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How an AI-Driven Innovation Platform Can Benefit Your Life Sciences Organization


Finding the right information quickly from huge volumes of data is critical to support business decisions. However, most data is unstructured, and a manual review of the vast amounts of unstructured data requires a lot of manpower and can be costly. In the healthcare and life sciences industry there is a massive amount of data to compile. Internal documents, journal articles, patents, lab notes and clinical reports are just a few on the infinite list of data sources. Using conventional keyword search and manually scanning is no longer a practical option for the surmounting data. It also does not allow for insights into solving problems or analyzing information.

Healthcare and life sciences professionals need a platform that offers a way to browse and interact with data and search results. The more informed and structured data becomes, the faster you can get the answers you need to make decisions based on facts, rather than intuition.

Since most data is unstructured, an AI (Artificial Intelligence)-driven software platform, like GENAIZ, is the solution. Platforms using Natural Language Processing (NLP), Machine Learning (ML) and artificial intelligence (AI) technology offer users access to specialized tools like data aggregation, knowledge management and semantic search. All of which come together to streamline analysis of data and offer insights.

Here are ways that your healthcare and life sciences organization can benefit from using a platform with cutting-edge AI/ML-based intelligent assistants.

Ontologies help the AI understand your search

Ontologies are a powerful tool that help you access your data in a way that it can be understood. They allow a high-level overview that breaks down a subject and related areas. Ontologies codify domain terminology, concepts and relationships. Once the data has been linked to an ontology then this data understands you, what you are searching for and how you search. More concisely, mapping data to an ontology means that the AI can now understand the data in terms of the domain expert to ‘understand’ how the domain expert thinks. Ontologies can provide lists of key concepts, with names and synonyms and even take the context of terms into account.

Whether it is search, question answering or the discovery of new knowledge, ontologies help data speak your language and helps the AI understand the true meaning of your search. The GENAIZ platform can incorporate numerous ontologies. For example, an epidemiology ontology can include multiple facets of transmission modes, demographic parameters, etc. Using specialized NLP, the data can be linked to epidemiological concepts and a knowledge graph can be created that facilitates activities like question answering.

QA response system provides responses

When you query a cutting-edge AI/ML/NLP-based platform, like GENAIZ, you will receive a contextualized answer, based on the millions of data inputs it searches. An AI-driven innovation platform solution using NLP, can process massive amounts of unstructured data, recognize linguistic aspects and use semantics to determine the actual context and meaning of a concept to provide an answer, not just a list of documents.

Questions are passed through GENAIZ’s enterprise search platform and initial results are processed with deep neural networks, similar to BERT, to locate the best answer. Using appropriate ontologies to ensure a greater understanding of medical, scientific and clinical text, the QA response system can process the important data and clearly provide factoid and non-factoid responses.

With non-factoid answers, the AI looks across multiple sources and attempts to synthesize and answer your query by pulling information from multiple documents. It can even go so far as to synthesize new sentences generated by the AI to provide answers.

For factoid answers, where the answer already exists within the data, the AI will figure out the most relevant documents and provide a link to the source documents where the answer can be found. For example, if you asked, ‘what are the effects of cancerous tumor growth?’ Within the list of sources documents ranked as most appropriate, you would likely find an article like ‘cells with abnormal DNA’, where it would talk of effects of cancerous tumor growth.

Semantic Search

Semantic search is a natural language search

Semantic search allows you to access vast knowledge bases that would otherwise be unreachable. It is a search with meaning that seeks to improve search accuracy by choosing points of reference such as synonyms, variation of words, concept matching and natural language queries to provide relevant searches. Not only is the way the search is conducted important, but how the knowledge is retrieved plays a vital role. Using NLP, semantic search understands the meaning of what you are asking for, not only the literal keyword, but the meaning. For example, if you are searching ‘Brittle diabetes mellitus’, a keyword search will give you all files, notes and entries that include the actual word ‘Brittle diabetes mellitus’. Semantic search will bring up everything that includes ‘Brittle diabetes mellitus’ but will also provide you with things related to the term and pseudonym including labile diabetes, brittle diabetes, and unstable diabetes mellitus.

Modern platforms using NLP for semantic search can analyze huge amounts of structured and unstructured data in a consistent and unbiased manner. They can interpret the different languages to extract the key facts and relationships between the text. They collect all the unstructured data and use indexing tools like content extraction, tokenization and stop word filtering to compile it into computable data that offers a more intelligent search.

Search and question answering are some of the powerful AI/ML tools that users can apply to explore their data web and help jump straight to an answer. The results offered can be articles, external or internal documents, as well as previous internal initiatives or initiative from other platform community instances. When you consider the large amount of data created daily from clinical trials and electronic health records, to name a few, automation saves a lot of valuable time.

Recommendations put the focus on a solution

Based on a similar NLP pipeline as semantic search, the GENAIZ platform offers a powerful passive knowledge-based recommendation engine. These recommendations encourage convergent thinking to focus on finding one solution or answer by understanding your subject and related subjects. It is designed to help you in whatever activity you are trying to perform in GENAIZ.

The recommendation system that leverages collaborative filtering, content-based filtering and other techniques combined with deep learning and NLP. The recommendation system combines your activities and current context into a function that provides a list of recommended links and documents. For example, a project manager engaged in a costing exercise would be provided with links to previous or closely related costing outcomes, best practise approaches to project costing and experts in the organization who could assist or answer questions.

Insights broaden your search

Where recommendation systems are focused on providing closely related results, Insights encourage divergent thinking by presenting unexpectedly related information on your search subject. This ‘serendipitous’ information aims to add aspects to your search to broaden your query. Insights are able to consider factors such as varied user interests and the desire for change and incorporates this ‘serendipity’ into the recommendation engine to improve result quality and usefulness.

For example, if you were searching for ‘lung cancer’, insights may bring up the related subject of ‘lung cancer tomosynthesis’. Tomosynthesis is a method for performing high-resolution limited-angle tomography at radiation dose levels comparable with projectional radiography. In relation to lung cancer, when compared with conventional chest radiography, chest tomosynthesis can offer improved sensitivity in the detection of CT-proven lung nodules. This is a serendipitous result given such a high-level search of lung cancer.

Flexible, intuitive and interactive, insights ensure you can identify and extract vital and otherwise hidden knowledge from unstructured data.

Flexibility encourages innovation

The GENAIZ AI-driven innovation platform is designed to grow and adapt. Tailored to clients evolving needs, integration of almost any data source is managed without costly technological reconfiguration or time-consuming enhancements. Semantic search filtering, categorization and grouping of results can be configured for various users and deeper accurate results based on ontologies can be added over time.

The flexible GENAIZ interface is intuitive and does not lock users into a series of pre-defined operations, allowing explorations and trials, without the need to keep track of the data entered. The platform is constantly adapting to help the user progress, for whatever path they choose. This makes it possible to consolidate, measure projects on similar grounds and assess global progression on whatever framework or methodology is used by scientists and other personnel involved.

The interactive platform has an impactful change on the entire innovation process for healthcare and life sciences organizations. Clients using the GENAIZ AI-driven innovation platform can reduce their workload, avoid duplication of work, increase innovation success rate, speed up time to market and reduce costs.

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GENAIZ is a revolutionary AI/ML solution that sits on top of existing infrastructure allowing clients to foster innovation by encouraging collaboration and improving knowledge sharing, without unnecessary complexity.


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