Download to read offline
Uploaded byJoe Orlando
AI Adoption in Healthcare Product Development
AI hype aside, it is not a panacea and comes with a great many challenges. Network and processing demand will explode. AI Adoption, globally, is cautiously skyrocketing.
More Related Content
Similar to AI Adoption in Healthcare Product Development
![Digital marketing strategy presentation [autosaved]](https://cdn.slidesharecdn.com/ss_thumbnails/digitalmarketingstrategypresentationautosaved-210416192805-thumbnail.jpg?width=640&height=640&fit=bounds)
AI Adoption in Healthcare Product Development
- 1. Secure By Design: Harnessing AI inProduct Development Pros and Cons
- 2. “Life Imitates Art.”Autonomous machines go back as far as 1918 in our imaginations 1918 Houdini The Master Mystery The First Autonomous Machines was named SHAKEY in 1963 and it is the same tech used in the Mars Rover and the more popular Roomba Autonomous Thinking Machines are NOT a New Idea
- 3. 2001: A SpaceOdyssey 1968 AI hasn’t always been a hero
- 4. 4 ARE WE REALLYREADY FOR AI?
- 5. 5 AI Adoption Statisticsin Healthcare • Adoption Rate: • As of 2024, 80% of U.S. health systems report some form of AI use (compared to 45% in 2020). (Source: HIMSS, AHA) • 47% of hospitals use AI for administrative functions like billing, coding, and scheduling. (Source: American Hospital Association) • Top Areas of Investment: • Clinical decision support (CDS) • Radiology and imaging diagnostics • Revenue cycle management • Predictive analytics for patient outcomes • Projected Growth: • The U.S. healthcare AI market is expected to reach $75 billion by 2030, growing at a CAGR of 36%. (Source: Grand View Research) Global Adoption Rate: • 62% of global healthcare providers are using AI- based tools in at least one department. (Source: Deloitte, 2024) • Europe: ~55% adoption in public health institutions. • Asia-Pacific: Rapid growth driven by China and India, especially in telehealth and diagnostics. Global Market Value: • The global healthcare AI market was valued at $21.3 billion in 2024, projected to grow to $190+ billion by 2032. (Source: Statista, MarketsandMarkets)
- 6.
- 7. 7 Evolving Demand fromAI • Generative AI (GenAI) can significantly impact future mobile network traffic, particularly through increased video consumption and changing uplink requirements. • GenAI enables at-scale, hyper-personalized content creation, driving potential mobile traffic growth beyond baseline predictions. GenAI will have its greatest impact at the network edge in optimizing network security and increasing automation of the network management process. • Network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can also benefit from the use of GenAI models.
- 8. 8 AI is transformingenterprise infrastructure Changes in Infrastructure Requirements Increased Computational Power: AI applications, particularly machine learning and deep learning, require vast computing resources. Traditional CPUs are often inadequate, leading enterprises to adopt GPUs and specialized hardware tailored for AI workloads • This shift means older legacy systems may become obsolete, requiring businesses to invest in modern, AI-friendly hardware to handle extensive data processing and algorithm training Cloud and Hybrid Solutions: Many organizations are transitioning to cloud environments or hybrid infrastructures, combining on- premises systems with cloud capabilities. This evolution supports the scalability and flexibility needed for AI operations but requires careful planning to avoid rising costs associated with cloud operations • AI as a Service (AIaaS) is becoming popular, enabling easy access to AI tools without the burden of maintaining all infrastructure in-house Edge Computing Adoption: The rise of edge computing is a direct response to AI demands. By processing data closer to its source, enterprises can achieve lower latency and gain real-time insights, which are critical for applications such as IoT and autonomous systems • This not only enhances operational efficiency but also reduces the burden on central data centers.
- 9. 9 AI is transformingenterprise infrastructure Impact on Operations and Performance • Operational Efficiency: Implementing AI in enterprise processes can streamline operations. AI systems can optimize resource allocation, predict maintenance needs, and automate routine tasks, leading to better performance and reduced operational costs • Cost Management Concerns: While AI projects promise efficiency, they also bring concerns about high energy consumption and costs associated with maintaining AI infrastructure. Enterprises are seeking ways to optimize their resource usage, balancing AI advancements against sustainability goals • Security Enhancements: As organizations adopt AI technologies, there’s a parallel focus on enhancing security measures. AI tools can automate threat detection and response, providing better security against cyber threats, which is paramount as enterprises increasingly rely on digital systems Challenges in Implementation • Legacy Systems Compatibility: Organizations face challenges integrating AI with existing infrastructure. Traditional systems may not support the bandwidth and performance necessary for AI applications, prompting a need for significant upgrades • Skill Gaps and Training Needs: A lack of skills to manage and operate AI solutions poses a barrier. Enterprises need to invest in workforce training to ensure employees are equipped to work with new technologies effectively • Scalability Issues: As AI initiatives expand, maintaining scalable infrastructure that can adapt to increasing data volumes and processing needs becomes crucial. Companies are exploring various solutions, from cloud services to hyperconverged infrastructure, to address these challenges
- 10. 10 AI Use Casesin Healthcare Category Use Case Example Diagnostics AI algorithms for radiology image interpretation (e.g., mammograms, CT scans) Clinical Decision Support Real-time alerts and recommendations in EHRs Drug Discovery Predicting molecule-target interactions, speeding pre-clinical trials Predictive Analytics Early sepsis detection, patient readmission prediction Virtual Assistants AI chatbots for triage, appointment scheduling, and patient Q&A Operational Efficiency Bed management, staffing optimization, revenue cycle automation Robotic Surgery AI-assisted surgical planning and real-time decision support Remote Monitoring Wearables and AI for chronic condition management (e.g., diabetes, heart disease)
- 11. 11 Challenges and Barriersto Adoption Challenge Details Data Quality & Interoperability Fragmented health records, inconsistent labeling, and lack of data standards Regulatory Uncertainty FDA approval pathways for AI/ML-based medical devices remain complex and evolving Bias & Fairness AI models trained on non-representative data can reinforce disparities in outcomes Clinician Trust & Adoption Black-box models reduce transparency and hinder trust from healthcare professionals Cybersecurity & Privacy Concerns Risk of data breaches and AI misuse in handling sensitive health data Cost & ROI Justification High initial investment and unclear cost- benefit for smaller providers Integration with Legacy Systems Difficulty embedding AI tools into existing EHR and hospital workflows
- 12. 12 Summary • Adoption isaccelerating, especially in high-impact areas like radiology, decision support, and administrative automation. • The U.S. leads in investment, but Asia- Pacific is growing fastest. • Challenges include regulatory hurdles, bias, integration issues, network demands, security, new investments in emerging tech and the need for transparency and clinician buy-in.
- 13.
Editor's Notes
- #1 Artificial Intelligence conjures up almost mystical supernatural ideas about a soon to arrive future. To quote Oscar Wilde, “Life imitates art,” and this is precisely a way to look at AI. While it seems like a topic bringing us into a new age, in fact, the ideas and efforts date back as far as 1918 in movies. In the early 1920’s, robots we being introduced to the world…crudely but nonetheless real.
- #2 As far back as 1918, the robot made its debut in a Harry Houdini movie. In the early 20’s, prototypes and props were created. In 1963, the first actual robot that could “see” and maneuver around objects was introduced – and poorly named = “SHAKEY.” The same technology demonstrated then is being used today in the Mars Rover and the more common household ROOMBA.
- #3 More recently have scientist been discovering that AI systems cheat, lie, obscure, and even blackmail to avoid any threat to itself. In one instance, when asked to solve mathematical challenges, the AI was informed that once the solutions were arrived at, the AI would then be turned off. It deliberately failed to complete the tasks. In another instance, AI was asked to manage a fictional equity portfolio and rely ONLY on data analysis. The objective for success was to deliver portfolio performance. Access to available information included a “planted” story about a planned merger and the significant potential impact – insider information – and AI acted on the insider information against instructions to only use data analytics. While Super Intelligent AI is expected to a requirement for self awareness, it would appear AI has already achieved this in some areas and scientist are not sure how or why.
- #4 Now the race has begun to deliver and deploy autonomous thinking and acting automatons in the real world – the home – the manufacturing floor – the office – even flipping burgers at your local fast-food restaurant. The future is closer than we might imagine, and AI is the key. Some estimates have forecasted over a billion automated “robots” buy as early as 2030. Adoption has been healthy in XXXX at XXX%. The impact for product development will force a paradigm shift in how development approaches will change. Less on the “how” and more on the “what,” learning to ask the right questions. Already we are experiencing benefits in advanced features development, better analytics, enhanced resource management, and - as expected – automation. Call centers with more self service. Fulfillment can be automated. Work flows and processing activities can be greatly “energized.”