How AI applications changing/ helping the Regulatory Affairs..?

The recent past technology advancements or applications of Artificial Intelligence (AI) tremendously discussed, seeable and debated auspicated to deliver revolution, especially in the medical and bio pharma segment —from aiding drug discovery, development, post marketing (safety detection), diagnostics and patient engagement and finally have become part of the lexicon in the industrial revolution.

That’s highly beneficial, happy and fairly good for bio, pharma research as well healthcare segments.  What’s not so enhancing is that there are solid or fairly decent risks, which, if not befittingly and instantaneously buttonholed, might retard the benefits that Artificial Intelligence has to put up to both of these segments; as the industry deals with information heterogeneity. Up to now, AI has been positioned in visual, numerical diligences of information only in pilots for medical record excavation, biometrics and optical diagnostics.  The achievement in these areas only countenances more mystifying evaluation of AI before bulk form data takes over.

The application of AI in textual information from various sources such as medical journals, health authority directories, biopharmaceutical literature, professional blogs, social media channels, public articles, web-search, etc in a number of life sciences processes such as regulatory affairs or regulatory intelligence, clinical trial intelligence, literature search for  scientific writing, market intelligence, patent intelligence, pharmacovigilance, drug safety, product labeling, etc, have some great success stories , with limited ambit. The intellects are not too unmanageable to witness. Medical care context and rendition are vital to reasonably process medical care information, either it is voice or speech to text conversion, Information extraction from unstructured or semi structured channels, healthcare bots, structured content authoring, etc. This entails that artificial intelligence or modern-day technologies by themselves are not sufficient. Contextualizing these applications with subtlety human intelligence or clinical expertise is highly significant, something that the segment has initiated to see for themselves. For instance, accurately bringing the treatment formulation Vs.  The suspect / accompaniment drug, Symptom or Indication Vs. adverse event, etc, requires the right point of histo,patho physiological or disease profile understanding and that only human intelligence can bring (Qualified professional is the right personnel)where artificial intelligence can’t be replaced their role however simplifies their process understanding or making an insight for providing accurate treatment procedures or options .

Still, there are areas where AI applications having important role and significant base. For instance in the scientific arena , AI applications can be contextualized and tailor-made for real-time voice -to-text changeovers to repulse both efficiency and efficacy of scientific data or safety information or product quality complaint. Similar manner, data collection, extraction and auto-popup of scientific information from structured, semi-structured, and unstructured channels is also a magnificent possibility of the AI, where the algorithms pulls up the quality data with lesser efforts, with lesser resources and time.

Not only voice to text, data collections, the AI can have much more stronger, extensive and bigger usage in other life science area as well for instance; the industry is researching AI-led platforms for response generation for regulatory queries, search and retrieval of legislation, policy, guidance, document, or evidence base information from regulatory repositories and authoritative sources importantly piloting various channels evidence to provide exact, latest legislation information, touching the efficiency , strength and potency of the drug regulatory information process. This has the possibility overtime to germinate into Regulatory chatbot. 

One of the really exciting things is, AI potential to transmute document authoring and management, that can possibly impact the creation and upkeep of number of drug authority documents (Clinical, marketing, safety, Quality/ CMC, labeling, advertisement, Non clinical, licensing - checklists, forms, procedure other regulatory publications, scientific and non scientific) documents by leveraging machine learning algorithms for indexing, for easy retrieval and helps for perfect decision making; Natural Language Processing (NLP) rules refabricate components for document authoring, auto classify the document without human intelligence, parse the documents also web crawl techniques helps to bring new updates as and when there is a change in the health authority repositories finally these technology combinations will accurate, brings timely info, would definitely impact the effectiveness, efficiency and eases regulatory commitments process can be for new filing, other submissions eventually increases RA productivity and shortens the time spent finding, analyzing new information for the regional need(s).