The AI Revolution in Healthcare
Authors: Tim Hulsen, Senior Data & AI Scientist at Philips & Professor AI & Data Supported Healthcare, Rotterdam University of Applied Sciences, Francesca Manni, Clinical Scientist at Philips and Katarzyna Markiewicz, Director, AI Strategic Intelligence Lead at Philips
Artificial intelligence and big data analytics are ushering in a transformative era in healthcare, fundamentally changing our approach to public health. These technologies enable healthcare providers to move from reactive to preventive care, analyze vast amounts of medical data in real time, and deliver more personalized treatment options to patients worldwide. Healthcare providers are becoming increasingly aware of the benefits and opportunities of applying AI in the patient care cycle [1].
According to the Philips 2024 Future Health Index [2], a comprehensive study of 3,000 healthcare leaders across 14 countries, healthcare leaders have already implemented AI for clinical decision support across different domains, e.g., radiology and in-hospital patient monitoring. Furthermore, 92% of leaders consider automation crucial for addressing staff shortages and freeing up more time for high-skill work and enabling better patient care. Looking ahead, 41% of healthcare leaders prioritize AI implementation in remote patient monitoring over the next three years.
However, despite the AI hype observed for several years and the fact that, like many digital health applications, AI thrived during the pandemic, we can safely say that we are still at relatively early stages of AI maturity [3]. Growing healthcare challenges combined with opportunities that AI technologies bring, creates a huge opportunity for talented machine learning and AI professionals who want to put their skills to good use to help improve the lives of people across the world.
Emerging Trends Shaping Healthcare AI
The healthcare AI landscape is rapidly evolving across two major fronts. Traditional ML continues to advance diagnostic accuracy, optimize clinical workflows, and enhance medical imaging analysis. Meanwhile, generative AI is opening new frontiers in drug discovery, medical research synthesis, and personalized treatment planning. These developments are creating unprecedented opportunities for AI practitioners to drive healthcare innovation [4].
Philips’ Vision for AI-Powered Healthcare
Philips champions a human-centered approach to AI development, creating solutions that enhance rather than replace healthcare professionals, enabling them to focus on patient care. Our AI initiatives in medical imaging, patient monitoring, and healthcare informatics are guided by rigorous ethical principles and a singular focus: improving patient outcomes worldwide. AI and big data are some of the tools that are helping us achieve our vision to provide care to billions of people.
Philips’ approach
Purpose-Driven Innovation
AI represents more than a path to profit — we believe it’s a powerful tool for improving human lives. Our innovations help healthcare providers deliver better care to more patients worldwide.
Real-World Impact Through Global Partnerships
Our vast partnerships with healthcare providers and hospitals worldwide provide invaluable real-world feedback and insights. AI practitioners at Philips work on actual healthcare challenges, collaborating directly with medical professionals who use our solutions daily.
Diverse, High-Quality Data Access
Our global presence provides access to diverse and high-quality medical data sets, critical for developing more inclusive and unbiased AI systems [5]. This unique advantage enables our teams to create solutions that work effectively across different demographics and healthcare settings.
Responsible AI
While we are pushing the boundaries of what is possible at the intersection of AI and healthcare, we don’t lose sight of ethical principles such as transparency, reliability, and fairness. Responsible AI (RAI) provides a framework to ensure these principles are preserved in AI initiatives [6]. By embracing the (current and future) technical possibilities of AI, and at the same time making sure that AI is explainable and responsible, we help hospitals withstand future challenges [7].
World-Class Expertise and Collaboration
Philips brings together leading experts in healthcare and technology. Our collaborative environment ensures that AI specialists can focus on their strengths while learning from professionals across various disciplines, from clinical experts to software architects.
Philips AI projects
Our AI solutions are carefully designed to give back time to healthcare professionals and enhance their decision-making. Whether by enabling fast, high-quality imaging, optimizing clinical workflows, or providing timely clinical insights, we use AI to address providers’ most pressing needs and challenges. By integrating AI into clinical and operational workflows, we enable more efficient, informed, and personalized care. We focus on enhancing human expertise and empowering caregivers to focus on what matters most: patient care. Our AI solutions are centered around four value drivers: 1) streamlining workflows and operations; 2) providing clinical insights; 3) expanding access to patient care; and 4) supporting personalized self-care.
Here are some examples of Philips AI solutions related to these value drivers:
- Streamlining workflows and operations
Philips AI solutions automate administrative and repetitive tasks, giving healthcare professionals more time to focus on patient care. For example, MR SmartSpeed [8] is an AI-based imaging technology that can increase imaging speed by up to a factor of 3 while providing up to 65% greater resolution* to deliver outstanding image quality. It is applicable to 97% of clinical protocols** to address the needs of a broad range of patients in various conditions.
*Compared to SENSE imaging.
**On average, measured across a sample of sites from Philips MR Installed Base.
2. Providing clinical insights
Philips AI solutions empower clinicians with actionable insights at the point of care, supporting informed decision-making in critical moments. Our CT Lung Nodule CAD [9] offers an automated process that identifies and marks regions of interest based on image features associated with lung nodules. It is intended for use as a second reader after an initial interpretation of the diagnostic image has been performed [10].
3. Expanding access to patient care
Philips AI solutions help healthcare professionals reach more patients, including those in underserved communities, by extending high-quality care from the hospital into the home and other care settings. One example is Cardiologs [11], an arrhythmia diagnostic software that is cloud-based, vendor-neutral and powered by AI* to streamline ECG analysis. Its clinically validated algorithm helps detect more than 20 types of arrhythmia events and parameters to expand the breadth of the physician’s clinical interpretation.
*The Cardiologs Platform interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician’s knowledge of ECG patterns, patient background, clinical history, symptoms, and other diagnostic information.
4. Supporting personalized self-care
Philips AI solutions help people take care of their health and well-being at home by offering personalized guidance tailored to their needs and preferences. Philips Sonicare DiamondClean Prestige 9900 [12] is an electric power toothbrush that uses sensors to detect the cleaning motions people use and the coverage they achieve. It pairs with a mobile app that allows people to better understand their brushing technique and take an active role in improving their oral health.
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References:
1. Luordo, D. et al.: Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life 14 (5): 557 (2024). https://www.mdpi.com/2075-1729/14/5/557
2. Philips 2024 Future Health Index. https://www.philips.com/a-w/about/news/future-health-index/reports/2024/better-care-for-more-people
3. Olaye, I.M., Seixas, A.A.: The Gap Between AI and Bedside: Participatory Workshop on the Barriers to the Integration, Translation, and Adoption of Digital Health Care and AI Startup Technology Into Clinical Practice. J. Med. Internet Res. 25, e32962 (2023). https://www.jmir.org/2023/1/e32962/
4. Hulsen, T.: Artificial Intelligence in Healthcare: Current State and Future Perspectives. MDPI (2024). https://www.mdpi.com/books/reprint/9229-artificial-intelligence-in-healthcare-current-state-and-future-perspectives
5. Hulsen, T.: Big and rich data as fuel for the AI engine. Medium (2023). https://medium.com/philips-technology-blog/big-and-rich-data-as-fuel-for-the-ai-engine-a7c10755b003
6. Trocin, C., Mikalef, P., Papamitsiou, Z., Conboy, K.: Responsible AI for digital health: A synthesis and a research agenda. Inf. Syst. Front. 25, 2139–2157 (2023). https://doi.org/10.1007/s10796-021-10146-4
7. Kuran, D., van Wissen, A., van Hartskamp, M., Janssen, G.: Navigating the Future: Philips and the Evolving AI Regulation Landscape. https://medium.com/philips-technology-blog/navigating-the-future-philips-and-the-evolving-ai-regulation-landscape-5ff05e70bd3d
8. Philips MR SmartSpeed. https://www.usa.philips.com/healthcare/resources/landing/smartspeed
9. Philips CT Lung Nodule analysis ClearRead CAD.
https://www.philips.co.uk/healthcare/product/NICA555/ct-lung-nodule-analysisclearreadcad
10. Philips Advanced Visualization Workspace 15 — Clinical Datasheet. https://www.documents.philips.com/assets/20230725/835aafd3d76e420d987bb04a01540d97.pdf
11. Philips Cardiologs https://cardiologs.com/
12. Philips AI-enabled Solutions. https://www.philips.com/a-w/about/artificial-intelligence/ai-enabled-solutions.html#triggername=less2_isc2
