Innovative AI - Artificial Intelligence (AI) and its impact on businesses.
AI in Healthcare: Transforming Medicine for Better Patient Outcomes
This week in Innovative AI
Innovative AI Shorts:
MIT researchers developed a groundbreaking system combining light and electrons for faster and greener computing
The Power Duo: AI and Liquid Cooling Unleash the Potential of Data Centers
The Importance of a Positive Case for AI in Retail
Executives’ Highlight: The Application of Artificial Intelligence in the Healthcare Industry
Prompts for your Organization: Midjourney for product photos;
ChatGPT to prepare for job interviews
Overview: Center for Deep Tech Innovation Events
Tools to try out: Logo.ai
Innovative AI Shorts
MIT researchers developed a groundbreaking system combining light and electrons for faster and greener computing
Researchers at MIT have developed a new system that combines light and electrons, aiming to unlock faster and greener computing. Traditionally, computing is done using electrons, which generate heat and can slow down the performance. However, by integrating photons with electrons, the team was able to create a method that utilizes both particles for data transfer, resulting in faster and more efficient computation. This new approach not only increases the speed of computing but also reduces energy consumption, making it a greener alternative to traditional computing methods. The system, called "photon–electron signal processing" (PESP), has the potential to revolutionize the field of computing, benefiting various industries including telecommunications, data centers, and even medical imaging. With further advancements and optimizations, PESP could pave the way for a future of faster and more sustainable computing.
The Power Duo: AI and Liquid Cooling Unleash the Potential of Data Centers
AI and liquid cooling are shaping the future of data centers, according to Forbes. With increasing heat generated by data centers, traditional air-based cooling systems are becoming inadequate. Liquid cooling, using substances like water or oil, offers a more efficient and environmentally friendly solution. AI optimization further enhances the cooling process by adjusting coolant flow and temperature based on real-time data. This technology reduces energy consumption by up to 50%, resulting in cost savings and a reduced carbon footprint. Furthermore, liquid cooling improves server performance and lifespan, making it a promising solution for the growing demands of the data center industry.
The Importance of a Positive Case for AI in Retail
In today's rapidly evolving retail industry, artificial intelligence (AI) is playing a crucial role in driving growth and enhancing customer experiences. However, there is often skepticism and fear surrounding AI, which can hinder its adoption. In a recent article, Forbes Tech Council discusses the importance of retail leaders making a positive case for AI to address these concerns and demonstrate the potential benefits it can bring. By highlighting the ways in which AI can optimize operations, personalize customer experiences, and drive revenue growth, retail leaders can build trust and confidence in this transformative technology. By leveraging AI's capabilities, retailers can stay ahead of the curve and continue to thrive in today's competitive landscape.
The Application of Artificial Intelligence in the Healthcare Industry
In recent years, the healthcare industry has been undergoing a silent revolution, powered by artificial intelligence (AI). From predictive analytics to intelligent robots assisting in surgeries, AI is reshaping how we approach health. In this article, we will take a look at the application areas where AI is or will be having a big impact on the healthcare industry.
The economic impact of AI in healthcare:
Before we delve into the application areas, we want to first give you a little outlook on some economic figures. A recent survey by Morgan Stanley Research showed that 94% of healthcare organizations are already using AI in some form. They estimate that the industry's average budget allocation for AI technologies will nearly double from 5.7% in 2022 to an estimated 10.5% in 2024. Experts thereby see the greatest potential in the following areas:
Biopharma
Healthcare services and technology
Life sciences tools and diagnostics
Medical technology
Let us take, e.g., a look at drug discovery, clinical development, and manufacturing (biopharma). The number of applications submitted to the U.S. Food and Drug Administration (FDA) for drugs and biologics containing AI components is increasing rapidly. Experts see major enhancements of AI in shortening drug development time, reducing R&D spending, and increasing the likelihood of successful patient outcomes.
For instance, according to Terence Flynn, Head of U.S. Biopharma Research at Morgan Stanley, a 2.5% improvement in preclinical development success rates could lead to more than 30 new drug approvals within a decade. That alone would add $70 billion in value to the biopharma industry.
AI technologies relevant to healthcare
With the economic figures in mind, let us turn the AI technologies (becoming) relevant in the healthcare sector. In the following, we list and describe the currently most important ones:
Machine Learning: Neural Networks and Deep Learning:
Machine learning, a statistical technique for fitting models to data, is one of the most common forms of AI, with 63% of companies employing it in their businesses according to a Deloitte survey already from 2018 - the actual number should be much higher. In healthcare, it is predominantly used in precision medicine to predict successful treatment protocols based on various patient attributes and the treatment context. Deep learning, a more complex form of machine learning, involves neural network models with many levels of features or variables predicting outcomes. It has found substantial application in healthcare, notably in radiology where it aids in recognizing potentially cancerous lesions. Deep learning is also increasingly being applied to enhance the detection of clinically relevant features in imaging data beyond what the human eye can perceive - promising greater accuracy in diagnosis compared to previous automated tools for image analysis.
Natural Language Processing (NLP):
NLP has been a focal point in AI research since the 1950s, aiming to make sense of human language through applications such as speech recognition, text analysis, and translation. In healthcare, it is predominantly used to create, understand, and classify clinical documentation and published research. NLP systems can analyze unstructured clinical notes on patients, prepare reports, transcribe patient interactions, and facilitate conversational AI, enhancing both administrative efficiency and patient care.
Rule-Based Expert Systems:
Rule-based expert systems, which dominated AI technology in the 1980s, are based on a collection of "if-then" rules to analyze information. In healthcare, they have been widely employed for clinical decision support over the last few decades. Despite their utility, they face limitations such as difficulty in handling large rule sets and adapting to changing knowledge domains, which has led to a gradual shift towards data and machine learning algorithm-based approaches.
Robotic Process Automation (RPA):
RPA technology performs structured digital tasks for administrative purposes, acting like a semi-intelligent user of the systems. In healthcare, it is utilized for repetitive tasks such as prior authorization, updating patient records, and billing, reducing manual errors and saving time. It can also be combined with other technologies like image recognition to extract data from faxed images for input into transactional systems, promising a more efficient administrative process in healthcare settings.
Technological advancements and operational efficiency through AI:
As already outlined, AI can have an enormous positive impact on the work of healthcare professionals. Particularly in the areas of analytics, as well as operational activities and surgery, we believe there is great potential. Below we listed a few examples that we consider interesting.
The healthcare sector in particular is characterized by a complex web of data that includes patient records, clinical trials, and research studies. AI can leverage this rich data landscape to derive actionable insights, paving the way for innovations in treatment and patient care. For example:
AI can help detect and diagnose diseases faster and more accurately because of its predictive capabilities. A significant step here was the introduction of next-generation sequencing (NGS) and the transition to electronic health records. This has opened the doors for advanced diagnostics.
The integration of genomic data into patient records and medical imaging (technique and process of imaging the interior of a body for clinical analysis) enabled the development of algorithms. These algorithms can provide profound insights into the causes of diseases and reveal subtle correlations that cannot be detected by conventional analytical methods.
Furthermore, there is significant upside potential in the medical technology (medtech) space, as only 24% of medical devices currently use AI technologies. Early patient interactions, such as continuous glucose monitoring and cardiac monitoring, are a good fit for AI use. For example, AI can analyze data from devices to detect abnormalities and recommend treatments.
From an operational perspective, AI can help to reduce the stress of healthcare professionals. Below, we listed 3 examples of how AI can help to make their everyday lives easier.
AI can significantly reduce the burden of administrative tasks in healthcare settings. According to a report by IBM, AI can assist doctors in dictating notes hands-free, allowing them more time for face-to-face interactions with patients. Moreover, AI facilitates accurate coding and seamless information sharing between departments, enhancing the efficiency of billing processes and reducing the time spent on paperwork.
Virtual nursing assistants are becoming increasingly popular in healthcare settings. According to a study cited by IBM, 64% of patients are comfortable using AI for round-the-clock access to the support that nurses provide. These assistants, often facilitated through AI-powered chatbots or apps, can answer medication-related questions, forward reports to doctors, and assist patients in scheduling visits, thereby reducing the workload on clinical staff and enhancing patient care.
AI is also playing a crucial role in reducing dosage errors, thereby improving patient health outcomes. For instance, a study published in Nature Medicine highlighted an AI system capable of identifying errors in how patients self-administer medications, such as insulin, using a wireless sensing tool that operates in the background, much like a Wi-Fi router. This system can flag errors in real time, helping to prevent adverse effects and improve health outcomes.
AI can enhance safety and precision in surgery. In some cases, AI enables surgeons to operate in tiny spaces that would have required open surgery, reducing risks such as blood loss and infection. Moreover, AI-assisted robotic surgery can work around sensitive organs and tissues, minimizing scarring and shortening recovery times, as noted in various reports by healthcare technology firms.
Eventually, another aspect we considered quite interesting is fraud prevention. Nowadays the healthcare industry faces enormous fraud issues, amounting to around $380 billion per year according to industry reports. AI can help identify unusual or suspicious patterns in insurance claims, such as billing for costly services that were not provided or running unnecessary tests to exploit insurance payments.
Examples of real-world application areas of AI in healthcare
After having outlined the potential of AI in the healthcare sector, we want to present you with some real-world examples of innovative use cases.
Drug Discovery and Development:
In the realm of drug discovery and development, AI is making significant strides. Companies like Atomwise are utilizing AI for drug discovery, leveraging deep learning algorithms to predict which molecules could be effective in treating diseases. This approach has the potential to significantly reduce the time and cost associated with bringing new drugs to market.
Virtual Health Assistants:
Virtual health assistants are enhancing patient engagement and care coordination. For instance, the startup Babylon Health offers an AI-powered app that can listen to patients' symptoms and offer medical advice based on its analysis. These virtual assistants help in managing health more effectively by providing personalized advice, reminders for medication, and helping to schedule appointments, thereby improving adherence to treatment plans and enhancing health outcomes.
Diagnostic AI:
Diagnostic AI is revolutionizing healthcare by enhancing the accuracy and efficiency of diagnosing diseases. For instance, Google's DeepMind has been used to analyze eye scans to detect signs of diabetic retinopathy, a leading cause of blindness, with a high degree of accuracy. Moreover, AI algorithms can analyze medical images, pathology slides, and even genomic sequences to identify diseases early on, sometimes even before symptoms appear, facilitating timely interventions and improving patient outcomes.
Remote Patient Monitoring:
Remote patient monitoring is another area where AI is making a significant impact. Companies like Biofourmis use AI to analyze data from wearable devices to monitor patients' health in real-time, identifying potential issues before they become serious problems. This not only ensures continuous monitoring of patients but also reduces the need for frequent hospital visits, promising a more patient-centric approach to healthcare.
Predictive Analytics:
Predictive analytics is helping healthcare providers anticipate issues before they occur. For example, the Cleveland Clinic uses AI to analyze data and identify patients at risk of readmission, enabling early interventions and reducing the burden on healthcare systems. By analyzing patterns in large datasets, AI can help identify patients at risk of developing certain conditions, enabling early interventions and potentially saving lives.
Considerations in AI Healthcare Applications
Since the healthcare sector is a very important one, considerations must also be taken into account. In the following, we outline what we consider to be the most important aspects:
Data Privacy and Security:
As AI systems often require access to large amounts of sensitive personal data, ensuring data privacy and security is paramount. Healthcare organizations must implement robust security measures to protect patient data from unauthorized access and cyber-attacks. Moreover, there is a need for clear guidelines on data usage to prevent misuse and ensure that individuals retain control over their personal information.
Bias and Representativity:
AI systems are trained on data, and if this data is not representative of the diverse patient population, it can lead to biased outcomes. It is essential to ensure that AI algorithms are developed using diverse datasets to avoid biases based on race, gender, age, or other factors. Moreover, continuous monitoring and validation of AI systems are necessary to identify and mitigate any biases that may arise.
Informed Consent:
In the context of AI in healthcare, informed consent goes beyond the traditional understanding. Patients should be informed about the involvement of AI in their care, including the potential risks and benefits. This would allow patients to make informed decisions about their treatment and whether they are comfortable with the use of AI in their healthcare journey.
Accountability and Responsibility:
With AI systems making critical decisions in healthcare, determining accountability in case of errors becomes a complex issue. It is essential to establish clear lines of accountability to ensure that responsible parties can be held accountable in case of adverse outcomes. Moreover, there should be mechanisms in place for continuous monitoring and evaluation of AI systems to ensure their safety and effectiveness.
Concluding remarks
The rise of artificial intelligence (AI) is starting a new era where technology and human expertise intersect to redefine patient care. However, this journey will not be without its share of challenges and concerns. One of the most pressing issues is the delicate balance between the benefits of AI-driven efficiency and the preservation of the human touch and empathy in patient care.
AI offers undeniable advantages such as increased efficiency, cost savings, and enhanced diagnostic capabilities. It has the potential to revolutionize healthcare by providing quicker disease detection, streamlining administrative tasks, and offering personalized treatment recommendations. Yet, AI cannot replace the human touch and empathy. These characteristics will always remain unique to humans and play an important role in the relationship between a healthcare professional and his patient.
This is why, experts advocate for a responsible AI adoption that adheres to the "Goldilocks rule" - a balance that integrates AI seamlessly into workflows to augment, rather than replace, the skills of healthcare professionals. In this ideal scenario, AI analyzes data to provide actionable insights, assists surgical robots, and handles repetitive tasks such as paperwork, allowing healthcare professionals to spend more time with their patients.
Prompts for your Organization
Midjourney for product photos
“/imagine: Eye-level Commercial film photography of a red wine bottle in the middle of vines on a cliff close to the sea in Portugal, sunny summer day, packshot, focus, depth of field, shot on 120mm, shot on Hasselblad, sharp focus, --v 5.2 --ar 16:9“
ChatGPT to prepare for job interviews
“You’re the interviewer for this role of junior analyst at EY. Can you come up with 3-5 interview questions that I should be prepared for?
Overview: Center for Deep Tech Innovation Events
As some of our readers might know, we offer a series of webinars on the topic of AI (and other technologies in the near future).
Below you find a list of upcoming webinars:
Wednesday, October 4, 2023, 4:00 p.m. (CET) - Prompt Engineering for Business Transformation: A Workshop
more to come!
For more content, feel free to visit our YouTube channel and LinkedIn
Tools to Try out: logo.ai
LogoAi is an AI-powered logo creation platform that facilitates the design of logos, matching stationery, and brand identities. It provides a variety of templates and adjustment options when creating a logo - hence, it is suitable for individuals with little to no graphic design experience. While the design process is free, users are required to pay to download their creations, with the available packages offering varying features, including different resolution options for downloads.
We tested it and think that it is a nice tool to get the first ideas of how your logo could look like. We particularly liked that once designed you can look at how your logo would look on different surfaces from a company sign to a shirt print. All in all, it is suitable for experimenting and playing around, whether it is sufficient to actually design your logo is up to you.
Eventually, we come to the end of this newsletter. Stay tuned for the upcoming editions, where we will keep informing you about the latest developments and applications of AI for business and beyond.
Exemplary prompts
Midjourney for product photos
“/imagine: Eye-level Commercial film photography of a red wine bottle in the middle of vines on a cliff close to the sea in Portugal, sunny summer day, packshot, focus, depth of field, shot on 120mm, shot on Hasselblad, sharp focus, --v 5.2 --ar 16:9“
ChatGPT to prepare for job interviews
“You’re the interviewer for this role of junior analyst at EY. Can you come up with 3-5 interview questions that I should be prepared for?”
