Vladsblood
Regular
APT kept losing 100’s of millions and it’s MC went to 46 billion in a little over 1 year…strange ! Vlad
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Great post
Euks
Regular
Hey AusEire,
Has the company actually grown???
In the last 2 end of year financials the company only grew from 66 to 69 employees. I have sent direct emails to Tony twice asking what the current head count is but received no reply! I asked at the AGM in question time 5 months ago and Sean said “I’m not sure the exact number but we’ve definitely grown” so I’m still not convinced. Obviously everything that we have been told suggests growth but to what expense? Are some branches of the company shrinking whilst others grow???
It’s a genuine question by the way. Does anybody here happen to know how many employees currently work for Brainchip?
Maybe somebody with some clout can email Tony and ask
Cheers
Euks
Frangipani
Regular
A DELLightful article about the impact AI at the edge is having in three different industries: retail, manufacturing and energy.
www.forbes.com
LEADERSHIP
Elisabeth Plutko
Brand Contributor
Dell Technologies
BRANDVOICE| Paid Program
Nov 14, 2023,06:03pm EST
AI at the edge is transforming retail, manufacturing and energy. Here’s how.
Across all industries, consumers demand tailored experiences. Here's how AI at the edge is making it possible.
GETTY
Without a doubt 2023 is the year of all things AI. Organizations across industries are being tasked with incorporating AI into their strategy and for good reason, AI is an innovation game changer with one caveat – any AI strategy will only be as good and impactful as the data that feeds it. Organizations should be asking themselves, “how do I get the right data to bring my AI strategy to life?”
The data that is best equipped to fuel personalization and convenience is the data that is captured at the point of creation, or in other words, the data that is captured at the edge. AI at the edge is the key to harnessing this valuable data. It processes information close to the source, minimizing latency and enabling real-time decision-making. It’s the data that, across all industries, is fueling cost optimization and the streamlining of processes. Let’s take a closer look at the impact AI at the edge is having in three different industries, retail, manufacturing and energy.
Imagine stepping into a retail store. The moment you cross the threshold, a chime welcomes you. As you navigate the aisles, the shelves seem to come alive, showcasing products that resonate with your tastes and needs. Even the in-store displays and digital signage offer recommendations tailored to you. This immersive and personalized experience is the essence of a smart store, a retail environment where data-driven technologies personalize the customers experience in physical retail spaces.
The opportunities to innovate with edge data in retail do not end there. By applying automation to shopping data, retailers can act with agility to maintain product availability and service levels during busier periods or unforeseen labor shortages. Meanwhile, customers receive what they need, when they need it. To learn more about the opportunities that AI at the edge is opening for retailers click here.
Picture a factory floor where machines run with orchestrated precision, humming with purpose, each optimally positioned and staffed for their role in the production process, materials flowing seamlessly, guided by an invisible hand. Production schedules adjust on the fly to optimize efficiency, and maintenance tasks are executed just when they're needed. This is the reality of a smart manufacturing facility. Here, data-driven technologies, real-time analysis, and AI at the edge have revolutionized the way products are made.
In the manufacturing world, where precision and efficiency are paramount, the concept of a smart factory has far-reaching implications. AI at the edge ensures machinery operates at peak efficiency. Edge devices monitor equipment performance, detect anomalies, and enable predictive maintenance. This not only reduces downtime but also leads to significant cost savings and increased productivity. Forward-thinking manufacturers are actively addressing challenges such as data silos, fragmented technologies and IT/OT communication. ‘Smart’ factories are pushing new boundaries in operational productivity, automation, employee safety and more.
Edge computing is reshaping how energy companies can maximize resources while reducing risk and environmental impact. The energy industry benefits from AI at the edge by creating smarter grids. From AI and machine learning to computer vision, energy suppliers are utilizing new methods to optimize output, ensure reliability, and reduce human intervention. Click here to learn more about the innovative opportunities unfolding within the energy sector.
Elisabeth Plutko
Elisabeth is an experienced marketing content strategy leader with 18 years of experience across the retail, technology and financial services industries. In her current role, Elisabeth leads the content strategy efforts for Dell Technologies’ edge portfolio. Elisabeth previously led the design and implementation of PNC Bank’s retail financial wellness strategy. Elisabeth holds a B.A. degree in Business Communication from Grove City College. Read Less
Dell Technologies BrandVoice: The Advantages Of AI At The Edge
Across all industries, consumers demand tailored experiences. Here's how AI at the edge is making it possible.
www.forbes.com
LEADERSHIP
The Advantages Of AI At The Edge
Elisabeth Plutko
Brand Contributor
Dell Technologies
BRANDVOICE| Paid Program
Nov 14, 2023,06:03pm EST
AI at the edge is transforming retail, manufacturing and energy. Here’s how.
Across all industries, consumers demand tailored experiences. Here's how AI at the edge is making it possible.
GETTY
Without a doubt 2023 is the year of all things AI. Organizations across industries are being tasked with incorporating AI into their strategy and for good reason, AI is an innovation game changer with one caveat – any AI strategy will only be as good and impactful as the data that feeds it. Organizations should be asking themselves, “how do I get the right data to bring my AI strategy to life?”
The Power of Good Data
In today's digital age, personalization and convenience are no longer a mere luxury; they’re an expectation. Across all industries, consumers demand tailored experiences. So, how do businesses deliver such a level of personalization? The answer lies in good data and its intelligent utilization.The data that is best equipped to fuel personalization and convenience is the data that is captured at the point of creation, or in other words, the data that is captured at the edge. AI at the edge is the key to harnessing this valuable data. It processes information close to the source, minimizing latency and enabling real-time decision-making. It’s the data that, across all industries, is fueling cost optimization and the streamlining of processes. Let’s take a closer look at the impact AI at the edge is having in three different industries, retail, manufacturing and energy.
Retail: A Convenient, Personalized In-store Shopping Experience
Customer data is a retailer’s most valuable resource. Edge solutions are revolutionizing how that data is collected, engaged with, and acted upon to create seamless new experiences. In the retail sector, AI at the edge is transforming how businesses connect with their customers. In-store sensors and cameras gather data on foot traffic and product interactions. This data informs personalized marketing strategies, improves inventory management, and enhances the overall shopping experience.Imagine stepping into a retail store. The moment you cross the threshold, a chime welcomes you. As you navigate the aisles, the shelves seem to come alive, showcasing products that resonate with your tastes and needs. Even the in-store displays and digital signage offer recommendations tailored to you. This immersive and personalized experience is the essence of a smart store, a retail environment where data-driven technologies personalize the customers experience in physical retail spaces.
The opportunities to innovate with edge data in retail do not end there. By applying automation to shopping data, retailers can act with agility to maintain product availability and service levels during busier periods or unforeseen labor shortages. Meanwhile, customers receive what they need, when they need it. To learn more about the opportunities that AI at the edge is opening for retailers click here.
Manufacturing: Optimizing Efficiency
In a sector where productivity is a key differentiator, AI at the edge is redefining what today’s manufacturers can accomplish.Picture a factory floor where machines run with orchestrated precision, humming with purpose, each optimally positioned and staffed for their role in the production process, materials flowing seamlessly, guided by an invisible hand. Production schedules adjust on the fly to optimize efficiency, and maintenance tasks are executed just when they're needed. This is the reality of a smart manufacturing facility. Here, data-driven technologies, real-time analysis, and AI at the edge have revolutionized the way products are made.
In the manufacturing world, where precision and efficiency are paramount, the concept of a smart factory has far-reaching implications. AI at the edge ensures machinery operates at peak efficiency. Edge devices monitor equipment performance, detect anomalies, and enable predictive maintenance. This not only reduces downtime but also leads to significant cost savings and increased productivity. Forward-thinking manufacturers are actively addressing challenges such as data silos, fragmented technologies and IT/OT communication. ‘Smart’ factories are pushing new boundaries in operational productivity, automation, employee safety and more.
Energy: A Smarter Grid
The impacts of AI at the edge extend into the energy sector as well. Envision a power grid that reacts in real-time to the energy needs of homes, businesses, and industries. As the sun sets and solar panels become less effective, the grid seamlessly shifts to other sources of energy, ensuring a constant and reliable power supply. Electric vehicle charging stations operate efficiently, adjusting to peak demand hours, minimizing energy waste. Smart appliances can be dynamically controlled to run at staggered intervals through the night, generating only the necessary demand for the power grid and ensuring that, come morning, your dishes are clean and your clothes are washed. This is the reality of a smart energy grid. The concept of a smart energy grid illustrates how real-time data analysis and AI at the edge are transforming the way we generate, distribute, and consume energy, with implications that extend beyond the energy industry into our daily lives and a more sustainable future.Edge computing is reshaping how energy companies can maximize resources while reducing risk and environmental impact. The energy industry benefits from AI at the edge by creating smarter grids. From AI and machine learning to computer vision, energy suppliers are utilizing new methods to optimize output, ensure reliability, and reduce human intervention. Click here to learn more about the innovative opportunities unfolding within the energy sector.
Transforming Your Organization’s Future with AI at the Edge
As organizations in retail, manufacturing, and energy continue to adopt emerging technology, the bar for customer expectations rises even higher. The key to meeting those expectations is good data, and the place to find good data let alone make sense of it is by applying AI at the edge. By harnessing the power of AI at the edge, your organization can not only meet but exceed consumer expectations and accelerate business growth and success as ideas become innovative realities.
Elisabeth Plutko
Elisabeth is an experienced marketing content strategy leader with 18 years of experience across the retail, technology and financial services industries. In her current role, Elisabeth leads the content strategy efforts for Dell Technologies’ edge portfolio. Elisabeth previously led the design and implementation of PNC Bank’s retail financial wellness strategy. Elisabeth holds a B.A. degree in Business Communication from Grove City College. Read Less
Frangipani
Regular
jtardif999
Regular
Is this a deliberate stir on your part or are you showing yourself to be a tad ignorant? Your comment is just silly in the context of what is understood about the time line to product royalties.. ‘no one is using Akida’ ? No one as yet (that we are aware of) has bought a product containing Akida IP. Our two licencees Renesas and Megachips have not officially released products, maybe soon? Megachips has a number of sublicensees that haven’t declared products yet. But hang on, we also potentially have Merc, Valeo, VVDN and Socionext going into production runs for products being released later this year or the next that probably contain Akida IP/Akida1500, ..but as you say the last quarterly shows that no one is currently using Akida
so that’s a bit shocking isn’t it!
Tothemoon24
Top 20
Morning Tothemoon & Fellow Chippers,
Just having a quick listen to the above podcast again , noticed that Zac S . Mentioned that HP Polygon??.... about 6:43 min in , have started production run for a VR / AR headset .
* Had a look on google and found this company which HP Inc. Bought out last year for $40.00 per share or whole company for $3,300,000,000.00. ( I'd imagine this is USD ).
Chip + Prophesee + HP Inc. ????
* possibly my over active imagination with a mild case of delirium setting in.
.
Product hitting consumer market shortly ????
Regards,
Esq.
Just having a quick listen to the above podcast again , noticed that Zac S . Mentioned that HP Polygon??.... about 6:43 min in , have started production run for a VR / AR headset .
* Had a look on google and found this company which HP Inc. Bought out last year for $40.00 per share or whole company for $3,300,000,000.00. ( I'd imagine this is USD ).
Chip + Prophesee + HP Inc. ????* possibly my over active imagination with a mild case of delirium setting in.
. Product hitting consumer market shortly ????
Regards,
Esq.
Last edited:
Tothemoon24
Top 20
Exciting News! 
We are thrilled to announce the grand gathering of over 1000 valued customers across 27 countries worldwide for INVENT Innovation Valeo Event!
INVENT marks a groundbreaking moment for Valeo, as we introduce a revolutionary phygital experience. This event provides our IAM aftermarket customers with an early look at our innovative products and technologies set to launch in 2024.
Over the next 3 weeks, from Italy to Poland, China to France, Brazil to Turkey, and beyond, we're excited to come together with our partners to unveil the latest Valeo innovations. As a global leader in Electrification and ADAS, Valeo is sharing key trends in the automotive industry as well as revealing tailored initiatives for each local market, fueling collaborative business development in the upcoming year. ☀
Consider this just a teaser... It starts tomorrow in Bologna, Italy. Get ready for an exhilarating journey of innovation!
Tothemoon24
Top 20
Pom down under
Regular
Frangipani
Regular
Do you or does anyone you know suffer from a rare disease? Then you will surely be interested in finding out more about how the implementation of AI can become a gamechanger in the field of so-called orphan disease research, from diagnostics and monitoring of disease progression to therapeutic strategies and personalised drug development.
I was reminded about an article by Italian researchers titled The Impact of Artificial Intelligence in the Odyssey of Rare Diseases that I had read a while ago, when I noticed Anil Mankar liking this post:
I am not sure whether there is any connection between this company and Brainchip or whether our co-founder simply wanted to show his appreciation for a bunch of people wanting to “make a meaningful impact in changing the lives of patients living with severe, complex diseases” - for all we know, his life or that of his loved ones or friends could be affected.
Companies such as Probably Genetic may not necessarily need neuromorphic technology when implementing AI in order to help patients suffering from rare diseases, but it would be wonderful if Brainchip was somehow involved in contributing to such a worthy cause.
www.mdpi.com
Anna Visibelli
1,*,†
,
Bianca Roncaglia
1,†,
Ottavia Spiga
1,2,3,*
and
Annalisa Santucci
1,2,3
1
Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
2
Competence Center ARTES 4.0, 53100 Siena, Italy
3
SienabioACTIVE—SbA, 53100 Siena, Italy
*
Authors to whom correspondence should be addressed.
†
These authors have contributed equally to this work.
Biomedicines 2023, 11(3), 887; https://doi.org/10.3390/biomedicines11030887
Received: 7 February 2023 / Revised: 28 February 2023 / Accepted: 8 March 2023 / Published: 13 March 2023
(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)
Download keyboard_arrow_down
Browse Figure
Versions Notes
Keywords:
rare disease; machine learning; artificial intelligence; precision medicine; data analysis
First, the process of diagnosing a rare disease is often long and exhausting. In 25% of patients, it takes between 5 and 30 years after disease onset to receive a correct diagnosis, which requires the participation of a competent and comprehensive clinical team [9]. A survey conducted from October 2019 through March 2020 by the National Organization for Rare Disorders [10] on 1108 individuals found that 50% of patients and caregivers attribute diagnostic delays to a lack of knowledge about the disease, while 42% believe that delays are caused by limited medical specialization. Many patients identified the problem of doctors not being able to link symptoms, particularly between different organ systems, in addition to the fact that waiting times to consult specialists are long and there would be a need for more tests. A diagnostic delay can have tremendous effects on the patient’s clinical picture, so prompt and accurate diagnosis are the starting point for being able to find therapeutic interventions and resources that can ensure a good clinical outcome [11].
What complicates the rare disease odyssey is that the diagnosis is never the end of the journey, since even from a prognostic and therapeutic point of view, there are huge gaps to be filled [12]. The difficulties encountered at the prognostic level are related to the lack of valid parameters and/or biomarkers, since the molecular pathophysiological mechanisms are still largely unknown. Moreover, the small number of patients does not allow statistically significant parameters to be derived [13]. Thus, the prognosis of patients may change depending on various genetic and environmental factors, but it is complicated to arrive at standards of care for treatment and rehabilitation because health research is necessarily conducted on a small scale and cannot be based on evidence or experience [14]. Conventionally, it takes 10 to 15 years to bring a drug to market, with an average R&D cost of $2.6 billion [15]. These two factors represent a bottleneck in the drug discovery pipeline for rare disorders, as research costs are high while revenues are low due to the small number of patients. This implies that the development of new drugs and treatments can be time-consuming and hindered by the lack of data and funding [16]. The heterogeneous patient populations, often unknown etiology and pathogenesis, the timing of disease progression, and the lack of exhaustive clinical studies make the search for specific drugs very difficult [17]. The key problems related to the development of drugs and therapies for rare diseases are [13]:
Possible innovative answers to biomedical and clinical challenges come from the world of information technology, and a striking example has been the fight against COVID-19. Since the beginning of the pandemic, artificial intelligence (AI) has played a crucial role in the battle against the virus, and several methods have been applied for various purposes [18]. Machine learning (ML) and deep learning (DL) models have been used for the early detection and diagnosis of COVID-19 by monitoring the demographic, clinical, and epidemiological characteristics of patients, and for developing diagnostic tools that can quickly analyze CT scans and X-rays to identify patterns indicative of the disease [19]. AI has also been used to predict patient vulnerability, in order to administer appropriate drugs and treatments [20], as well as being decisive in accelerating the discovery of potential vaccines. Similarly, AI has been an essential ally for public health policies in contact tracing, monitoring the spread of the virus, and creating predictive models that have helped to identify potential outbreaks. Thus, it is clear how AI is increasingly coming to the aid of physicians at every stage of disease management, to evaluate the efficacy of medical treatments or deeply investigate the correlation between patients and treatments according to their own molecular characteristics. The precision medicine approach is widely applied to the healthcare area, in particular to rare diseases with the creation of patient registries leveraging large amounts of data to discover potential links. It is a comprehensive and prospective approach to prevention, diagnosis, treatment, and monitoring, built on the genetic characteristics of the individual. Harmonizing databases and including registries are the major facilitators to understand the complexity of diseases, to conduct clinical trials, to improve the drug development process, and to assign the right treatment to the right individual after reliable patient stratification. AI is an ally that can integrate and analyze heterogeneous data (e.g., multi-omics data as well as images). However, first-generation AI systems, which rely on the development of algorithms for diagnosis and treatment that are trained on big data, are not always adequate to meet the needs of rare diseases. Data scarcity and sparsity characterize these disorders, due to fragmented knowledge and the limited number of data and specimens available [21]. Phenotype and disease severity as well as pharmacogenomic and pharmacokinetic factors are the elements on which successful diagnosis and treatment depend [13]. Diagnostic decision support systems (DDSS) already exist, i.e., expert systems that support doctors in facilitating the diagnostic process by incorporating medical knowledge. These systems have been proven effective and have improved clinical diagnosis by compiling lists of appropriate differential diagnoses for a given sample of tests [22,23]. For rare diseases, these systems need to be implemented. Networks and registries have been built to bring together data and expertise on rare diseases, making them free, accessible, and shareable worldwide. One example is Orphanet, which over the past 20 years has become the go-to source for information on rare diseases, facilitating access to information and means to identify potential patients, and contributing to the development and sharing of knowledge. Other available datasets are the Online Mendelian Inheritance in Man and Human Phenotype Ontology. The knowledge deposited in these databases is used by DDSSs built for rare disorders, some examples of which are FindZebra [24], PhenoTips [25], Rare Disease Discovery [26], and Ada DX [27].
Second-generation AI systems were designed to fill the diagnostic, prognostic, and therapeutic gaps that must be overcome to achieve patient-centricity for patients with rare diseases [28] (Figure 1). These systems use a personalized closed-loop system designed to enhance end-organ function, overcome problems of tolerance or loss of efficacy, and improve patients’ responses to chronic drugs [13] in a precision medicine perspective.
Figure 1. Examples of ML applications within diagnosis, prognosis, and treatment.
(…)
Predictive modeling techniques, such as DL, have been used to forecast the progression of rare diseases, allowing for earlier interventions and better treatment planning. This could potentially lead to a more accurate classification of rare diseases and enable the development of more targeted treatments. From a precision medicine perspective, by identifying biomarkers associated with a particular rare disease, AI algorithms can help to develop personalized treatment plans, helping to improve patient outcomes and reduce the risk of side effects.
AI has also shown promise in the field of drug development for rare diseases. AI algorithms can be used to analyze patient data and identify subpopulations of patients who may be most likely to respond to a particular drug. This can help to make clinical trials more efficient and increase the chances of a drug being approved for use in patients with rare disorders. Despite the potential benefits of ML, there are still challenges that must be overcome to fully realize its potential in the field of rare diseases. These include a lack of large, well-annotated datasets, and the need for interpretable models that can be easily understood and trusted by clinicians. Rare diseases usually present an unusually big data regime, which is characterized by huge omics data but a limited number of patients. The rarity of orphan patients, despite the presence of registries, still has a large impact on ML analyses, and thus open data can contribute significantly to support the modeling attempt. In the future, patient registries and open data need to be integrated, translating the largest amounts of data available into potential connections. Finally, a current limitation is the lack of interpretability, which makes it difficult for clinicians and researchers to understand algorithm outputs. In fact, explainability is one of the most debated topics for the application of AI in healthcare. While AI-based systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to attract criticism.
In this context, explainable AI approaches are the new frontier of ML applications in healthcare, in order to ensure the understanding, by both clinicians and patients, of the “mental process” followed by the artificial brain to reach a certain decision.
In conclusion, the application of ML techniques can greatly assist rare disease research and treatment, but to use it effectively, it needs to be implemented under the right ethical principles, avoid biases, and also be transparent for the patient. To fully tap into its potential, AI needs to be validated through clinical trials and real-world evidence. Furthermore, it needs to be accompanied by regulatory frameworks that ensure the safety and reliability of AI-based medical devices and diagnostic tools. More work is needed to overcome data-related challenges, ensure fair and trustworthy models, and help translate the research findings into practical applications that can benefit patients and their families.
I was reminded about an article by Italian researchers titled The Impact of Artificial Intelligence in the Odyssey of Rare Diseases that I had read a while ago, when I noticed Anil Mankar liking this post:
I am not sure whether there is any connection between this company and Brainchip or whether our co-founder simply wanted to show his appreciation for a bunch of people wanting to “make a meaningful impact in changing the lives of patients living with severe, complex diseases” - for all we know, his life or that of his loved ones or friends could be affected.
Companies such as Probably Genetic may not necessarily need neuromorphic technology when implementing AI in order to help patients suffering from rare diseases, but it would be wonderful if Brainchip was somehow involved in contributing to such a worthy cause.
The Impact of Artificial Intelligence in the Odyssey of Rare Diseases
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly...
www.mdpi.com
The Impact of Artificial Intelligence in the Odyssey of Rare Diseases
by
Anna Visibelli1,*,†
,
Bianca Roncaglia1,†,
Ottavia Spiga1,2,3,*
and
Annalisa Santucci1,2,3
1
Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
2
Competence Center ARTES 4.0, 53100 Siena, Italy
3
SienabioACTIVE—SbA, 53100 Siena, Italy
*
Authors to whom correspondence should be addressed.
†
These authors have contributed equally to this work.
Biomedicines 2023, 11(3), 887; https://doi.org/10.3390/biomedicines11030887
Received: 7 February 2023 / Revised: 28 February 2023 / Accepted: 8 March 2023 / Published: 13 March 2023
(This article belongs to the Special Issue Artificial Intelligence in the Detection of Diseases)
Download keyboard_arrow_down
Browse Figure
Versions Notes
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1–9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.Keywords:
rare disease; machine learning; artificial intelligence; precision medicine; data analysis
1. Introduction
The term rare diseases refers to a vast set of diseases that affect a small proportion of the total population; there are more than 7000 known disorders, and an estimated 250 rare new diseases are discovered annually [1]. In addition, diseases with a prevalence of <1 case per 50,000 population are defined as ultra-rare [2]. This definition is related to a prevalence threshold [3], and it differs depending on the jurisdiction. In Europe, the European Medicines Agency considers a prevalence of less than 5 in 10,000 people (less than 1 in 2000) [4], while in the United States, diseases affecting less than 200,000 people in the country were defined as rare by the Orphan Drug Act in 1983 [5]. In Japan, the Ministry of Health, Labor, and Welfare defines a threshold of fewer than 50,000 individuals in the country (equivalent to less than 1 in 2500 people) [6]. Therefore, an international definition of rare disease is lacking. Although individually they can be considered rare, they collectively afflict more than 500 million people worldwide [7]. Most of these disorders have characteristics that pose serious challenges for both researchers and public health professionals, especially for patients who face not only a loss in terms of health and quality of psychological and social well-being, but also financial burdens [8].First, the process of diagnosing a rare disease is often long and exhausting. In 25% of patients, it takes between 5 and 30 years after disease onset to receive a correct diagnosis, which requires the participation of a competent and comprehensive clinical team [9]. A survey conducted from October 2019 through March 2020 by the National Organization for Rare Disorders [10] on 1108 individuals found that 50% of patients and caregivers attribute diagnostic delays to a lack of knowledge about the disease, while 42% believe that delays are caused by limited medical specialization. Many patients identified the problem of doctors not being able to link symptoms, particularly between different organ systems, in addition to the fact that waiting times to consult specialists are long and there would be a need for more tests. A diagnostic delay can have tremendous effects on the patient’s clinical picture, so prompt and accurate diagnosis are the starting point for being able to find therapeutic interventions and resources that can ensure a good clinical outcome [11].
What complicates the rare disease odyssey is that the diagnosis is never the end of the journey, since even from a prognostic and therapeutic point of view, there are huge gaps to be filled [12]. The difficulties encountered at the prognostic level are related to the lack of valid parameters and/or biomarkers, since the molecular pathophysiological mechanisms are still largely unknown. Moreover, the small number of patients does not allow statistically significant parameters to be derived [13]. Thus, the prognosis of patients may change depending on various genetic and environmental factors, but it is complicated to arrive at standards of care for treatment and rehabilitation because health research is necessarily conducted on a small scale and cannot be based on evidence or experience [14]. Conventionally, it takes 10 to 15 years to bring a drug to market, with an average R&D cost of $2.6 billion [15]. These two factors represent a bottleneck in the drug discovery pipeline for rare disorders, as research costs are high while revenues are low due to the small number of patients. This implies that the development of new drugs and treatments can be time-consuming and hindered by the lack of data and funding [16]. The heterogeneous patient populations, often unknown etiology and pathogenesis, the timing of disease progression, and the lack of exhaustive clinical studies make the search for specific drugs very difficult [17]. The key problems related to the development of drugs and therapies for rare diseases are [13]:
- only small cohorts of patients are interested in purchasing these drugs, making them so-called orphan drugs because they are not competitive for pharmaceutical companies.
- difficulties in treatment because most rare diseases are caused by genetic errors and/or have a degenerative nature.
- significant percentages of patients do not respond to available therapies due to partial or complete loss of response.
Possible innovative answers to biomedical and clinical challenges come from the world of information technology, and a striking example has been the fight against COVID-19. Since the beginning of the pandemic, artificial intelligence (AI) has played a crucial role in the battle against the virus, and several methods have been applied for various purposes [18]. Machine learning (ML) and deep learning (DL) models have been used for the early detection and diagnosis of COVID-19 by monitoring the demographic, clinical, and epidemiological characteristics of patients, and for developing diagnostic tools that can quickly analyze CT scans and X-rays to identify patterns indicative of the disease [19]. AI has also been used to predict patient vulnerability, in order to administer appropriate drugs and treatments [20], as well as being decisive in accelerating the discovery of potential vaccines. Similarly, AI has been an essential ally for public health policies in contact tracing, monitoring the spread of the virus, and creating predictive models that have helped to identify potential outbreaks. Thus, it is clear how AI is increasingly coming to the aid of physicians at every stage of disease management, to evaluate the efficacy of medical treatments or deeply investigate the correlation between patients and treatments according to their own molecular characteristics. The precision medicine approach is widely applied to the healthcare area, in particular to rare diseases with the creation of patient registries leveraging large amounts of data to discover potential links. It is a comprehensive and prospective approach to prevention, diagnosis, treatment, and monitoring, built on the genetic characteristics of the individual. Harmonizing databases and including registries are the major facilitators to understand the complexity of diseases, to conduct clinical trials, to improve the drug development process, and to assign the right treatment to the right individual after reliable patient stratification. AI is an ally that can integrate and analyze heterogeneous data (e.g., multi-omics data as well as images). However, first-generation AI systems, which rely on the development of algorithms for diagnosis and treatment that are trained on big data, are not always adequate to meet the needs of rare diseases. Data scarcity and sparsity characterize these disorders, due to fragmented knowledge and the limited number of data and specimens available [21]. Phenotype and disease severity as well as pharmacogenomic and pharmacokinetic factors are the elements on which successful diagnosis and treatment depend [13]. Diagnostic decision support systems (DDSS) already exist, i.e., expert systems that support doctors in facilitating the diagnostic process by incorporating medical knowledge. These systems have been proven effective and have improved clinical diagnosis by compiling lists of appropriate differential diagnoses for a given sample of tests [22,23]. For rare diseases, these systems need to be implemented. Networks and registries have been built to bring together data and expertise on rare diseases, making them free, accessible, and shareable worldwide. One example is Orphanet, which over the past 20 years has become the go-to source for information on rare diseases, facilitating access to information and means to identify potential patients, and contributing to the development and sharing of knowledge. Other available datasets are the Online Mendelian Inheritance in Man and Human Phenotype Ontology. The knowledge deposited in these databases is used by DDSSs built for rare disorders, some examples of which are FindZebra [24], PhenoTips [25], Rare Disease Discovery [26], and Ada DX [27].
Second-generation AI systems were designed to fill the diagnostic, prognostic, and therapeutic gaps that must be overcome to achieve patient-centricity for patients with rare diseases [28] (Figure 1). These systems use a personalized closed-loop system designed to enhance end-organ function, overcome problems of tolerance or loss of efficacy, and improve patients’ responses to chronic drugs [13] in a precision medicine perspective.
Figure 1. Examples of ML applications within diagnosis, prognosis, and treatment.
(…)
5. Conclusions
ML methods have shown promise in the identification and diagnosis of rare diseases. With the vast amounts of data now available through electronic health records and heterogeneous databases, AI algorithms can help quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. For example, researchers use ML algorithms to analyze patient data and identify characteristic patterns associated with certain rare diseases. By doing so, these tools can help to narrow down the list of possible diagnoses, making it more likely that patients will receive a correct diagnosis in a timely manner.Predictive modeling techniques, such as DL, have been used to forecast the progression of rare diseases, allowing for earlier interventions and better treatment planning. This could potentially lead to a more accurate classification of rare diseases and enable the development of more targeted treatments. From a precision medicine perspective, by identifying biomarkers associated with a particular rare disease, AI algorithms can help to develop personalized treatment plans, helping to improve patient outcomes and reduce the risk of side effects.
AI has also shown promise in the field of drug development for rare diseases. AI algorithms can be used to analyze patient data and identify subpopulations of patients who may be most likely to respond to a particular drug. This can help to make clinical trials more efficient and increase the chances of a drug being approved for use in patients with rare disorders. Despite the potential benefits of ML, there are still challenges that must be overcome to fully realize its potential in the field of rare diseases. These include a lack of large, well-annotated datasets, and the need for interpretable models that can be easily understood and trusted by clinicians. Rare diseases usually present an unusually big data regime, which is characterized by huge omics data but a limited number of patients. The rarity of orphan patients, despite the presence of registries, still has a large impact on ML analyses, and thus open data can contribute significantly to support the modeling attempt. In the future, patient registries and open data need to be integrated, translating the largest amounts of data available into potential connections. Finally, a current limitation is the lack of interpretability, which makes it difficult for clinicians and researchers to understand algorithm outputs. In fact, explainability is one of the most debated topics for the application of AI in healthcare. While AI-based systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to attract criticism.
In this context, explainable AI approaches are the new frontier of ML applications in healthcare, in order to ensure the understanding, by both clinicians and patients, of the “mental process” followed by the artificial brain to reach a certain decision.
In conclusion, the application of ML techniques can greatly assist rare disease research and treatment, but to use it effectively, it needs to be implemented under the right ethical principles, avoid biases, and also be transparent for the patient. To fully tap into its potential, AI needs to be validated through clinical trials and real-world evidence. Furthermore, it needs to be accompanied by regulatory frameworks that ensure the safety and reliability of AI-based medical devices and diagnostic tools. More work is needed to overcome data-related challenges, ensure fair and trustworthy models, and help translate the research findings into practical applications that can benefit patients and their families.
Getupthere
Regular
Microsoft Unveils AI Chip That Could Replace Nvidia in Its Cloud — Barron's
Microsoft will soon use its own in-house chip instead of Nvidia’s for some of its AI applications. On Wednesday, Microsoft (ticker: MSFT) announced its Azure Maia AI Accelerator. The chip is designed to run generative AI and other AI workloads—including large language model training and...
Shorter cycles- let the games begin!
Arm Collaborates with Microsoft on Custom Silicon to Unlock Sustainable, AI-Driven Infrastructure
In collaboration with Arm, Microsoft announced their first custom silicon built for the cloud, based on Arm Neoverse CSS.
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Arm on LinkedIn: #msignite #onarm
📢🚨 News from #MSIgnite: Microsoft Azure Cobalt 100 is here! Based on Arm Neoverse CSS, Microsoft's first custom silicon built for the cloud is designed to…
MDhere
Regular
Morming fellow brners, you heard it from the horses mouth in the most recent podcast, Ceo thinks it represents good buying opportunity for investors.
Now correct me if im wrong BUT a CEO addressing shareholders in a podcast filled with questions from shareholders is NOT going to step a foot out of place, so in SAYING HE THINKS ITS A GOOD BUYING OPPORTUNITY FOR INVESTORS,
he means exactly that! And this podcast is for current investors and future investors who are tuned into the podcast.
So to all the naysayers out there including yesteday playground brawl - Ceo thinks it's a good buying opportunity! Get yr lunch money out and either buy shares or suck on yr lollipops and zip it
get yr mums to wash yr hair with Pantene when u get home at 3pm.
I know for a fact if I had enough money I would be buying shares and not a F ing lollipop!!
Ok now I need A COFFEE @M_C
Now correct me if im wrong BUT a CEO addressing shareholders in a podcast filled with questions from shareholders is NOT going to step a foot out of place, so in SAYING HE THINKS ITS A GOOD BUYING OPPORTUNITY FOR INVESTORS,
he means exactly that! And this podcast is for current investors and future investors who are tuned into the podcast.
So to all the naysayers out there including yesteday playground brawl - Ceo thinks it's a good buying opportunity! Get yr lunch money out and either buy shares or suck on yr lollipops and zip it
get yr mums to wash yr hair with Pantene when u get home at 3pm.I know for a fact if I had enough money I would be buying shares and not a F ing lollipop!!
Ok now I need A COFFEE @M_C
Quiltman
Regular
Morming fellow brners, you heard it from the horses mouth in the most recent podcast, Ceo thinks it represents good buying opportunity for investors.
Now correct me if im wrong BUT a CEO addressing shareholders in a podcast filled with questions from shareholders is NOT going to step a foot out of place, so in SAYING HE THINKS ITS A GOOD BUYING OPPORTUNITY FOR INVESTORS,
he means exactly that! And this podcast is for current investors and future investors who are tuned into the podcast.
So to all the naysayers out there including yesteday playground brawl - Ceo thinks it's a good buying opportunity! Get yr lunch money out and either buy shares or suck on yr lollipops and zip it
I know for a fact if I had enough money I would be buying shares and not a F ing lollipop!!
Ok now I need A COFFEE @M_C
In the same vein ...
As a CEO, 2 years into a 5 year programme , a programme which is signed off by the board, I would only say 2024 was a "make or break" year for the company if I knew , for certain, it was a "make". Or else my 4th and 5th years may be looking a little tenuous.
Otherwise, you would hear me saying things like :
"2024 is the last of our foundation building years", or,
" 2024 will see us to continue to build on the ecosystem ".
...
buena suerte :-)
BOB Bank of Brainchip
Thanks for your late response !
Have been dreaming for 9 Years!!! no problem with that
One day
Schnitzel lover
Regular
Sean did say 2024 is make it break..I liked that honesty.. And I agree with him.."situation we're in"
By "situation" do you mean heavily invested in a ground breaking, future titan of the tech world who's on the brink of bringing in millions if not billions in revenue in the coming years..........?
Sorry lacking context, i may have fallen asleep during your previous posts
FYIA
View attachment 49543
The truth is it’s a listed company and the price is the only true measure of how well it’s going pre-commercialisation stage.. And by that I mean revenue incoming from commercial engagements and royalty from products..
So by its very definition, many listed companies that are close to reporting good revenue and having a large addressable market, will have significantly higher market valuations than BRNs current one.. This is where @DerAtkienDude is 100 percent on the money, in that there’s currently no definable and measure-able way to genuinely say that Brainchip is successful at the moment.
Not until they truly do show market penetration through their financials can anyone state Brainchip is currently successful.. Hell, even the CEO says he is not satisfied with Brainchips current sales results.
And when the financials do show what we’re all wanting to see then, I’m sure @DerAtkienDude will also agree that it is then considered succeeding..
Success is the progressive realisation of a worthy ideal.. What’s BRNs worthy ideal? To promote and saturate the market with devices that are eco friendly and solve many bottleneck mainstream challenges of today.. When-we start seeing some of that market saturation and penetration, then we can say they are progressively realising their worthy ideal- success..
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