While a number of AI-enabled devices are out in the market, and significant developments in drugs and medical devices are also happening, the task of releasing them to the market is no easy feat. Governed by regulatory bodies, any drug or medical device undergoes stringent checks and trials before it hits the market. 

Recently, the US FDA (Food and Drug Administration) cleared an AI device last week that can assist in detecting all types of skin cancer. The AI-powered device DermaSensor is reported to aid physicians in detecting skin cancer, with its pivotal trial demonstrating a sensitivity of 96% and a 97% probability of accurately identifying a skin lesion as benign. While the product was only recently approved, the approval process for DermaSensor began eight years ago. 

Obstacles for Path To Launch

The U.S. Food and Drug Administration – FDA, which is a federal agency within the Department of Health and Human Services, plays a vital role in regulating the development and marketing of any new medical drug or device, however, the process to get approval from the department is lengthy. 

The US has one of the most stringent regulations for medical drugs and devices in the world. The average time from FDA application to approval of drugs is 12 years. Furthermore, the projected average cost of bringing a new drug from concept to market surpasses $1 billion. 

The only biggest exception to the lengthy problem, was witnessed during the pandemic. Pfizer and BioNTech were granted Fast Track Designation for mRNA-based vaccine development, thereby expediting the release process to less than a year. 

Significant advancements are also being made to smoothen out approvals and upgrades for AI devices. Last year, the FDA proposed a plan that will allow developers of AI-enabled medical devices to automatically update their products that are already being used in the clinic. 

“While dozens of companies have attempted to address this problem in recent decades, we are honoured to be the first device cleared by the FDA that provides PCPs (Primary Care Physicians) with an automated tool for evaluation of suspicious lesions,” said Cody Simmons, co-founder and CEO of DermaSensor. 

In India, the Central Drugs Standard Control Organisation (CDSCO) takes around six to nine months for completing any medical device registration, only if a technical presentation or Subject Expert Committee (SEC) review is not required. The registration does not expire as long as the maintenance fee is paid every five years. As of now, there is no separate provision for AI-enabled devices.

AI-enabled device for detecting skin cancer

AI Assistance Might Transform Oncology 

While the discussion on AI replacing doctors is far-fetched, the role of AI in diagnosis and assisting doctors are finding large use cases. Traditional methods of skin cancer detection involve dermoscopy where a medical professional physically examines a patient’s skin and lesions. However, this approach has room for human error. The diagnosis accuracy for such screening techniques is estimated at 75-84%. DermaSensor delivers a 97% of accuracy for identifying lesions as benign. The device can detect all three types of skin cancers-melanoma, basal cell carcinoma and squamous cell carcinoma. 

“We are entering the golden age of predictive and generative artificial intelligence in healthcare, and these capabilities are being paired with novel types of technology, like spectroscopy and genetic sequencing, to optimise disease detection and care,” said Simmons. 

When brought in contact with the skin, DermaSensor emits light, and then captures the wavelengths of light reflecting off cellular structures beneath the skin’s surface. It then utilises specific algorithms to analyse the reflected light and detect the presence of skin cancer. The algorithms are trained on data of more than 4000 malignant and benign lesions. 

A number of deep learning-based methods have been proposed to assist dermatologists in accurate diagnosis of skin cancer.  

Last week, MIT released two AI-powered models, PRISM neural network (PrismNN) and logistic regression (PrismLR), that helps with early detection of pancreatic cancer. The model is reported to identify 35% of cancer cases, while conventional screening methods only result in a 10% identification rate.

As the regulatory bodies aim to ensure a safe deployment of AI-enabled devices, delays in approval are likely to persist.