AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
Curator: Aviva Lev-Ari, PhD, RN
We had researched the topic of AI Initiatives in Big Pharma in the following article:
- Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma
We are publishing a Series of Five articles that demonstrate the Authentic Relevance of Five of the Ten Digital IP Asset Classes in LPBI Group’s Portfolio of IP for AI Initiatives at Big Pharma.
-
For the Ten IP Asset Classes in LPBI Group’s Portfolio, See
https://pharmaceuticalintelligence.com/portfolio-of-ip-assets/
- PharmaceuticalIntelligence.com Journal – Projecting the Annual Rate of Article Views
• IP Asset Class V: 7,500 Biological Images in our Digital Art Media Gallery, as prior art. The Media Gallery resides in WordPress.com Cloud of LPBI Group’s Web site
Expert‑curated healthcare corpus mapped to a living ontology, already packaged for immediate model ingestion and suitable for safe pre-training, evals, fine‑tuning and inference. If healthcare domain data is on your roadmap, this is a rare, defensible asset.
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
Article Conclusions by @grok:
Conclusions and ImplicationsLPBI’s e-books are “ready-to-ingest” for Big Pharma AI, enabling from efficiency gains to diagnostic breakthroughs. No prior comprehensive ML attempts highlight untapped value [by Big Pharma. However, we conducted in-house ML on two of the e-Books]; bilingual editions support global/equitable applications. Technical Implications: Powers multilingual small models for precision medicine. Business Implications: Fuels ROI on investments like Pfizer’s $500M push; licensing potential for partnerships. Unique Insight: In AI’s scale race, these assets provide a “rare moat” via curated human opus—unlike raw data, they embed clinical foresight for transformative inference. The article is promotional yet substantive, with dense Amazon links and calls to resources (e.g., BioMed e-Series page, IP portfolio). It builds on the prior Class I piece by shifting to long-form, creative text for deeper AI personalization.
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class III: 100 e-Proceedings and 50 Tweet Collections of Top Biotech and Medical Global Conferences, 2013-2025
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class V: 7,500 Biological Images in LPBI Group’s Digital Art Media Gallery, as prior art
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders
In the series of five articles, as above, we are presenting the key AI Initiatives in Big Pharma as it was created by our prompt to @Grok on 11/18/2025:
- What are PFIZER’s AI INITIATIVES?
@Grok Response:
x.com/i/grok/share/0ol5VOJsEYs11baXq4xkzNb0h
| Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis. |
| Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines |
| Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools -Decentralize Trials -inventory management |
| Disease Detection and Diagnostics:
– ATTR-CM Initiative – Rare diseases |
| Generative AI and Operational Tools:
– Charlie Platform – Scientific Data Cloud AWS powered ML on centralized data – Amazon’s SageMaker /Bedrock for Manufacturing efficiency – Global Health Grants: Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care |
| Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database – AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine – Webinars of AI for biomedical data integration – Webinar on AI in Manufacturing |
| Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity – Part of $7.7B cost savings – Ethical AI, diverse DBs – Global biotech advances: China’s AI in CRISPR |
-
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
The Left Column was written @Grok
The Right Column was written by Aviva Lev-Ari, PhD, RN
|
AI Initiative at Big Pharma i.e., Pfizer |
e-Books Domain-aware Editorials and Curations |
| Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis. | The electronic Table of Contents of every e-book is a CONCEPTUAL MASTER PIECE of one unique occurrence in Nature generated by the Editor, or the Editors that had
– Commissioned articles for the e-Book – Had selected articles from collections of Categories of Research created by domain knowledge experts – Had reviewed the TOTALITY of the Journal’s Ontology and found new concept to cover in the e-Book not originally planned – The vision of the Editor-in-Chief of the BioMed e-Series that reflects the BIG PICTURE of Patient care delivery. – UC, Berkeley PhD’83 – Knowledge student and Knowledge worker, 10/1970 to Present – Conceptual pioneer of 26 algorithms in Decision Science of Operations Management decision support tools – 2005 to Present in the Healthcare field. – 2005-2012: Clinical Nurse Manager in Post-acute SNF settings and Long-term Acute care Hospital Supervisor – had developed a unique view on Diagnosis, Therapeutics and Patient care delivery – The BioMed e-Series is the EPITOM of human CREATIVITY in Healthcare an OPUS MAGNUM created by collaboration of top Scientists, Physicians and MD/PhDs – The 48 e-Books Published by LPBI Group – represent the ONLY one Publisher on Amazon.com with +151,000 pages downloaded since the 1st e-book published and Pay-per-View was launched by Amazon.com in 2016. |
| Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines | Two volumes on the BioMed e-Series were subjected to Medical Text Analysis with ML, Natural Language Processing (NLP).
– Cancer, Volume 1 (In English part of the Spanish Edition, Series C) – Genomics, Volume 2 (In English part of the Spanish Edition, Series B) – GPT capabilities are warranted to attempt to subject to ML every book of the MUTUALLY EXCLUSIVE 48 URLs provided by Amazon.com to LPBI Group, the Publisher. – 5 URLs for 5 Bundles in The English Edition: – Series A,B,C,D,E – English Edition – All books in each series – 5 Corpuses for domain-aware Small Language Model in English – All books in each series – 5 Corpuses for domain-aware Small Language Model in Spanish – 5 URLs for 5 Bundles in The Spanish Edition: – Series A,B,C,D,E –Spanish Edition
|
| Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools -Decentralize Trials -inventory management |
|
| Disease Detection and Diagnostics:
– ATTR-CM Initiative – Rare diseases |
– No one had attempted ML on every book, only two books were analyzed by ML.
– No one had attempted ML on all the Volumes in any of the 5 Series. – No one had attempted ML on all the 48 books – WHEN that will be done – a REVOLUTION on Disease Detection and Diagnostics will be seen for the first time
|
| Generative AI and Operational Tools:
– Charlie Platform – Scientific Data Cloud AWS powered ML on centralized data – Amazon’s SageMaker/Bedrock for Manufacturing efficiency – Global Health Grants: Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care |
Add the content of all the Books to Charlie Platform |
| Partnerships and Education
Collaborations: IMI Big Picture for 3M – sample disease database
AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
Webinars of AI for biomedical data integration
Webinard on Ai in Manufacturing |
e-Books are the SOURCE for Education
– Offer the books as Partnership sustenance |
| Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity – Part of $7.7B cost savings – Ethical AI, diverse DBs – Global biotech advances: China’s AI in CRISPR |
URLs for the English-language Edition by e-Series:
Series A: Cardiovascular Diseases ($515) https://www.amazon.com/gp/product/B07P981RCS?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks Series B: Frontiers in Genomics ($200) https://www.amazon.com/gp/product/B0BSDPG2RX?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks Series C: Cancer & Oncology ($175) https://www.amazon.com/gp/product/B0BSDWVB3H?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks Series D: Immunology ($325) https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks Series E: Patient-Centered Medicine ($274) https://www.amazon.com/gp/product/B0BSDW2K6C?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks
|
CONCLUSIONS: The Voice of Dr. Stephen J. Williams PhD
Overview: This is the second installment in a five-article series on LPBI Group’s digital IP assets for AI in pharma. It focuses on IP Asset Class II—48 e-books (bilingual English/Spanish editions)—as a proprietary, expert-curated textual corpus for training and inference in healthcare AI models. Drawing from a November 18, 2025, Grok prompt on Pfizer’s AI efforts, the article maps e-book content to pharma applications, highlighting untapped ML/NLP potential for small language models. Unlike Class I (journal articles), this emphasizes long-form editorial creativity and bilingual scalability, positioning the assets as a “defensible moat” for Big Pharma’s AI acceleration.Main Thesis and Key Arguments
- Core Idea: LPBI’s e-books, with 152,000 pay-per-view downloads (largest for any single Amazon e-publisher since 2016), offer domain-specific, human-curated content (e.g., conceptual tables of contents as “masterpieces” reflecting patient care visions) that outperforms generic data in AI training. This enables precise inference for drug discovery, diagnostics, and efficiency, fostering human-AI synergy.
- Value Proposition: The BioMed e-Series (5 series: A-E, each bundled as a corpus) totals 48 volumes from collaborations with top scientists/MD/PhDs. Editor-in-Chief’s expertise (UC Berkeley PhD ’83, decision science algorithms, clinical nursing) infuses “big-picture” insights. Valued for multilingual models; only two volumes (Cancer Vol. 1, Genomics Vol. 2) have seen ML analysis—full application could “revolutionize” disease detection.
- Broader Context: Part of LPBI’s 10 IP classes; five (I, II, III, V, X) are AI-ready via living ontology. Contrasts with open-source data by emphasizing ethical, diverse, creative inputs for foundation models.
AI Initiatives in Big Pharma (Focus on Pfizer)Reuses the Grok prompt on Pfizer’s AI, with key highlights (verbatim from article’s table):
|
Initiative Category
|
Description
|
|---|---|
|
Generative AI Tools
|
Saves up to 16,000 hours annually in literature searches/data analysis.
|
|
Drug Discovery Acceleration
|
AI, supercomputing, ML to streamline R&D timelines.
|
|
Clinical Trials & Regulatory Efficiency
|
Predictive tools, decentralized trials, inventory management.
|
|
Disease Detection & Diagnostics
|
ATTR-CM Initiative, rare diseases focus.
|
|
Generative AI & Operational Tools
|
Charlie Platform; AWS-powered Scientific Data Cloud; SageMaker/Bedrock for manufacturing; Pfizer Foundation’s AI Learning Lab for equitable care.
|
|
Partnerships & Education
|
IMI Big Picture (3M sample disease database); AIPM Symposium (drug discovery/precision medicine); Webinars on AI for biomedical integration and manufacturing.
|
|
Strategic Focus
|
$500M R&D reinvestment by 2026 for AI productivity; part of $7.7B cost savings; ethical AI with diverse DBs; global advances (e.g., China’s CRISPR AI).
|
Mapping to LPBI’s Proprietary DataA core table aligns Pfizer initiatives with e-book alignments, showcasing ingestion for AI enhancement:
|
Pfizer AI Initiative
|
e-Books Alignment
|
|---|---|
|
Generative AI Tools (16,000 hours saved)
|
Electronic TOCs as conceptual masterpieces: Editor commissions/selections/ontology reviews reflect big-picture patient care (UC Berkeley PhD ’83, decision science pioneer, clinical experience); BioMed e-Series as opus magnum of human creativity; 48 e-books with 152,000+ downloads since 2016.
|
|
Drug Discovery Acceleration
|
ML/NLP applied to Cancer Vol. 1 (Series C) and Genomics Vol. 2 (Series B); Extend GPT to all 48 books via 5 English bundles (Series A-E) and 5 Spanish bundles as corpuses for domain-aware small language models.
|
|
Clinical Trials & Regulatory Efficiency
|
(No specific mapping provided.)
|
|
Disease Detection & Diagnostics (ATTR-CM, rare diseases)
|
Untapped: No prior ML on all books/volumes/series; Full analysis promises revolution in detection/diagnostics.
|
|
Generative AI & Operational Tools (Charlie, AWS, etc.)
|
Ingest all book content into Charlie Platform for centralized ML.
|
|
Partnerships & Education (IMI, AIPM, webinars)
|
e-Books as education source; Offer for partnership sustenance.
|
|
Strategic Focus ($500M reinvestment, ethics)
|
Bundles enable diverse, ethical DBs; URLs for English Series: A (Cardiovascular, $515): [link]; B (Genomics, $200): [link]; C (Cancer, $175): [link]; D (Immunology, $325): [link]; E (Patient-Centered, $274): [link]. Spanish equivalents implied.
|
Methodologies and Frameworks
- AI Training Pipeline: Package e-books (textual corpus) for pre-training (ontology-mapped ingestion), fine-tuning (e.g., ML on series bundles), and inference (e.g., diagnostics queries). Bilingual support for global models.
- ML/NLP Demo: Applied to two volumes; scale to 48 books via GPT for small language models (5 corpuses/language).
- Productivity Model: Amplifies Pfizer’s savings (16,000 hours, $7.7B total) by injecting expert creativity; ethical focus on diverse, compliant data for equity (e.g., AI Learning Lab).
- Insights: Quotes editor’s vision as “epitome of human creativity”; predicts full ML rollout as first-of-its-kind revolution, differing from Class I’s article-based ontology by focusing on editorial depth.
Conclusions and Implications
1 web page
2012pharmaceutical
sjwilliamspa
Leave a Reply