ISSN: 2455-2976
Journal of Cardiovascular Medicine and Cardiology
Short Communication       Open Access      Peer-Reviewed

In theory-Sharing AI’s black box sounds great-But in reality it’s not

Richard M Fleming1*, Matthew R Fleming1 and Tapan K Chaudhuri2

1PhD, MD, FHHI-OmnificImaging-Camelot El Segundo, CA, USA
2MD, Eastern Virginia Medical School Norfolk, VA, USA
*Corresponding author: Richard M Fleming, PhD, MD, JD, FHHI-OmnificImaging-Camelot El Segundo, CA, USA, E-mail:
Received: 20 December, 2019 | Accepted: 25 January, 2020 | Published: 27 January, 2020
Keywords: FMTVDM; AI: Artificial intelligence, ML: Machine learning: M2M: Machine-to-Machine; Cardiology; Oncology

Cite this as

Fleming RM, Fleming MR, Chaudhuri TK (2020) In theory-Sharing AI’s black box sounds great-But in reality it’s not J Cardiovasc Med Cardiol 7(1): 002-003. DOI: 10.17352/2455-2976.000103

In theory, the concept of sharing scientific information and knowledge seems safe [1]. However, in reality those who seek to obtain that knowledge are not always so pure in motive – consider Nobel’s experience with dynamite (Patent # 78317) - something driven home at every meeting we present The Fleming Method for Tissue and Vascular Differentiation and Metabolism (FMTVDM) at [2], an example of which is shown in Figure 1.

Last year during the 2018 ASNC Conference, we presented research information on an AI for quantification of nuclear imaging utilizing Quantitative Coronary Arteriography (QCA) and FMTVDM for Machine-to-Machine (M2M) learning [3,4]. The proprietary equations derived over two decades are a key component to the application of the intellectual property of the utility patent-FMTVDM [5-7]-and are continuing to evolve. Fortunately these proprietary equations remain separate from the patent. Providing a double safe guard against those whose motives is less focused on providing better patient care and more attentive on making money.

Every time we present-either at conferences or publications-we receive the same request for the raw data, from which these proprietary equations could be derived. Many people are so bold as to ask for the actual proprietary equations themselves incorrectly believing they have the right to that knowledge. When they ask what the proprietary equations are-we simply tell them- they’re proprietary.

During the Artificial Intelligence (AI) session at the same conference, one of the younger investigators in the audience asked IBM, Google, Apple and others-the panelists for the AI session - during the Q & A, why researchers should share their raw data with these corporations, who will then use the data to produce patented products. Products, which the researchers-and others-will then have to pay to use? The original researcher does the work, while these corporations and BigPharma, will take that research for the purpose of making money. The original researcher will no longer be able to afford to use their own research because it will be legally owned by these corporations; corporations who did none of the original work.

For research studies not including potential intellectual property we would agree with the potential availability of raw data. The first author has provided such data on multiple occasions-e.g. dietary research NCT00324545-to assist with a better understanding of the impact of certain diets.

The next question becomes whom such data should be made available to. Just because someone owns a computer and can run analytic programs does not mean they are qualified to analyze, interpret or discuss the data. As was true during the time of Nobel, there are many who believe-particularly during this era of social media-that their voice should be heard in the discussion of topics, which they-many times-have a vested financial interest in. Science is not elevated by such discussions and progress is retarded through such financial interests.

That being said-we agree with the release of raw data under circumstances where legitimate scientists and physician-scientists are asking to see the data for the purpose of understanding outcomes. We also agree with the release of non-IP data to appropriate journals and conferences, so the journals and conferences can determine research validity-all of which should occur prior to the conference or publication, to address any legitimate questions or concerns.

However, we do not agree with the release of IP data absent NDA and contractual agreements-and then believe such IP needs to be carefully protected from contamination and abuse [8] by individuals and corporations, whose motivations are not patient driven. Something guaranteed under Article I, Section 8, Clause 8 of the U.S. Constitution.

FMTVDM is a utility patent 9566037, issued to Dr. Fleming on 2-14-2017. Figure reproduced with expressed consent of first author.


FMTVDM is a utility patent issued to first author. Authors MR Fleming, TK Chaudhuri have no disclosures.

  1. Wang F, Kaushal R, Khullar D (2019) Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine? Ann Intern Med. Link:
  2. (2017) The Fleming Method for Tissue and Vascular Differentiation and Metabolism (FMTVDM) using same state single or sequential quantification comparisons. Patent Number 9566037. Link:
  3. Fleming RM, Fleming MR, Chaudhuri TK, McKusick A (2019) Machine Learning through FMTVDM Proprietary QCA Equations. J Angiol Vasc Surg 4: 026.
  4. Fleming RM, Fleming MR, Dooley WC, Chaudhuri TK (2019) From Coronary Arteriography to Stenosis Flow Reserve to FMTVDM. The Sequential Evolution of Artificial Intelligence in Cardiology and Oncology – Removing the Human Error Element. Acta Med Sci Med Sci 114-118. Link:
  5. Fleming RM, Fleming MR, Dooley WC, McKusick A, Chaudhuri T (2018) FMTVDM©℗ Provides the First Nuclear Quantitative Method for Nuclear Cardiology and Introduces a New Era for Nuclear Cardiology. J Nucl Card 25: 1453.
  6. Fleming RM, Fleming MR, Chaudhuri TK (2019) True AI Implementation Through FMTVDM Proprietary Equations and QCA. Biomed J Sci & Tech Res 20: 15154-15160.
  7. Fleming RM, Fleming MR, Chaudhuri TK (2019) Replacing Cardiovascular Risk Factors with True AI and Absolute Quantifiable Measurement (FMTVDM) of Coronary Artery Disease. Int J Res Studies Med & Health Sci 4: 11-13. Link:
  8. Fleming RM, Chaudhuri TK, Harrington GM (2020) The Statistical Analysis of Data Validity as Acknowledged by ORI. How Three Statisticians Validated Original Data and Exposed Plagiarism by a Public Defender. The Implications for all Research and Article III Courts. Biomed J Sci & Tech Reports 24: 18254-18272. Link:
© 2020 Fleming RM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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