Qualitative Research: AI’s Role in Analysis Advancement


Qualitative research plays a pivotal role in enriching our comprehension of individual narratives and experiences. It is a cornerstone methodology for design researchers seeking to forge a deep connection with user perspectives, particularly during the initial phases of the design process. This approach is instrumental in guiding iterative design developments, ensuring that end-user needs are comprehensively addressed. Qualitative data encompasses a diverse array of formats, including textual content, photographs, and videos. Typically, these studies involve a more focused sample size, often with 10 or fewer participants, to facilitate an intensive, detail-oriented analysis that quantitative methods may not capture.

Although qualitative research is the methodology of choice for design researchers, the approach requires a considerable time commitment. Qualitative data is known for being unwieldy at times, and words and images require more hours of analysis than numeric data. Often, our clients are eager to obtain research findings as quickly as possible to move a product or system into production. Therefore, large scale qualitative studies are not feasible for most design research projects. With the recent surge in the availability of AI language model tools, we speculated that ChatGPT could be used to analyze extremely large sets of qualitative data more efficiently. To that end, we conducted a 6-month project testing ChatGPT as a potential tool for qualitative data analysis.


Our aim in conducting this project was to determine if AI could produce insights from a large dataset that would otherwise be unmanageable and time prohibitive for a human researcher. We used data from 25,000 open response questions to explore the capacity and capability of ChatGPT as computer-assisted qualitative data analysis software (CAQDAS). The dataset we used was provided by the VIA Institute on Character, a local non-profit organization with which we are affiliated. We decided to experiment with ChatGPT to determine if it could reliably and accurately analyze text data. Our expectation was that if ChatGPT could analyze qualitative datasets with tens or hundredsof thousands of respondents, new pathways for qualitative researchers may develop.Using AI for data analysis could change the trajectory of a research design and lead to large scale qualitative studies that were not possible before now.


To test the limits of ChatGPT 4.0 (the only version with the means to upload files), we tried two different approaches to determine the capability of the tool.


We started with a vague set of user queries to place the data preparation load on the CAQDAS and to determine if it would complete the same tasks a human researcher would.

User Query: Analyze the data in column AQ, identify themes, and provide 3-5 insights based on participant responses.

Result: Not useful.

  1. ChatGPT did not automatically clean the data without instruction which caused an error. The output from ChatGPT indicated the data file was either too long or too complex and it was unable to proceed with analysis. The raw data included responses such as “N/A” or random strings of letters, which a researcher would have deleted or ignored before analysis.
  2. Lacking more specific instruction, ChatGPT defaulted to a quantitative approach to data analysis, even though the data were text responses. One of the first outputs ChatGPT produced was a count of the most common phrases in the dataset.
We concluded that this approach to creating user queries was not useful. ChatGPT attempted to analyze the data but quickly became overwhelmed and either produced an error message or continued to attempt analysis, getting caught in the AI version of theMac’s “spinning wheel of death.



We then provided ChatGPT with more specific instructions. We instructed it toclean, review and code the data, then create insights using a theoretical framework as a guide for analysis.

User Query: I'd like to analyze some text data using Peace Psychology and Positive Psychology as theoretical frameworks. Include content from the VIA Institute on Character as an additional framework. Focus on data in the 'Open Responses_Political Differences' column.

First, ignore text that indicates a respondent did not want to answer such as 'N/A' or random strings of letters. Leave those cells blank. Next, use descriptive codessuch as a phrase that describes the content of the targeted data.

Create a new document and filter the data from columnAD. Group the data according to the codes created in columnAD and list each data point that corresponds to the code.

Create 3-5 insights using the coded data in ColumnAD using positive psychology and peace psychology as theoretical frameworks.

Create a Word document and place the insights you created in it. Make the file available for download.

Result: Success

ChatGPT produced a list of five insights using the VIA classification of character strengths as a framework, supported by references to positive psychology and peace psychology.

Our last step was to think about how to test the accuracy and reliability of the findings. Rachael has a strong background in qualitative methodology, so she extracted a sample of 20 data points and used a traditional qualitative approach to code the data. We also queried ChatGPT using the same data set with similar user queries to determine the degree of similarity between both sets of outputs.


Within the first 20 rows of participant responses, we identified at least three codes related to the five insights ChatGPT produced. We were pleased with the accuracy of the codes as they related to the insights from our sample data.


Both researchers requested insight generation from ChatGPT using similar but not identical queries. Chat GPT’s output for both sets of queries was closely aligned. Three of the five generated insights were nearly identical between the two researchers and two were unique per researcher. It is important to note that even within research teams, differences arise between researchers’ interpretation of qualitative data. Disagreements are resolved through discussion and are typical of a qualitative research project.

Finally, we tested some of the limits of ChatGPT with several ad hoc user queries. Ourqueries produced a great deal of information in a very short period.


  1. Code Participant Country of Origin: We queried the data set to determine if each participants country of origin represented an individualistic or collectivist nation(coded as “country designation”). We asked ChatGPT to code each participants country of origin using this designation. The codes were placed in a new column created by ChatGPT.
  2. Group Data and Analyze Frequency: We asked ChatGPT to examine the most common keywords and themes by country of origin. It provided a summary for all countries represented in this data set with the most frequently used codes according to participant’s country of origin.
  3. Character Strength Frequency by Country Designation: We prompted ChatGPT to organize the data according to country designation and create a bar chart to illustrate the frequency of character strengths chosen as most beneficial by participants.

All the ad hoc requests were completed successfully. ChatGPT confirmed its approach with users before completing each step. For example, after requesting the data be coded according to country designation, ChatGPT responded:

Sure! In order to label countries as "collectivist" or "individualist," I would need a reference or criteria to determine which category each country falls into. There are various sources and studies that have categorized countries based on their cultural dimensions, including the work by Geert Hofstede.

Do you have a specific reference or criteria in mind that you'd like me to use? Alternatively, I can use Hofstede's cultural dimensions as a general guideline.

ChatGPT also encouraged us to use caution when interpreting the findings from the between-country analysis, because significantly more participants were from individualistic nations.


We learned through this project that ChatGPT exhibits several esoteric preferences for working with Excel files. We only used Excel to upload data sets, so our suggestions are restricted to this software.

1. ChatGPT cannot analyze data if it has been tagged with a data type. The output will state that it completed the user query, but new files will not show any changes.

SOLUTION: Remove any Data Types tags before uploading Excel files to​​​ChatGPT.

qualitative research data types

2. ChatGPT prefers references to column names instead of the letters Excel uses to identify columns.

SOLUTION: If a user query contains a letter identifier instead of a column name, remove the space between the word “Column” and the letter.

CORRECT: “Provide a mean for the data in columnAI.”

INCORRECT: “Provide a mean for the data in Column AI.”

3. Unless instructed, ChatGPT will not automatically clean uploaded data. If a user attempts to request analysis before cleaning, it will respond with an error message.

SOLUTION: Provide explicit instructions for data cleaning before analysis.


We shared just a fraction of the user queries we submitted over a 6-month period to test ChatGPT as a qualitative analysis tool. We presented the successes and failuresas linear, concise processes for readability. However, early in the project, ChatGPT was often overwhelmed with requests and our queries resulted in error messages. Queries usually required several back-and-forth inputs between researchers and the AI to clarify instructions. With little or no guidance, ChatGPT was unable to produce results. We found that the AI required specific instructions to function as computer-assisted qualitative data analysis software. Our bottom-line recommendation is that well trained researchers test the tool using a data set for which they already possess human produced findings. Compare those findings with ChatGPT's output and evaluate its reliability and accuracy.

Based on our brief examination of ChatGPT’s capability, we advise only well-trained researchers with extensive qualitative research experience to use AI as a computer-assisted data analysis tool. As in any other profession, expertise and training are the best predictors of quality work. As the saying goes, garbage in garbage out.Users with no idea how to design a rigorous research study will not provide the needed input for AI to perform adequately.

Our early work indicates the potential for AI to assist in qualitative data analysis. Like other CAQDAS products such as MAXQDA and NVivo, the software serves as a management and organizational tool. We envision ChatGPT as a marginally higher-leveltool with the capacity for categorizing and summarizing qualitative data, with the proper guidance and instruction.

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  • Rachael Clark

    Rachael Clark

    Senior Design Researcher | [email protected]

    Rachael brings over 10 years of research experience to her role at Kaleidoscope Innovation. She has advanced training in clinical psychology and mixed methods research methodology. Guided by the principles of positive psychology, Rachael uses a human-centered lens for deeply understanding the user experience. Her work at Kaleidoscope focuses on human-machine interaction and identifying design changes capable of positively impacting well-being at the individual and institutional levels.

  • Grant Cothrel

    Grant Cothrel

    Senior Design Engineer | [email protected]

    Grant is a Senior Design Engineer who enjoys being faced with new challenges, and recognizes that well-designed products lead to better experiences and outcomes for users. He loves the design process, and has operated primarily in medical device and industrial applications. His passion to understand, innovate, and simplify has been supported and strengthened by the Kaleidoscope team and their talented partners. At home, he always has a fun project in the works (think: wooden bicycle, handheld Theremin, one-string electric guitar)!

Orthopedics Unleashed: The AI-Implant Revolution

The orthopedic segment is currently undergoing a remarkable transformation that is fueled by the convergence of smart implants and artificial intelligence (AI), ushering in a new era of innovation. This powerful combination is revolutionizing the field, enhancing diagnosis, improving surgical precision, optimizing post-operative care, and delivering better outcomes for patients. In this article, we delve into the top 10 ways in which smart implants and AI are driving this transformation, paving the way for a new standard of personalized and data-driven patient care. 

  1. REAL-TIME MONITORING AND FEEDBACK: Smart implants embedded with sensors and wireless connectivity enable real-time monitoring of vital parameters such as joint movement, implant performance, and tissue response. This data provides valuable insights to healthcare professionals, facilitating early detection of complications and enabling timely interventions.
  2. PREDICTIVE ANALYTICS FOR PROACTIVE INTERVENTION: By harnessing the power of AI and machine learning algorithms, smart implants can analyze large volumes of patient data to predict and prevent adverse events. These predictive models help identify patients at high risk of implant failure or post-operative complications, allowing for proactive interventions and personalized care plans. 
  3. PRECISION SURGERY AND NAVIGATION: AI-powered surgical planning and navigation systems provide surgeons with detailed anatomical information, assisting in precise implant placement and alignment. This technology improves surgical outcomes, reduces complications, and enhances patient satisfaction. 
  4. INTELLIGENT REHABILITATION AND PHYSICAL THERAPY: Smart implants, in conjunction with AI-driven rehabilitation programs, enable personalized and adaptive physical therapy. By monitoring patient progress and adjusting therapy regimens in real-time, these systems optimize recovery and rehabilitation, leading to faster and more successful outcomes. 
  5. ENHANCED PATIENT ENGAGEMENT AND EDUCATION: Smart implants equipped with patient-centric interfaces and mobile applications empower patients to actively participate in their own care. These technologies provide educational resources, track progress, offer reminders, and enable direct communication with healthcare providers, fostering a collaborative and engaged patient experience. 
  6. REMOTE MONITORING AND TELEHEALTH: AI-powered remote monitoring solutions enable healthcare providers to remotely assess patient progress, detect potential complications, and provide virtual consultations. This approach improves access to care, reduces healthcare costs, and enhances patient convenience, particularly for those in remote or underserved areas. 
  7. DATA-DRIVEN TREATMENT DECISION MAKING: AI algorithms can analyze vast amounts of patient data, clinical trials, and research studies to provide evidence-based treatment recommendations. This data-driven approach enhances treatment decision-making, optimizing outcomes and reducing variability in care. 
  8. PERSONALIZED IMPLANT DESIGN AND MANUFACTURING: AI algorithms can analyze patient-specific data, such as anatomical scans and biomechanical parameters, to design and manufacture personalized orthopedic implants. This customization improves implant fit, functionality, and patient satisfaction. 
  9. PREDICTIVE MAINTENANCE AND LONGEVITY ASSESSMENT: Smart implants equipped with AI algorithms can continuously monitor implant performance and assess the risk of wear, fatigue, or failure. This predictive maintenance approach allows for proactive interventions, reducing the likelihood of unplanned revisions and improving implant longevity. 
  10. BIG DATA ANALYTICS FOR RESEARCH AND INNOVATION: The integration of smart implants and AI generates vast amounts of patient data, contributing to large-scale data repositories for research and innovation. AI algorithms can analyze this data to identify trends, patterns, and insights, leading to breakthroughs in orthopedic treatments, implant designs, and surgical techniques. 

The integration of smart implants and AI is reshaping the orthopedic segment, unlocking new possibilities for personalized, data-driven, and patient-centric care. From real-time monitoring to precision surgery, predictive analytics to remote monitoring, these innovations are transforming the way orthopedic conditions are diagnosed, treated, and managed. As we continue to explore the potential of smart implants and AI in orthopedics, it is evident that this convergence will play a pivotal role in improving patient outcomes, reducing healthcare costs, and advancing the field of orthopedic medicine into a new era of innovation and excellence. 

Moreover, the article also acknowledges the importance of orthopedic product development, design, and consulting in driving these advancements. Medical device engineering consultants are key players in translating concepts to commercialization, ensuring the successful development of products such as Total Hip Arthroplasty Systems, Total Knee Arthroplasty Systems, Trauma Plates and Screw Systems, Surgical Navigation Systems, Cervical Plates and Screws, Facet Screw Systems, and Interbody Fusion Devices. This collaborative effort between orthopedic design experts and engineering consultants contributes to the overall quality improvement projects within the orthopedic field. Additionally, the incorporation of concepts like augmented reality, fixation, and retractor systems further underscores the comprehensive scope of innovation within orthopedics. 

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  • Matt Suits

    Matt Suits

    Head of Sales | [email protected]

    Matt has always loved interacting with clients to find solutions for their challenges. He was drawn to business development at Kaleidoscope Innovation because of the great potential he saw. After graduating from the Lindner College of Business at the University of Cincinnati, he worked with two startups, a marketing consultancy, a financial services company and the non-profit 3CDC. He believes that listening is the most important part of sales. In his free time, Matt enjoys movies, trying new foods, traveling and the great outdoors.

Rapid Prototyping Revolutionizing Orthopedic Device Development

Rapid prototyping has emerged as a transformative force within the field of orthopedic device development, reshaping the way medical devices are conceptualized, tested, and brought to market. In this article, we delve into the substantial influence that rapid prototyping is exerting on the orthopedic industry, exploring its advantages, applications, and prospective implications. 

Accelerating Innovation and Iteration through Orthopedic Product Development 

The dynamic realm of orthopedic product development has found a robust ally in rapid prototyping. This innovation leverages advanced 3D printing and additive manufacturing technologies to swiftly transform digital models into tangible prototypes. In a mere matter of hours or days, engineers and designers working on orthopedic research can iterate and refine designs, hastening the innovation cycle. This acceleration paves the way for speedier iterations, efficient incorporation of feedback, and optimal design enhancements. The outcome? Augmented device performance, elevated functionality, and an expedited journey from concept to commercialization. 

Customization and Personalization in Orthopedic Device Design 

Orthopedic devices necessitate tailored solutions to harmonize with the distinctive anatomical requisites of individual patients. The prowess of rapid prototyping empowers product development teams to craft patient-specific orthopedic implants and instruments. This is achieved through the fusion of advanced imaging techniques, computer-aided design, and orthopedic design consulting. By capitalizing on these rapid prototyping technologies, orthopedic professionals can engineer bespoke solutions that not only offer impeccable fit, but also precise alignment and superior functionality. The upshot? Optimized patient outcomes, heightened satisfaction, and an orthopedic product development landscape poised for transformation. 

Efficient Testing and Validation of Orthopedic Devices 

Prototypes conjured through rapid prototyping techniques transcend the realm of theory. They are subjected to rigorous testing and validation processes that mirror real-world circumstances. For orthopedic product design teams, this means a proactive identification of potential design glitches, a comprehensive evaluation of performance parameters, and steadfast regulatory compliance. By fostering an environment of early feedback and iterative testing, manufacturers can effectively curtail errors, slash costs, and expedite the time to market for orthopedic devices. The outcome? Enhanced efficiency, reduced risk, and orthopedic product development that adheres to the highest standards. 

Collaboration and Stakeholder Engagement in Orthopedic Design Consulting 

The power of rapid prototyping extends beyond the realm of design teams to foster productive collaboration among diverse stakeholders in orthopedic device development. By providing tangible prototypes for visualization and interaction, rapid prototyping emboldens surgeons, engineers, and stakeholders to contribute valuable insights. This collaborative approach facilitates informed decisions regarding design adaptations, usability enhancements, and functional requisites. The ultimate goal? Orthopedic instrumentation that seamlessly align with the desires and needs of end-users, culminating in heightened adoption and acceptance within the healthcare community. 

Cost-Effectiveness, Risk Mitigation, and Orthopedic Engineering 

The conventional pathways of orthopedic product development are often fraught with steep upfront costs, protracted timelines, and inherent risks. Rapid prototyping emerges as a potent antidote to these challenges, seamlessly curtailing development costs and compressing timeframes. Moreover, it serves as a vanguard against design pitfalls, identifying and resolving issues in their embryonic stages. By harnessing the potential of rapid prototyping, orthopedic product development teams adeptly allocate resources, attenuate financial risk, and usher innovative products to market with unprecedented efficacy. 

Future Implications of Orthopedic Device Engineering 

The impact of rapid prototyping in orthopedic device development is poised to burgeon exponentially in the forthcoming years. As materials, 3D printing technologies, and artificial intelligence continue to evolve, innovation will flourish, enabling the genesis of intricate and sophisticated orthopedic devices. Rapid prototyping shall remain at the heart of translating these breakthroughs into tangible solutions, relentlessly pushing the boundaries of orthopedic care. 

In conclusion, the landscape of orthopedic device development stands forever transformed by the advent of rapid prototyping. Through its application, orthopedic professionals have been empowered to create patient-specific solutions, improve device performance, and enhance patient outcomes. With the orthopedic industry embracing rapid prototyping technologies, we can expect an accelerated pace of innovation, a more personalized approach to care, and the development of advanced orthopedic devices that will shape the future of musculoskeletal medicine. 

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  • Matt Suits

    Matt Suits

    Head of Sales | [email protected]

    Matt has always loved interacting with clients to find solutions for their challenges. He was drawn to business development at Kaleidoscope Innovation because of the great potential he saw. After graduating from the Lindner College of Business at the University of Cincinnati, he worked with two startups, a marketing consultancy, a financial services company and the non-profit 3CDC. He believes that listening is the most important part of sales. In his free time, Matt enjoys movies, trying new foods, traveling and the great outdoors.

Technology’s New Helping Hand

In today's fast-paced world, it's become almost second nature for us to order products online and have them delivered right to our doorstep. From the newest gadgets to the clothes on your back, the efficiency of the process is truly remarkable. But have you ever stopped to wonder what goes on behind the scenes during this intricate process? How do companies plan, implement, and control where your goods and services are shipped to with such precision and speed? Let’s delve into the fascinating world of supply chain management and discover the secrets behind its efficiency.  

 At the heart of this remarkable process lies a well-orchestrated network of manufacturers, distributors, logistics providers, and retailers. A talented group of people uniquely specialized in their field to get these finished goods to your doorstep. Together they ensure that your products meet customer needs in a timely, cost-effective manner by optimizing their operations and working with meticulous planning and coordination.  

 The journey begins here at Kaleidoscope, we embark on the process of product development, collaborating closely with companies like P&G. Take Tide Pods, for example. We design, engineer, and test the product to meet consumer demands. From ideation to final production, extensive research and development are essential to creating innovative and high-quality products. This journey requires a strong partnership and collaborative effort to bring these ideas to life. 

 Once the products are ready to go, they are transported to distribution centers or warehouses strategically located to facilitate efficient distribution. These distribution centers act as hubs where products are received, sorted, and prepared for further transportation. Advanced technologies, such as automation and robotics, are employed to ensure that each delivery is assigned the most appropriate route, minimizing travel time and maximizing efficiency. 

 Once an order is placed, logistics comes into play. Sophisticated systems manage inventory, track shipments, and optimize routes to ensure timely delivery. The products are carefully packaged and labeled, ready to embark on their journey to the customer's doorstep. This process can be transported to you in various modes, including trucks, trains, ships, airplanes, and depending on the urgency of the delivery even drones.  

One of the key drivers of efficiency in this process is advanced technology. Logistics is a rapidly growing industry full of artificial intelligence, machine learning, and big data analytics. This technology is constantly analyzing vast amounts of data, so that companies can gain valuable insights into consumer behavior, and demand patterns. Furthermore, with the ability to track our packages in real-time we can track shipments, demand forecasting, route optimization, and manage inventory. This abundance of technology helps improve overall supply chain performance. 

Another crucial aspect of efficient product delivery is what’s called “last-mile logistics”. This final step of the journey from the distribution center to the customer's doorstep can often be the most challenging. To overcome this, companies are using innovative strategies to enhance efficiency and customer satisfaction. Delivery through drones, autonomous vehicles, and even crowdsourced delivery services are being tested and implemented to reduce delivery times and overcome the challenges of urban congestion.  

 So, the next time you receive a package at your doorstep, take a moment to appreciate the incredible logistics infrastructure and the efforts that go into making it happen. Acknowledging that behind these technological advancements, there is a dedicated workforce of logistics professionals who work tirelessly to ensure the smooth flow of goods.  The evolving world of logistics continues to push boundaries and find innovative solutions to meet the increasing demands of e-commerce and consumer expectations. It's an exciting time to witness the transformation of how products reach us with such efficiency and convenience. 

 What do you think the future of logistics will look like as technology advances? Do you have any experiences or insights to share about the logistics behind product deliveries? We'd love to hear your thoughts in the comments below. 

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  • Taylor Schmitt

    Taylor Schmitt

    Marketing Co-op | [email protected]

    Taylor Schmitt is currently a student at The Ohio State University, where she studies marketing. She loves exploring new opportunities and facing new challenges. While working at Kaleidoscope she has been able to work closely with the sales team to support business growth and brand visibility

  • Matt Suits

    Matt Suits

    Head of Sales | [email protected]

    Matt has always loved interacting with clients to find solutions for their challenges. He was drawn to business development at Kaleidoscope Innovation because of the great potential he saw. After graduating from the Lindner College of Business at the University of Cincinnati, he worked with two startups, a marketing consultancy, a financial services company and the non-profit 3CDC. He believes that listening is the most important part of sales. In his free time, Matt enjoys movies, trying new foods, traveling and the great outdoors.

The Future of AI-powered Healthcare

What is artificial intelligence (AI)? Is it the evoking computer from sci-fi aware of its own existence and determined to destroy humanity? Is it a robot that does our job for us while we kick our feet up? Right now, maybe it is neither, it can be defined as “A System that mimics human intelligence to perform complex tasks using advanced learning algorithms that capture underlying patterns and relationships from the data they collect.”  The tasks and benefits from such a system can be many but generally serve as three main use categories: accuracy improvement, automation of tasks, or a recommendations engine.

In developing a SAMD (Software As a Medical Device) product consider both the regulatory guidelines and best practices.  The FDA is partnering with industry to develop regulations in this emerging field, they recently released a guidance on Clinical Decision Support Software describing the criterion in which software is considered a medical device by the agency. And, during software life-cycle development, ISO 62304 outlines the processes of risk management, maintenance, configuration management, and problem resolution.

Developers should build in systems on the front end for data mining whether in the form of document capturing tools, video data collection, speech recognition, or otherwise.  And, comprehensive cybersecurity around these data sources in addition to the access, analysis, and output systems.

Lastly, algorithms should take bias into account.  This is already present in the diagnosis making process today, clinicians can jump to conclusions based on early information and stick to their guns even as new information becomes available (premature closure / anchoring). The algorithms themselves can have bias, in how data is fitted when machine learning is automated.

  • Automation Bias: Tendency of people to show deference to automated output, maybe due to person’s lack of confidence/experience, or assumption that the automation designed to make the correct determination.
  • Fitting Bias: Over Fitting- Automation has been overly relying on the trained data and does not provide correct responses when given new information.  Under Fitting - Machine is under trained and doesn’t correctly identify relationships between the variables.

Widespread AI use is in its infancy, its currently being leveraged across several surgical products currently on the market including surgery planners, guidance systems, AR, blood loss monitoring, and predictive analytics. The future holds many opportunities for AI to burn down existing healthcare challenges.

Accuracy Improvement:

  • Comprehensive Patient Medical Information
  • Summarization and Highlighting of Patient Case History
  • Accurate Encoding of procedures and diagnosis for insurance
  • Accurate diagnosis from medical images
  • Risk-aware decision making –using predictive analysis of surgical outcome, implant choice, length of hospital stay, risk of re-hospitalization
  • Post op x-ray, feedback loop, feedback to surgeon on trending accuracy stats, predictive risks
  • Physician burnout - make less errors during diagnosis
  • Physician shortage – making fewer surgeons more efficient

Automation Enabled Improvements:

  • Improved surgical planning / operation
  • AI-assisted surgical robotics
  • Supply chain automation
  • Reduced non-conformances, out-of-commission instrument sets
  • Reduced waste, reprocessing costs
  • Smart intra-op assistant / training

Recommendations Engine:

  • Patient/procedure/surgeon customized device on demand
  • Fair surgeon success ratings based on predictive risk/outcomes
  • Informing consumers on surgeon/facility for their condition to maximize outcomes

It probably won’t be too far into the future before some of these AI-enabled improvements become mainstream practice in the healthcare domain. The recent advances in ChatGPT have shown how complex knowledge intensive tasks such as text summarization, essay generation, intelligent Q&A (Question and Answer), etc. can be accomplished by current language models.  Convolutional Neural Networks (CNN)-based deep learning models are showing promise for automatic detection and classification of tumors in medical imaging. Advanced Machine Learning (ML), Rule-based modeling, and Embedded-AI can help with addressing other opportunities such as risk prediction, improved surgical planning, AI-assisted robotic devices, supply chain automation, and customized recommendations

AI will help in bringing consistency in the process, improve overall efficiency, reduce cost of operations while adhering and improving the regulatory compliance.

Interested in implementing AI/ML technology into your business?

Kaleidoscope uses advanced learning algorithms to capture patterns and relationships within your data to help you better understand the data collected and provide both exploratory and predictive analytics based on findings. Contact Matt Suits: [email protected]

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  • Eric Kennedy

    Eric Kennedy

    Principal Engineer | [email protected]

    Eric Kennedy is an engineer at Kaleidoscope Innovation based in Cincinnati, Ohio, and has over 15 years of global medical device experience leading large- and medium-scale concept-to-launch orthopedic, micro-surgical, cardiovascular and ophthalmic devices.

  • Dr. Ravi Nandigam

    Dr. Ravi Nandigam

    Principal Consultant

    Dr. Ravi Nandigam is a Principal Consultant in the Advanced Engineering Group at Infosys. He has 15 years of experience applying Artificial Intelligence, Machine Learning, and Software-based solutions in diverse Engineering domains. Dr.Nandigam is an inventor of a patent and author of many technical articles in peer-reviewed international journals on topics of AI/ML-based applications in Engineering.

  • Dr. Ravi Kumar G. V. V.

    Dr. Ravi Kumar G. V. V.

    Vice President and Head Advanced Engineering Group (AEG)

    Dr. Ravi Kumar is Vice President and Head Advanced Engineering Group (AEG) of Engineering Services, Infosys. He led numerous innovations and applied research projects for more than 26 years. His areas of expertise include mechanical structures and

AI as Intelligent Design? Not Yet, But It’s Coming.

From art generators to chatbots, AI seems to be having its zeitgeist moment in popular culture. But for those of us who work in design, the near-term and future applications of AI have been lively discussion points in strategic planning meetings for quite some time. There is no doubt that AI will be an instrumental part of our world’s future. It will allow us to rapidly synthesize all the data being collected via our phones, cameras, computers, smart devices, and much more, giving us the ability to decipher and understand that data in illuminating, meaningful, and likely, world-changing ways.  

What does this mean for the design industry? Though it may be a long time before AI is able to design a product from the ground up, the potential is clearly there. In fact, we believe AI is a tool that designers should be adding to their arsenal sooner rather than later. 


Putting AI to Work 

To put our money where our industry-informed opinions are, the Kaleidoscope Innovation team recently embarked on a studio project to design a high-end lighting fixture that could mimic lighting patterns found in nature. The project would enable our team to flex our aesthetic skills while using the full range of our design toolbox. One of those tools is Midjourney, a proprietary artificial intelligence program produced by an independent research lab by the same name. Though still in the open beta phase, Midjourney proved to be a useful partner in our mission. The collaboration between AI and the guiding hand of our expert design team delivered intriguing results. 

One important distinction about the AI portion of the project: We were not setting out to produce real-world functionality, and in fact, we had no expectation or need for the AI to produce fleshed-out ideas or even design sketches. This experiment was about exploring new territories in aesthetics and applying them to materials and manufacturability considerations. 

Our first step was to gather a team to collaborate on the search terms that would help visually articulate the aesthetic aspirations for our new fixture. Midjourney works by inputting text-based prompts, which the AI algorithm uses to generate new images using vast databases of existing images. The terms we fed the algorithm included chandelier, lighting, brilliant, elegant light, airy, crystalline patterns of light, dancing, photorealistic detailed plants, greenery, daytime, bright, modern, beautiful, natural colors, garden, and greenery. The team also used technical inputs alongside these qualitative descriptors to determine the aspect ratio and resolution while also guiding the algorithm to reference certain lighting styles and rendering approaches.  

Digesting these descriptive words, Midjourney searched vast amounts of data across the internet to create original—albeit amalgamated—artwork. The images it produced reflected the algorithm’s interpretation of the inputs the team provided. From there, we tweaked specific inputs to alter the color, lighting, tone, and subject matter, continuing to iterate until we had collected a series of AI-generated lighting fixtures that could inspire the team.

How Did AI Do?  

Based on the text inputs the team provided, Midjourney was able to identify design elements that could produce the effect of light shining through leaves. The images it produced looked organic, almost surreal in the way they were able to capture the kind of nature-made glow and transparency that is elusive in real-world lighting solutions. The various iterations of artwork then became mood boards that set up our team to brainstorm ways in which the effect could conceivably be produced.  

The algorithm’s interesting use of materials, colors, lighting effects, and overall mood inspired us to apply those attributes to a holistic design. In other words, instead of our team scratching their heads visualizing how the light should transmit, AI provided us with ideas that enabled us to focus on materials, manufacturability, technical requirements, and more. Rather than spending hours scouring the internet for inspirational imagery, the team was able to craft that inspiration imagery ourselves through AI in a fraction of the time—imagery that exactly aligned with our design vision. 

concept board

Without question, Midjourney served as a highly effective springboard that sparked ideas our team would probably not have come up with starting from a blank sheet of paper and pen. In this sense, AI provides an upfront efficiency that can move a project farther down the road faster than it might otherwise have gone. Perhaps more than that, a significant strength of AI in this application is that it can cast a wide net in terms of inspiration and exploration. It’s an open mind, and designers should be willing—and eager—to go down the rabbit holes, teasing out new possibilities. Once an intriguing direction is established, the designer can take over to turn the AI-generated inspiration into an actual product.  

The key to a successful AI collaboration is plugging in the right words or phrases to best draw out the AI. And so, crafting prompts could be viewed more as art than science. Further, with a program like Midjourney, there is an element of unpredictability: You don’t have much control over what you’re going to get out of it. There is a lot of trial and error and shooting in the dark. Therefore, if you already have a set idea in mind, using AI to design it will probably be more frustrating than productive.  

The inherent aspect of exploration and discovery is a factor to consider as well. Our team felt excited about experimenting with this technology specifically because the lighting fixture was an internal project. Had we been designing for a client, we would have been more hesitant to use AI while balancing product requirements, timeline, budget, and resources.  

Lastly, because this was a purely aesthetic exercise, we weren’t trying to solve any mechanical problems through AI—that’s skill is not in its wheelhouse at this point. This limitation provides a real barrier to the widespread adoption of AI, but as the algorithms improve over time, AI may be able to help us solve even our stickiest mechanical problems. 

Beyond leveraging AI for creative exploration, Kaleidoscope has also put it to use in some of our research work. As part of our insights and user experience programs, we often do ethnography or time-and-motion studies in which we observe individuals interacting with a tool or experience. Typically, one of our team members is responsible for reviewing videos to log data, tracking everything from how often someone does something to the amount of time it takes them to do it. It’s a time-consuming process that has led us to start dabbling with programming AI to analyze video recordings for certain elements and then export the data quickly and effectively. Using AI to track the frequency and duration of actions for time-and-motion studies shows tremendous potential to save time and reduce costs while freeing our team members to focus on more creative assignments. 

The Verdict 

The Kaleidoscope team came away with an appreciation for where AI can support our design efforts today, particularly as a powerful aid in producing aesthetic inspiration and as a tool to sort and output raw data. Both help the design process in productive ways and serve as a small window to what may someday be an AI-driven design future.

This was written for IDSA, if you'd like to see the INNOVATION Magazine article, please check out idsa.org/news-publications/innovation-magazine/spring-2023/

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  • Tony Siebel

    Tony Siebel

    Director of Design | [email protected]

    Tony Siebel is director of design at Kaleidoscope Innovation, delivering a user-centered mindset to products and experiences.

  • Tom Gernetzke

    Tom Gernetzke

    Senior Industrial Designer | [email protected]

    Tom Gernetzke is a senior lead industrial designer at Kaleidoscope Innovation and has spent the last 12 years creatively bringing new product ideas to life.

  • Caterina Rizzoni

    Caterina Rizzoni

    Lead Industrial Designer | [email protected]

    Caterina Rizzoni is a lead industrial designer at Kaleidoscope Innovation and is the Director-at-Large of Conferences for IDSA.