job loss. With this integration, AI should be used responsibly and ethically.
• Regulatory Challenges: In such scenarios, AI diagnostics pose the most regulatory challenges. New technologies need a step-wise review and clearance process.
• Balancing It with the Clinic Workflow: Introducing AI tools into the prevailing clinic workflow is cumbersome and time-consuming. Improved integration and user acceptance are considered the essentials of successful implementation.
• Bias and Fairness: AI-based models tend to develop biases if the training data do not represent the targeted population. The issue of bias and fairness in AI-based diagnostics has been posed as essential in preventing the wrongful derivation of medical disparities in practice.
• Collaboration between humans and AI: AI has much to impart, but its input is indispensable here as it cannot fully substitute human intellect. So, optimal human-AI collaboration is necessary for the best possible diagnostic outcome.

Potential Future of AI in Healthcare & Medical Diagnosis

AI powered healthcare based diagnostics are still in their developing stages; however, they hold massive potential for growth. With years passed by, here are a few trends expected to shape the future of AI-based diagnostics:

• Personalised Medicine: AI will remain the main force behind this move toward personalised medicine, where treatment plans are tailored to each patient’s unique genetic and clinical profile.
• Collaboration with Humans: Doctors will not be substituted; they will be a potent tool to extend the human race’s potential. The most efficacious diagnostic systems will involve a fusion of the art of AI and human judgment.
• Democratisation: AI shall democratise healthcare, bringing sophisticated diagnostic tools to remote or underserved regions where specialists may not easily access them, thereby increasing the pool of potential patients served.
• Continuous learning: AI systems improve over time because they continuously learn new data and strive to make better diagnoses and fewer errors.

Final Takeaways

The potential of AI in diagnostics and patient care is not only a vision for the future, but it is already a reality we can use today. This transformational technology will help transform the care of patients and life-saving opportunities.

Being an AI software development company, AI integration with healthcare systems would enhance the results for patients and save their lives. Therefore, we invite healthcare organisations to use this critical tool to transform diagnostic capabilities. Together, we shall take leadership in a more innovative healthcare landscape where patients are placed at the centre.

Ready to unlock the true power of AI to transform your healthcare business?

Contact us today to discuss how our AI and Machine Learning development services shall enhance your diagnostic process and help improve patient care through advanced health software application development. Let’s build a healthier tomorrow!