Software Architect: Hyunsuk Frank Roh, MD


Publication

  -  1st author

  -  Protocols.io

  -  Acknowledged

The asterisk (*) denotes his corresponding authorship.






Contents

- Infrastructural Aspects of Software Component (in association with Device)

   (1) Device Interface
   (2) Waveform Analyses
   (3) Hemodynamics
   (4) Medical Statistics
   (5) Machine Learning

The idealism of a hemodynamic software

The complexity of hemodynamic models has prevented clinicians from getting the insights out of the models when relating the clinical issues with the hemodynamic model. Visualization is the most persuasive way to illustrate a hemodynamic equation, and simulation is needed to visualize how the equation changes upon the manipulation of the coefficient of equations. Thus, the success of the hemodynamic software depends on how easy it is to work with visualizing the hemodynamic model and how effective it is for clinicians to draw insights from the models.

Additionally, it would be better if the following conditions are fulfilled: -1) an engineer takes care of the CPU time and memory management when combining and implementing numerous hemodynamic models published so far; -2) the simulation software provides an alternative interface other than GUI, which could enable experts to work more flexibly with the hemodynamic model; -3) components such as device interface, medical statistics, and artificial intelligence are coherently integrated in order to facilitate hemodynamic research.

Infrastructural aspects of each component

Each component will be the basis upon which other components can be built. This circulative data flow in the architecture diagram will eventually contribute to the development of other components synergistically. In other words, when considering the final overall goal of this software project as facilitating the data flow according to the software architecture, one part of the development will benefit the other part of the research.

The hemodynamic workbench software will be implemented to provide the following infrastructural functionalities: (1) To receive signals from the hemodynamic instrument; (2) To extract necessary information by wavelet analyses; (3) To understand the data according to the hemodynamic model and simulation; (4) To provide medical statistics; (5) To perform an action by reinforcement of the learning process.

Why the thoracic cavity for hemodynamic software and robotic surgery?

The thoracic cavity is intriguing in regards to its demanding physiological and computational potential. It is physiologically intriguing how the lungs and the heart are directly governed by the laws of physics: the hemodynamics during blood circulation and respiration with relation to auscultation, electrocardiography, ECMO and anesthetic machines. Computationally, a kernel-level device driver and Bayesian-based machine learning algorithm can be employed for (1) monitoring of the states of the thoracic organs, (2) computer-assisted hemodynamic modeling and simulation, and (3) machine learning for information processing. In addition, the thoracic cavity is ideal for a specialty that sits on the cusp between surgery and engineering to perform intellectually and technically challenging surgical robotic R&D projects on the organs encased by bones, which are best accessed and manipulated by a thin robotic hand instrument with ergonomic advantages. This will widen the indication of robotic cardiovascular surgery with new surgical procedures that integrate various additional hemodynamic devices and computational support.

"Surgeons must progress beyond the traditional techniques of cutting and sewing that have been their province since surgeons were barbers to a future in which approaches involving minimal access to the abdominal cavity are only the beginning." - Pappas et al. (2004) N Engl J Med.


(1) Device Interface index

Device driver interface component will enable the software to access raw data directly from a device. Biomedical companies seem to welcome the idea of enabling third parties to write software for their devices, which is exemplified by 3M providing an SDK (Software Development Kit) to allow people to write software for its Bluetooth stethoscope. However, my ultimate goal will be to make one step further by implementing the kernel-level device driver that would connect devices more fundamentally (as compared to existing SDK) and, therefore, to establish an integrative and flexible hemodynamic workbench.
    Some EKG classification articles (Lee, 2013) (Lihuang, 2010) relied exclusively on the MIT-BIH arrhythmia database or the standard test material to evaluate their arrhythmia detection algorithms. However, to the best of our knowledge, the difficulty of acquiring additional new raw EKG dataset due to the absence of open-source device interface for EKG instrument may be at least partially attributed to those researchers's having to work exclusively on MIT-BIH arrhythmia database. Therefore, if this software can receive the EKG raw stream over a WiFi or USB connection from instruments, future engineers can acquire additional test materials by collecting further raw EKG data alongside with corresponding EKG diagnoses, directly.
    Nonetheless, companies would be cautious about opening their device protocols for my implementing the kernel-level device interface, since doing so might change the company's marketing strategies and policies. Therefore, continuous improvement of Project nGene.org® in the long-term to gain agreement concerning its clinical pragmatism and to embrace clinicians' needs by providing an easy-to-write environment for their own scripts will have to be prioritized over this kernel component.


(2) Waveform Analyses index

"(2) Waveform Analyses" component pre-processes the raw wavelet data directly from the devices via the "(1) Device interface" component. In order to handle the raw wavelet dataset, such as EKG, lung and heart sounds, etc., two core algorithms have been chosen to be common denominating features: Independent Component Analysis (ICA) separates the mixed wavelets, whereas Support Vector Machine (SVM) classifies things after being trained.
    Its benefit can be illustrated by how this feature may change the existing flow. These machine-learning components can be used tentatively, until a more precise implementation of the classification for wavelets is implemented later in the point of time. For example, machine-learning algorithms for classifying EKG would be no match for a manually-written conditional statements implemented according to the Sokolow-Lyon Criteria for left ventricular hypertrophy (LVH) (Sokolow, 1949), as it would be nonsensical for training SVM to distinguish whether the summation of the S wave in V1 and the R wave in V5 or V6 is greater than, specifically, 35mm or not for LVH. However, until the manually-implemented code is developed according to certain criteria, it may be better to employ machine-learning features to accommodate wavelets in order to accelerate research and development in the meanwhile.
    For an example of embedding this software into the educational CPR kit mentioned above, the AED (Automated External Defibrillation) algorithm requires distinguishing normal EKG from various arrhythmia cases. However, since the MIT-BIH "arrhythmia" database does not have normal EKG dataset, the "(1) Device Interface" component can be used to collect a normal EKG raw dataset. Once normal EKG data with diagnoses are accumulated, then the SVM algorithm can be trained to classify whether it should be defibrillated, synchronized cardioversion, non-shockable, and normal, until the development of a more accurate manually-programmed classifying algorithm.


(3) Hemodynamics index

Project nGene.org® intends to facilitate research on the hemodynamic model, not only to better understand the physiology, and but also to gain further insights into improving the model. There are numerous equations published already and in the future and it may be too late if we just wait for the echocardiography manufacturing engineer to implement the module for the equation we need. Unless it is open-sourced, it cannot possibly follow the speed of insights during research. Yale Neuron is open-sourced with GUI for simulating neuron network; however, in my opinion, no matter how flexibly a software architect may implement its GUI, it cannot be on a par with the flexibility and creativity of new equations and insights of clinicians in the future.
    Therefore, Project nGene.org® tries to circumvent this problem by integrating R script so that clinicians can add their equations to test those features during echocardiographic measurements on the flies. At the same time, I believe that the success of earning popularity depends on how easy and generic it is for clinicians to add and modify the source code. Since clinicians do not have time to spend on learning, it is very important to make it very intuitive to make them willing to invest their time. I think that clinicians will invest their time only if they can get it intuitively.


(4) Medical Statistics & (5) Machine Learning index

"(4) Medical Statistics" is something that I do, not as a destination, but as a necessary step. To put it straightforwardly, the ultimate goal is "(5) Machine Learning". "(5) Machine Learning" component is pushed back on the priority list in the Masterplan Chart, because the software is designed to provide the following different types of dataset for the machine-learning algorithms: (i) Directly from hardware via the kernel program part, "(1) Device Interface"; (ii) Indirectly processing the wavelets raw data from instruments, "(2) Waveform Analyses"; (iii) Parsing and processing articles, especially meta-analysis and survival curve data, "(4) Medical Statistics", via a semantic web.
    The semantic web is a very suitable piece for medicine due to several reasons: (1) It is very flexible to integrate other semantic webs together, such that it can be used as a knowledge database with numerical information. (2) This numerical information with a network form can be fed into Bayesian-based machine learning. (3) Meta-Analysis is one of the forms of very specialized information that are available in the domain of medicine, and getting the hazard ratio from the survival curve for meta-analysis was, in my opinion, the most difficult methodology and the most challenging technical barrier when building a semantic web database.




Software Architecture (The 2024 Edition)

Project nGene.org®, a distinctive hemodynamic software, is notably unique because its architecture is designed by an individual who is both an engineer and a doctor. This dual expertise in hemodynamics and device driver programming aims to reduce the intercommunication complexity discussed in Fred Brooks' "The Mythical Man-Month". Project nGene.org® is an integrative, interdisciplinary hemodynamic platform packaging foundational technologies for public use, aiming to accelerate advancements in the clinical and industrial hemodynamic fields through simplicity and affordability, inspired by "The Innovator's Prescription" by Clayton M. Christensen et al.

The software project is meticulously crafted, with each component acting as a foundational pillar for subsequent innovations, establishing a circular data flow within its architectural framework. This methodology is anticipated to synergistically propel the evolution of the platform's elements. The project's paramount objective is to refine data circulation to mirror its architectural blueprint, ensuring that progress in one domain reciprocally amplifies research endeavors across the board. The hemodynamic workbench software is poised to offer essential functionalities: (1) capturing signals from hemodynamic instruments, (2) distilling vital information via wavelet analyses, (3) decoding data through hemodynamic models and simulations, (4) compiling medical statistics, and (5) executing actions based on a reinforcement learning algorithm.

Implementing the software marks the recrystallization of his professional journey, serving as a compass to navigate his career. This endeavor will not only guide him towards new horizons but also enrich his understanding for further development, ultimately fulfilling his life's purpose and enhancing his sense of satisfaction.


Why the thoracic cavity for hemodynamic software and robotic surgery?

The thoracic cavity is intriguing due to its significant physiological and computational potential. It is fascinating how the lungs and the heart are directly governed by the laws of physics, evident in hemodynamics during blood circulation and gas exchange during respiration, as well as in relation to auscultation, echocardiography, ventilator, ECMO, and the use of anesthetic machines. From a computational perspective, a kernel-level device driver and machine learning algorithms can be utilized for: (1) monitoring the states of the thoracic organs, (2) assisting in hemodynamic modeling and simulation through computers, (3) enhancing information processing through machine learning, and (4) conducting robotic surgery based on deep learning from thousands of surgeries performed by human doctors, with the aim of eventually performing surgeries autonomously in lieu of human practitioners. Furthermore, the thoracic cavity presents an ideal opportunity for a specialty that bridges surgery and engineering, facilitating intellectually and technically challenging surgical robotic R&D projects focused on organs surrounded by bone. These projects can benefit from the use of slender robotic hand instruments designed for their ergonomic advantages. Such innovations will expand the scope of robotic cardiovascular surgery by introducing new surgical procedures that incorporate various additional hemodynamic devices and computational support.

"Surgeons must progress beyond the traditional techniques of cutting and sewing that have been their province since surgeons were barbers to a future in which approaches involving minimal access to the abdominal cavity are only the beginning." - Pappas et al. (2004) N Engl J Med.


(1) Device Interface index

(2) Waveform Analyses index

(3) Hemodynamics index

Drawing upon the concepts presented in "Computational Thinking" by Peter J. Denning and Matti Tedre, the value of modeling and simulation within the scientific community has become increasingly evident. Within the realm of hemodynamics, these tools have transcended their initial roles to become central pillars in the study and treatment of cardiovascular conditions. Their application enables a detailed and dynamic investigation into the cardiovascular system, allowing for the visualization and analysis of how blood flow and pressure respond to a myriad of physiological changes. This granular view is indispensable for accurately predicting the effects of alterations within the cardiovascular system, providing a critical foundation for the diagnosis and development of effective treatments for heart diseases. Moreover, through simulation, the practical application of theoretical models is realized, facilitating the safe, efficient, and risk-free testing of medical hypotheses and the optimization of surgical and non-surgical interventions.

The profound impact of modeling and simulation is perhaps most evident in addressing the complex challenges presented by congenital heart defects (CHD) and pulmonary arterial hypertension (PAH). PAH, in particular, poses a daunting physiological challenge within CHD management, necessitating specialized, highly precise treatments. The traditional approach to surgical intervention, fraught with significant risks especially for neonatal patients, underscores the urgent need for non-invasive, simulation-driven methodologies. The development of advanced modeling and simulation techniques, as demonstrated by Project nGene.org®, offers a promising avenue towards safer, more effective treatment options. By simulating the specific cardiovascular conditions associated with CHD and PAH, Project nGene.org® provides an innovative framework for deciphering the intricate factors that influence patient outcomes, significantly improving the potential for successful treatment while minimizing risk.

The initiative to harness the potential of PAH hemodynamic modeling and simulation as a cornerstone in the development of neonatal CHD surgery simulations aims not only to refine the understanding and management of PAH within the context of CHD but also to pioneer a path towards the simulation-based planning and execution of surgical interventions. The goal is to create a highly accurate, risk-free environment where surgical strategies can be tested and refined, ensuring the highest level of safety and efficacy in neonatal CHD treatments. Through the integration of these computational techniques, the field of hemodynamics is set to significantly advance, highlighting the critical role of modeling and simulation in transforming cardiovascular medicine and patient care.

(4) Medical Statistics & (5) Machine Learning index

Integrating "(4) Medical Statistics" into my work is not merely a destination but a vital step towards a broader objective: mastering "(5) Machine Learning". This component is strategically deferred in the Masterplan Chart, as the software is intricately designed to curate diverse datasets for machine learning algorithms through various means: (i) directly from hardware via the kernel in the "(1) Device Interface"; (ii) by processing raw wavelet data from instruments in "(2) Waveform Analyses"; and (iii) by parsing and analyzing medical literature, particularly meta-analyses and survival curve data, through "(4) Medical Statistics", utilizing a semantic web (or Web 3.0) approach. Initially, the semantic web seemed perfectly aligned with medical applications for several reasons: (1) Its inherent flexibility facilitates the integration of multiple semantic webs, creating a comprehensive knowledge database enriched with numerical data. (2) This numerically dense network is ideal for Bayesian-based machine learning applications. (3) Specifically, meta-analysis represents a form of highly specialized information within the medical domain, where deriving hazard ratios from survival curves posed a significant technical challenge and a methodological bottleneck in developing a semantic web database.

However, the rapid evolution of machine learning algorithms necessitated a shift in methodological approach. Acknowledging the advancements in deep neural networks and linear algebra techniques, especially Singular Value Decomposition (SVD), these methods now appear more apt for these objectives. This change in methodology is driven by the emerging efficiencies and capabilities of these algorithms in machine learning, signifying a pivotal adaptation to the evolving landscape of data analysis. This recalibration of approach, moving from a Bayesian-based semantic web to emphasizing deep learning and SVD, reflects a commitment to leveraging the most effective and advanced methodologies available in the field of machine learning. It underlines readiness to adapt and evolve in response to the dynamic nature of technological advancement and the continuous quest for more refined and powerful analytical tools.

The reconsideration of Bayesian algorithms also draws from a historical challenge in the field of artificial intelligence. Despite the Bayesian approach's flexibility and appeal, its application is marred by complexity in calculations beyond simple, restrictive assumptions. This complexity often necessitates approximation methods or sampling, which, while practical, diverge from dealing with the real posterior distribution directly. Further complicating the landscape was the neural network's initial inability to solve the exclusive OR (XOR) problem, a straightforward task achievable with basic digital logic gates but unattainable by a single-layer perceptron. Although it was known that multi-layer perceptrons could theoretically execute such tasks, the lack of effective training methods led to significant disillusionment and a temporary retreat from neural network research. This historical bottleneck highlights the limitations of early machine learning approaches and underlines the strategic pivot towards more advanced and capable methodologies, such as deep learning, that have since overcome these early challenges. (On February 5th, 2024, this segment of the software architecture underwent a revision to include sophisticated deep learning and SVD techniques.)







Relevant Books

Artificial Intelligence

In Ethem Alpaydin's "Machine Learning," while machine learning enables systems to adapt and learn from data in dynamic environments, artificial intelligence encompasses the broader capacity for systems to perform tasks requiring human-like intelligence, including but not limited to learning.

  -   A Perspective from 'AI Assistants' by Roberto Pieraccini

  -   A Perspective on the Evolution of 'Recommendation Engines' by Michael Schrage

  -   A Perspective from 'The Technological Singularity' by Murray Shanahan

  -   My Reflections on 'Computational Thinking' and the AI Revolution

  -   A.I. vs. Doctors in ElectroCardioGram (ECG)

  -   A.I. Engine

  -   In-Database Machine Learning




'AI Ethics' by Mark Coeckelbergh

  -   Exploring AI raises profound questions about our knowledge, society, and ethics, across several key domains:

↓ This content is not sourced from the book "AI Ethics." ↓


  -   Perspectives on Privacy Protection for Data Subjects (primarily derived from the Book: Data Science by Kelleher et al.)

  1. Collection Limitation: Personal data collection should be restricted and conducted lawfully and fairly. Where possible, it should be done with the data subject's knowledge or consent.
  2. Data Quality: Data must be pertinent to its intended use and maintained accurately, completely, and up-to-date as necessary.
  3. Purpose Specification: The reasons for collecting personal data should be clearly defined at the time of collection. Use of the data should be confined to these specified purposes or those compatible with them, with any change of purpose explicitly stated.
  4. Use Limitation: Personal data should not be used or disclosed for purposes other than those specified, except with the subject's consent or under the authority of law.
  5. Security Safeguards: Reasonable security measures must be in place to protect personal data from risks like loss, unauthorized access, or misuse.
  6. Openness: There should be a policy of transparency regarding practices and policies related to personal data. Information about data collection and usage, as well as details about the data controller, should be easily accessible.
  7. Individual Participation: Individuals should have the right to confirm if a data controller has their personal data, access their data in a timely and reasonable manner, and challenge or appeal any refusal to grant access. They should also be able to contest the accuracy of their data and have it corrected or amended as needed.
  8. Accountability: Data controllers must be accountable for adhering to these principles, ensuring compliance with the appropriate measures.

  -   Computational Approaches to Preserve Privacy (Data Science by Kelleher et al.)

  -   A Perspective from 'AI Assistants' by Roberto Pieraccini on the Impact of GDPR and Federated Learning




'Virtual Reality' by Samuel Greengard

- An Overview of Extended Reality (XR)

- Challenges and Solutions in Extended Reality (XR)

↓ In resonance with the themes explored in Samuel Greengard's book 'Virtual Reality,' this discussion presents my independent insights and perspective. ↓


- Exploring the Synergy of 3D Glasses, XR, and Hinduism in 'Avatar'

- 'Ready Player One' and the Inspiration Behind VR Innovation

- The Matrix: VR and the Realm of Simulated Reality

- Exploring AR and MR Technologies in 'Minority Report'

- Tron: The 1982 Odyssey into Digital Universes and the Dawn of Virtual Gaming

- The Convergence of VR and Reality in 'Tron: Legacy'

- From BOTW to TOTK: The Impact of 'The Legend of Zelda' on VR Gaming

- My Reflections on 'Spatial Computing': Shaping the Future of Healthcare and Mixed Reality





'Intellectual Property Strategy' by John Palfrey

Regardless of the industry, there's a need for a more flexible and expansive approach to intellectual property than previous generations adopted. Intellectual property laws are undergoing rapid transformations globally, affecting copyrights, patents, and trademarks alike. The most significant shifts are evident in the strategic thinking of business leaders regarding intellectual property, showcasing a dramatic evolution in just the last ten to twenty years.

  -   A Paradigm Shift in Collaborative Development (in the Web 2.0 Era)

↓ In alignment with the concepts explored in 'Intellectual Property Strategy', the following discussion offers my own independent insights and a perspective that resonates with the themes of the book. ↓


  -   Navigating the Digital Evolution From Web 1.0 to 4.0

  -   IP Strategy for the Symbiotic Web Era (Web 4.0): A Personal Perspective

  -   The Impact of Creative Priorities on Artistic Work and IP Strategies in the Digital Age: A Personal Perspective

  -   Balancing Open Innovation and Strategic Protection: A Personal Perspective




'Cloud Computing' by Nayan B. Ruparelia

↓ The information provided does not originate from the book "Cloud Computing," but it has been supplemented with relevant information. ↓


  -   Privacy Enhanced Through the Power of On-Device AI in Mobile Devices




'The Internet of Things' by Samuel Greengard







Confluence of Art, Literature, and Religion

Ghost in The Shell (1995)

  -   A 2023 Perspective on the Dawn of an Advanced AI Era

↓ The content presented below is not derived from 'Ghost in the Shell'; instead, it provides relevant comparative or supplementary perspectives related to the movie. ↓


  -   A Comparative Analysis of 'Ghost in the Shell' and 'Transcendence'

  -   Diverging Paths in Human-Machine Integration: Cyberpunk Edgerunners vs. Ghost in the Shell

  -   Memory and Embodiment in Blade Runner 2049: AI's Quest for Humanity

  -   Blade Runner (1982): Examining Humanity through Lifespan and Ambiguity




Battle Angel Alita (1993), the Manga

  -   Aspirational Echoes Between Illusion and Reality

  -   Conquering Karma Birthing Destined Chaos

  -   Brain, Freedom, and the Rudder of Life

  -   Alita's Judeo-Christian Allegory

  -   Alita's Ethical Odyssey for Humanity

↓ The following content, while not directly extracted from 'Battle Angel Alita', offers relevant additional insights or comparative analysis in relation to the Manga. ↓


  -   Cobb's Inception Warning and Ouroboros's Chaos in Alita's World

  -   2001: A Space Odyssey - Deciphering AI's Mythical Parallels with the Cyclops

  -   I, Robot: The Limits of the Three Laws in Safeguarding Humanity

  -   Ex Machina: The Paradox of AI Emancipation and the Prometheus Allegory

  -   Digital Quests and Ancient Myths: The Layered Symbolism of 'Ready Player One'




The Spectrum of Creativity: Originality and Emotional Connnection in Art

  In my own opinion, the creative priorities of artists can significantly influence their work. Some artists are driven by the pursuit of originality, eager to create something unprecedented and wary of being seen as copying past works. Their focus is on innovation and steering clear of criticism for lack of originality. On the other hand, there are artists whose primary goal is to connect emotionally with their audience. They might choose to work with more traditional techniques, mediums, or styles which, though seen by some as outdated, are deeply rooted in the collective psyche and thus more likely to strike a familiar chord with a larger audience. These time-honored methods are effective in evoking a shared emotional experience because they are embedded in the collective memory. Conversely, more modern and unfamiliar artistic expressions might not resonate as widely since they're not as well assimilated and might pose challenges for the audience in terms of relatability and comprehension.

  -   Stable Diffusion: Democratizing AI with an Open Source Model and Apple's Strategic Use of Metal and Core ML

  -   Traditional Arts vs. Artworks Illustrated through Deep Learning Techniques, Namely Stable Diffusion




Further Thoughts

  -   'Like Sunday, Like Rain': The Platonic Symphony of Eleanor and Reggie

  -   Revisiting 'The Lion King': A Tale of Courage, Confrontation, and Redemption







Current Topics and Trends

'The Innovator's Prescription' by Clayton M. M. Christensen

  -   "Disruptive"






'Crossing the Chasm' by Geoffrey A. Moore

  -   Understanding the "Chasm" in Technology Adoption

  -   Strategies to Cross the Chasm

↓ This content presents pertinent insights independently and does not form part of "Crossing the Chasm." ↓


  -   Insights from 'The 1-Page Marketing Plan'

  -   Insight and Influence through 'Everyday Business Storytelling'

  -   Insights from 'Information and The Modern Corporation'




'Crossing the Quality Chasm: A New Health System for the 21st Century' by Institute of Medicine et al.

  -   A Pre-2001 Perspective at the Time of Publication

  -   A Post-2001 Perspective Since its Publication




Digital Healthcare

  -   FirstDerm

  -   iStethoscope

  -   SpiroSmart

  -   Nymi

  -   Kardia Band, by AliveCor

  -   Apple Watch

Apple, (1) Measuring Data, (2) Gathering Data, and (3) Analyzing Data

  -   (2) Gathering Data

  -   (2-1) Apple Health Kit: Integrating Measurement Data

  -   (2-2) Apple Health Record: Integrating Clinical Data

Pear Therapeutics

  -   Norvatis

  -   Sandoz

Akili Interactive

  -   Merck and Amgen

Click Therapeutics

  -   Sanofi Ventures



  -   CRISPR Revolution in Genetics: From Nobel-Winning Breakthroughs to Legal Battles in Drug Patenting

  -   Exploring Genetic Treatment Options in Sickle Cell Disease

Genetic Treatment Pharmaceutical Company Price
LYFGENIA (lovotibeglogene autotemcel) bluebird bio, Inc. $3.1 million
CASGEVY (exagamglogene autotemcel) Vertex Pharmaceuticals and CRISPR Therapeutics Cheaper than LYFGENIA's $3.1 million

  -   Advancing Retinal Gene Therapy

Genetic Treatment Pharmaceutical Company Price
LUXTURNA (voretigene neparvovec-rzyl) Spark Therapeutics ~$0.85 million

  -   The Role of Bioinformatics and AI



In 2023: Robust investment activity in AI and machine learning sectors, despite a general VC funding downturn.

  -   Significant Impact of Generative AI

  -   Notable Mergers and Acquisitions in the Pharmaceutical Sector


In 2022: Despite a dip in overall M&A activity in early 2022, targeted acquisitions highlight a keen focus on innovation within wearable technology, genomics, and biosimilars.

  -   Wearable Technology and Genomic Testing

  -   Biosimilars and Rare Diseases


In 2021: Healthcare M&A returned to pre-pandemic trends with a focus on acquiring new capabilities, engaging in cross-border deals, and executing both carve-outs and full-company acquisitions.Record-high valuations were observed, with the median healthcare deal fetching 20 times forward-looking EBITDA, the highest level in decades.

  -   Expansion into New Therapeutic Areas

  -   Enhancements in Medical Equipment and Services

  -   Enhancing Customer Experience and Operational Efficiency Through AI

  -   Accelerating Drug Development and Healthcare Solutions

  -   Consulting Firms Bolstering AI Capabilities


Digital Healthcare









Visionary Considerations:

License Policy in the Long-Term Future

This software has been conceptualized and developed not as a final product, but with the intention to serve as a foundational infrastructure, providing a robust platform for external individuals to develop and implement their own programming scripts. Despite this, it is crucial to highlight that the current licensing policy for hosting and integrating third-party source code modifications on Project nGene.org® has been somewhat restrictive, a stance that stems from the project's unwavering commitment to achieving its long-term milestones and objectives. Managing and overseeing external contributions necessitates substantial resource allocation, and this could inadvertently lead to increased complexities and potential misunderstandings in internal communications. Moving forward, Project nGene.org® is dedicated to evolving and reaching a state of readiness to wholeheartedly welcome and support contributions from the clinical and engineering communities, subsequently leading to a revision and liberalization of our policies to foster a more open and inclusive environment for third-party contributions.


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Contact

Email: Support [AT] nGene.org

Call sign : K3CWKP (FCC) or DS1UHK (Emergency Radio Communication Support Corps)



Acknowledgment

Special thanks to my beloved mom who always trusts me. Were it not for her, it would be impossible for me to implement this software.