Generative AI with Google Lens to detect counterfeits in pharma

Amaninder Singh Dhillon, Consultant explains how combining generative AI and image recognition can help create a more efficient and accurate solution to identify counterfeit drugs and protect public health

Counterfeit pharma jeopardise patient safety, infringe on intellectual property rights, and undermine the credibility of the pharma industry. Current methods of combating counterfeits are often time-consuming, expensive, and prone to human error.

The recent government announcement that 300 samples would have QR codes has some restrictions. QR codes can be rendered inoperable by scratching them off using a sharp object or marker. It is useless if the QR codes are broken. What use does the label serve, and who is responsible for making sure the QR code is still working when it changes commercial hands? What assurance is there that counterfeiters who can easily mimic a brand’s packaging, name, and logo won’t also be able to mimic its QR codes?

A more efficient and accurate solution is required to identify counterfeit drugs and protect public health.

Combining generative AI and image recognition is probably the answer. With the use of Google Lens and Generative Artificial Intelligence (AI) technology, a complex system for assessing photo metadata (Photo Meta Data) on pharma packaging and verifying the authenticity in real time.

I’d want to make a recommendation for pharma companies, government officials, and customers to combine the strength of AI with picture recognition to create a foolproof way to restrict the risks connected with counterfeit drugs.

*In 2018, Google Images introduced some new features to its image search results. Next to a selected photo, the creator of the image, the credit line, and a copyright notice are immediately displayed. This works by reading the corresponding IPTC photo metadata fields embedded in the image file. On August 31, 2020, this feature was enhanced to also display a licensable badge above an image and a link to the licensing information.

System requirements

a. Generative AI: Implement advanced Generative AI algorithms that analyse visual patterns, text, and packaging features to identify discrepancies between genuine and counterfeit pharma packs.

b. Google lens: Utilise the powerful visual recognition capabilities of Google Lens to capture and interpret codes on the packaging swiftly.

c. Cloud-based database: Establish a cloud-based database containing comprehensive information about authorised pharma products, including packaging details, manufacturing locations, and authorised distributors.

d. Machine learning: Train the system using machine learning techniques to improve accuracy and adaptability over time.

System workflow

1. Generative AI will be used to create unique invisible image tags for each product (like UID). This can be done by training the AI on a dataset of existing batch numbers, allowing it to generate new, unique invisible image tags on brand logo / brand name that cannot be replicated by counterfeiters

2. These unique image tags will then be printed on the packaging of each pharma product during the manufacturing process. (Covertly on Brand Logo and Brand Name)

3. Google Lens will be used by consumers to scan these image tags. Upon scanning, Google Lens will verify the code against a database of legitimate codes (image tags). If the code matches one in the database, the product is confirmed as authentic. If not, the product is flagged as potentially counterfeit.

4. In addition, the system can provide detailed product information, such as manufacturing date, batch number, expiry date, etc., to further assure the consumer of the product’s authenticity.

Key features and benefits

a. Real-time authentication: The system provides instant verification of pharma packs by scanning image tags using Google Lens and analysing visual patterns with Generative AI. This ensures rapid and accurate identification of counterfeit products.

b. User-friendly interface: Develop a user-friendly mobile application or web portal that allows consumers, healthcare professionals, and regulatory authorities to access the system easily and perform authentication checks.

c. Comprehensive database: Maintain a cloud-based database containing detailed information about authorised pharma products, enabling prompt comparison and verification during scanning.

d. Enhanced traceability: Enable pharma companies to track their products throughout the supply chain, reducing the risk of counterfeit products entering the market and facilitating targeted recalls if necessary.

e. Regulatory compliance: Support regulatory authorities in enforcing pharma regulations and combating counterfeit drugs by providing them with a reliable tool for inspections and investigations.

f. Consumer empowerment: Empower consumers to verify the authenticity of pharma products before purchasing, thereby increasing trust in the market and reducing the potential harm caused by counterfeit drugs.

Counterfeit pharma products are a growing global concern. By offering a scalable and reliable solution, our business aims to address the needs of pharma manufacturers, regulators, healthcare professionals, and consumers worldwide. The market potential is substantial, with potential revenue streams including licensing the technology to pharma companies, charging fees for database access, and offering premium services for enhanced features.

By harnessing the power of Generative AI with Google Lens for scanning image tags on pharma packs, proposed solution provides an efficient, accurate, and scalable approach to combating counterfeit pharma products. With enhanced authentication capabilities, regulatory compliance support, and empowered consumers, the solution aims to make a significant impact on public health and safety while fostering trust in the pharma industry.

counterfeit drugsGenerative AIGoogle lensintellectual property rightspublic health
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