Artificial Intelligence In Pharmacy: Use Cases, Examples, Challenges

In recent years, artificial intelligence solutions have become a part of many industries. The technology encompassing everything from intelligent conversational solutions to complex computer vision tools is transforming retail, education, e-commerce, and logistics.

AI solutions have a bigger range of applications than helping with customer support or automating tasks around the office. More industries discover their value in research and development, with artificial intelligence and pharmacy not being an exception to this rule. In this article, we’ll cover its practical use, main applications, examples, challenges, and future.

How Artificial Intelligence Is Used In Healthcare

The latest advances in the field of AI have made them an integral part of many medical institutions and organizations. In the US alone, 22% of clinics have early-stage AI initiatives. Hospitals, research facilities, and other entities involved in the healthcare sector have found ways of using generative and computer vision solutions in their daily work.

Many organizations use AI chatbots to process patients, schedule appointments, and provide advice based on their symptoms. Additionally, these products help create test medical data, generate medical images for better diagnoses, and make personalized treatment plans. Some hospitals even use pharmacy artificial intelligence solutions for drug discovery.

Computer vision solutions also play a significant part in the medical industry. Products based on AI technology let experts get better dental images, monitor post-operational blood loss, run clinical trials, and discover dangerous diseases in their early stages. The use of AI for pharmacy also has multiple applications that are often overlooked.

The Role of Pharmacy Artificial Intelligence Solutions

Modern AI solutions play a considerable role in the work of pharmaceutical organizations. Today, eleven major US pharma companies use this tech for different purposes. These applications help improve workflows, medication management, and patient outcomes. Here’s how AI helps in these scenarios.

Better medication management

This is one of the areas where the use of AI has seen the most impact. AI-based support systems allow pharmacists to select drugs and their dosage better. With their help, professionals better understand potential adverse events and drug interactions, reducing the chance of medication mistakes.

Improved patient safety and outcomes

When it comes to the pharmaceutical industry, patient safety is a top priority. Using artificial intelligence in pharmacy helps detect and prevent various possible errors and hospital readmissions. These products can establish early stages of adverse events and address them promptly to avoid negative consequences.

Streamlined and efficient workflows

Modern AI-based tools automate various processes critical for the pharmaceutical industry. Their versatility allows professionals to focus on patient care and research. For example, robotic dispensing solutions that accurately package and label medication. These tools also help with inventory management and drug demand prediction.

10 Uses Cases For Pharmacy Artificial Intelligence Tools

By 2025, almost 50% of pharmaceutical companies will use some version of AI technology. This shows that the industry recognizes the transformative power of these tools and wants to invest. Currently, the technology has ten popular applications among organizations in this sector.

1. Automated quality control. Using AI’s subset computer vision technology allows companies to inspect raw ingredients, components, and end products automatically. This approach reduces medical risks and product defects.
2. Better compliance. Pharmaceutical companies accelerate approval timelines for new drugs by up to 70%. AI tools speed up this process by gathering the necessary information, such as manufacturing, preclinical, and clinical data.
3. Cancer research. Artificial intelligence solutions are becoming crucial in cancer research and drug development. Pharmaceutical companies use it to adjust development strategies. The tech’s algorithms predict which types of this disease will become resistant to existing medical products.
4. Clinical trials. With the help of AI, researchers streamline the clinical trial phase of drug discovery. This technology helps identify suitable candidates based on their medical history. Experts also use it to design and monitor clinical trials.
5. Drug discovery. Modern AI tools allow pharmacists to discover new medicine faster, using machine learning and big data. This reduces the time needed to develop new vaccines, lets experts consider possible mutations, and prepares for future research.
6. Drug repurposing. Pharmaceutical companies use AI to find new therapeutic uses for available drugs. This saves time and resources in discovering new medical treatments and better responding to sudden rises in infectious diseases.
7. Medical adherence. With artificial intelligence solutions, pharmacists have a better chance of finding the right doses and intervals for patients. This practice improves medical outcomes.
8. Personalized treatment. AI-based CV solutions accurately analyze patient reports, helping physicians develop tailored treatment options. This approach significantly improves care outcomes.
9. Predictive forecasting. Using AI in the pharmaceutical industry allows for predicting seasonal illnesses and pandemics. It lets medicine producers prepare the supply chains for extreme conditions and balance supply and demand.
10. Streamlined production. Introducing AI into drug manufacturing allows firms to keep the production lines running. This technology helps predict and address potential breakdowns of the supply chain and malfunctions with manufacturing tools.

Artificial Intelligence In Pharmacy: Best Real Examples

Most pharmaceutical companies don’t like disclosing their approach to using AI. While they keep a tight lid on this integration, several big names in this industry explained how they use AI. Here are the best examples we came across.

AstraZeneca

In 2021, the company worked with Oncoshot to find suitable candidates for clinical trials. Their collaboration helped select five targets for the trials of their drugs. Two were related to chronic kidney disease, and three to idiopathic pulmonary fibrosis. Since that time, the big pharma giant and the company partnered up to combat heart failure and systemic lupus erythematosus.

Bayer

Bayer and Exscientia partnered to experiment with artificial intelligence in small molecule drug discovery. As part of this agreement, Bayer’s expertise and Exscientia’s AI platform help speed up the process of finding candidates for new drug products that fall under the company’s main areas of expertise.

Eli Lilly

One of the world’s biggest pharma companies, Eli Lilly, announced many AI projects in June 2023. Its plan was to save millions of work hours and streamline many processes. Additionally, the company wanted to use AI to speed up drug discovery and automate regulatory processes. Lilly’s CEO expected that this approach would make the company more productive.

Pfizer

This pharmaceutical giant has been using supercomputers and AI since 2020. Pfizer uses these technologies to create new drugs and the COVID-19 vaccine. These technologies helped the company reduce computational time by 80-90% and design the antivirus treatment in the span of four months. Pfizer also works with CytoReason on an AI model of the immune system.

Sanofi

In 2018, the pharmaceutical company developed the Plai AI platform with the help of Aily Labs. Its goal was to use the tech for drug discovery, trials, and production. Plai uses Sanodi’s data to help the company make decisions for different parts of the development process. Sanofi also produces AI-connected insulin pens, demonstrating interest in healthcare products.

Challenges Of Using Artificial Intelligence In The Pharmacy Sector

Despite the advantages and proven use cases of combining artificial intelligence and pharmacy, the technology still has challenges companies must face. They influence the outcome of patient care and the efficiency of the end product. Here are the main drawbacks pharmaceutical providers have to contend with in order to use this technology efficiently.

1. Bias. In some cases, collecting data for training AI models creates biased outcomes. Even with accurate and representative data, there can be problems if it reflects inadequacies and underlying healthcare system biases.
2. Clinical implementation. There’s a lack of empirical evidence proving AI interventions' efficiency during clinical trials. Most AI research is retrospective and run in a controlled environment, making their results hard to check in real scenarios.
3. Data privacy and security. One of the main concerns about using AI among pharma companies is the work of solutions with potentially sensitive information. Patients and clinical study participants are concerned about their private data being leaked through AI datasets.
4. Data integration. Another problem arises when an AI system learns irrelevant connections between patient variables and outcomes. This can cause too many variables in relation to outcomes, leading to predictions with inappropriate features.
5. Ethical concerns. Accountability remains a top concern when using AI for pharmaceutical purposes. This makes it unclear who’s responsible for scenarios where things go wrong.
6. Patient safety. Data collected for selecting candidates and other activities can miss crucial points. This leads to errors and challenges in processing medical information via AI tools. ML algorithm decision mistakes are associated with inappropriate algorithms for particular data types.
7. Social concerns. Finally, there’s a social factor linked to the use of AI in the pharmacy sector. Healthcare workers fear their jobs will become obsolete because of the technology. However, this concern can be addressed by making people better understand the benefits of this tech.

The Future Of Using AI For Pharmacy

The latest developments in this field show the potential of AI revolutionizing the pharmaceutical industry. Further fine-tuning of these tools will benefit specialists in this field and patients. AI technology can potentially eliminate risk factors associated with drug discovery and clinical trials.

Additionally, artificial intelligence speeds up different areas of the drug development process and improves patient outcomes. Its wider use will reduce production costs through higher efficiency and error reduction. Despite its current limitations, AI integration can outweigh its potential drawbacks when used properly.

The global market of pharmaceutical AI products had a value of $908 million. By 2032, this sum is expected to reach more than $11,8 billion. These numbers demonstrate that the market will grow by a CAGR of 29,30%. These figures show that pharmaceutical companies will continue investing in this technology.

Conclusion

Artificial intelligence products already play a significant role in the broader medical industry. We are happy to participate in this process with several products made for this field under our name. If you have ideas about using AI for pharmaceutical products, we’re ready to hear your ideas and help make them real.