AI in Healthcare: Benefits, Applications, and Cases

Here are some significant applications of AI in healthcare:

Dermatology Scans for Melanoma

Skin cancer ranks among the most common types of cancer, with melanoma being a less frequent but particularly dangerous form. This year alone, melanoma is projected to cause over 7,000 deaths in the United States. The challenge with melanoma is its potential to metastasize, spreading to other parts of the body and invading surrounding tissues. AI technology is playing a crucial role in improving the early detection and diagnosis of this serious condition.

Here’s what you need to know:

Early detection is key—up to 86% of melanoma skin cancers can be prevented and treated if identified in their initial stages. This is where artificial intelligence and machine learning come into play.

Additionally, it’s important to note that distinguishing early-stage melanoma from benign moles and other malignant tumors can be quite challenging. To address this, medical professionals are turning to AI for assistance in their diagnostic decisions.

Lung Cancer

Detecting cancer has always been a significant challenge, and it continues to be. However, thanks to artificial intelligence (AI), lung cancers can now be identified through chest X-rays.

A study conducted by clinical researchers at Seoul National University Hospital revealed that a commercially available AI tool outperformed a team of four thoracic radiologists in both initial and subsequent readings.

“Windowing” allows both humans and robots to visualize X-ray details that exceed the color spectrum capabilities of standard monitors.

The artificial intelligence algorithm:

• Achieved higher specificity during initial readings compared to radiologists.
• Assisted team members in more effectively interpreting findings during the second read.

Overall, the results of this study are promising. However, many medical professionals remain skeptical about AI algorithms’ ability to deliver consistent results in clinical settings.

Implementing machine learning technologies in healthcare can also be quite challenging. Some organizations need to upgrade their existing software systems to accommodate advanced machine learning capabilities, while others require more sophisticated data management and governance processes to ensure the highest security standards for processed imaging and patient records.

Digital Pathology

In a recent study, board-certified pathologists diagnosed colorectal cancer with an accuracy of 96.9%, while AI slightly outperformed them with an accuracy of 98%.

The rapidly evolving field of digital pathology enables professionals to virtually represent glass pathology slides for in-depth analysis. AI supports pathologists in several key areas, including:

• Enhancing the accuracy of early detection and diagnosis
• Examining tissue samples in intricate detail
• Analyzing and interpreting images
• Identifying pathology categories linked to previous cases

A recent collaboration among cancer specialists involved reviewing whole slide images (WSIs) from 11,000 cancer patients across 32 cancer subtypes in a public database. The trained software combined annotations and image data to reach a “computational consensus” regarding the diseases presented.

The algorithm demonstrated impressive accuracy in identifying various pathology types on frozen section slides:

• 97% for kidney renal clear cell carcinoma
• 93% for bladder urothelial carcinoma
• 99% for ovarian serous cystadenocarcinoma

It also achieved similar accuracy with histopathology slides:

• 99% for skin cutaneous melanoma
• 100% for thymoma
• 98% for prostate adenocarcinoma