Philips has inked a memorandum of understanding (MOU) with Siloam Hospitals Group, a private healthcare provider in Indonesia.
The MOU will allow Philips to offer its AI capabilities to the Indonesian healthcare sector. The agreement is in collaboration with the Universitas Pelita Harapan (UPH) Medical Sciences Group, Philips said, and will include the following:
- The introduction of AI technologies into Siloam Hospitals in radiology, pathology, interventional cardiology, and clinical operations
- Development of local expertise in AI and digital health through collaboration with the UPH
- Sharing of global best practices in informatics and AI research with Siloam Hospitals and UPH
- Integration of Philips' AI offerings into Siloam Hospitals' healthcare management systems, including telemedicine, electronic medical records, and remote patient monitoring
- Support of the Indonesian government's healthcare goals
- Support of Siloam Hospitals' Environmental, Social, and Governance (ESG) efforts, including sustainable supply chains, environmental stewardship, equitable workplaces, and expanded healthcare access and education
"This partnership with Philips, Siloam Hospitals, and Universitas Pelita Harapan is a significant milestone in our journey to bring advanced healthcare technologies to Indonesia," said Caroline Riady, Siloam Hospitals Group's deputy president director.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
