How AI is Improving Diagnostics in Healthcare

The transforming technology – artificial intelligence (AI) has transformed various industries, including the healthcare industry. Its impact can be witnessed in terms of diagnostics, as it has significantly improved efficiency and accuracy.

Furthermore, the use of AI helps healthcare providers to reduce human errors, which positively improves the accuracy, efficiency, and speed of diagnostic processes.

The global market of AI is expected to grow from USD 10.4 billion in 2021 to USD 120.2 billion in 2028.¹ So, without much ado, let’s explore how AI is improving diagnostics in healthcare, focusing mainly on digital health and medical imaging.

Role of AI in Healthcare Diagnostics

Diagnostics is the process or technology used to determine the nature of a patient’s condition or health. Traditionally, healthcare providers rely heavily on medical tests and their skills to analyse symptoms and make informed decisions based on their experience.

However, we need to acknowledge that the complexity of healthcare and medical data is growing day by day; thus, there is a need for precision in diagnosis. This is a significant challenge for healthcare providers, which can be overcome with the help of AI.

Different AI models, like machine learning and algorithms, can be utilised to analyse vast amount of healthcare data quickly and accurately. Thus, it shall help improve the diagnostic efficiency.

As AI has taken over in many industries, it is expected to become the core of the healthcare industry, too. Thus, to stay relevant and competitive in the healthcare industry, you shall consider enrolling in an artificial intelligence in healthcare course. Let’s explore further and understand how AI can lead to more accurate diagnoses and improved patient care.

Enhancing Digital Health Diagnostics

Digital health encompasses a wide range of technologies aimed at improving healthcare delivery, including mobile health applications, telemedicine, and wearable devices. AI enhances digital health by providing tools that facilitate better diagnostics, monitoring, and management of health conditions.

Remote Monitoring via Wearable Devices

Wearable devices, such as smartwatches and fitness trackers, have gained popularity in recent years. These devices collect a wealth of health data, including heart rate, sleep patterns, physical activity, and more. AI algorithms can analyse this data in real time, offering insights into a user’s health status.

AI can identify irregular heartbeats or irregular blood pressure patterns and alert the patient regarding the need for prompt care.

Virtual Consultation

Virtual consultation gained significant prominence in the present era of digitalisation, especially after the Covid-19 pandemic breakdown, and it has become trendy. AI-powered platforms enable healthcare providers to conduct virtual consultations, allowing for quicker diagnoses without the need for in-person visits.

Chatbots and virtual assistants can guide patients through a series of questions, identifying potential conditions and suggesting next steps.

AI in Medical Imaging

Medical imaging is a critical aspect of diagnostics, providing visual representations of the interior of a body for clinical analysis. Techniques such as X-rays, MRI, CT scans, and ultrasounds play a vital role in diagnosing various medical conditions.

However, utilising AI-powered tools to analyse these diagnostic reports can help in reducing human errors and increase efficiency. Here’s how AI can be incorporated into medical imaging:

Automated Analysis

AI models, like deep learning, can be utilised for image analysis. These algorithms can be trained on large datasets of labelled images, learning to identify patterns associated with specific conditions.

For example, AI systems can detect tumours, fractures, or anomalies in radiological images with remarkable accuracy. As noted by Hosny (2018), AI tools in radiology can reduce the workload of radiologists by 20-50%.³

Improve Efficiency

In addition to enhancing diagnostic accuracy, AI improves workflow efficiency in medical imaging departments. Radiologists often face a heavy workload, interpreting thousands of images each day. AI can assist by prioritising cases based on urgency and flagging abnormal images for immediate review.

For instance, a study performed by Nature in 2020 identified AI model developed by Google Health outperformed the radiologists in breast cancer detection.²

Predictive Analysis

AI’s predictive capabilities can also be leveraged in medical imaging to forecast patient outcomes based on imaging data. By analysing historical data, AI can identify trends and make predictions about disease progression or treatment efficacy.

It was noted in a 2020 study that AI can predict the recurrence of breast cancer with an accuracy of 74%, compared to 69% for traditional methods. Thus, the health providers can rely on AI’s abilities.⁴

Challenges and Considerations

Despite having innumerable benefits, healthcare professionals may have to face various challenges too, including:

Data Privacy and Security

The use of AI requires access to large datasets, including sensitive patient information. Ensuring the privacy and security of this data is paramount.

Bias in AI Interpretation

AI algorithms are only as good as the data they are trained on. If the training data is biased or unrepresentative, the AI’s predictions may be flawed. Ensuring diversity and representativeness in training datasets is crucial for equitable healthcare outcomes.

Regulatory and Compliance

The deployment of AI in healthcare raises ethical questions regarding accountability and responsibility in diagnosis. Regulatory frameworks must be established to ensure the safe and ethical use of AI technologies.

Resistance to Change

People are afraid of change, and despite being a transformational technology, AI is expected to face resistance from healthcare professionals. Thus, the businesses shall provide adequate training and learning opportunities for the professionals to help them effectively utilise the technology.

Conclusion

AI is undeniably transforming the landscape of diagnostics in healthcare, mainly through its applications in digital health and medical imaging. By enhancing accuracy, efficiency, and personalisation, AI is paving the way for improved patient outcomes and a more effective healthcare system.

Furthermore, enrolling in an AI in healthcare course can help healthcare providers overcome challenges associated with utilising technology effectively. Additionally, it will help them improve their overcome productivity and efficiency.

References

1. Grand View Research (2022) AI In Healthcare Market Size, Share & Trends Analysis Report By Component (Hardware, Services), By Application, By End-use, By Technology, By Region, And Segment Forecasts, 2024 – 2030. Available at: https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-market#:~:text=b.,USD%2010.4%20billion%20in%202021.&text=The%20global%20market%20for%20AI,USD%20120.2%20billion%20by%202028.
2. Killock, D. (2020) ‘AI outperforms radiologists in mammographic screening’, Nature Review Clinical Oncology, 17(134). https://www.nature.com/articles/s41571-020-0329-7
3. Hosny, A. (2018) ‘Artificial Intelligence in Radiology’, Nat Rev Cancer, 18(8), pp. 500-510. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6268174/
4. Mazo, C., Aura, C., Rahman, A., Gallagher, W.M. and Mooney, C. (2022) ‘Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: a systematic review’, Journal of Personalized Medicine, 12(9), p.1496. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500690/