Eko Amri Jaya
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Eko Amri Jaya has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Binastya Anggara Sekti
Universitas Esa Unggul, Indonesia
2 shared publications - Jefry Sunupurwa Asri
Universitas Esa Unggul, Indonesia
2 shared publications - Ryan Putra Laksana
Universitas Esa Unggul, Indonesia
2 shared publications - Sawali Wahyu
Universitas Esa Unggul, Indonesia
2 shared publications - Vinsens Aji Pamungkas
Universitas Esa Unggul, Indonesia
2 shared publications
Latest Publications
- 3 July 2026
- Published by IGI Global Scientific Publishing
- p. 2311-2345
AI in Mental Health
Mental health disorders affect 970+ million people globally, with 75%+ treatment gaps in developing countries. This chapter examines AI's transformative potential through machine learning, natural language processing, computer vision, and wearable technologies enabling early detection, personalized interventions, and continuous monitoring. Current applications show promise: CBT chatbots like Woebot and Wysa reduce depression/anxiety symptoms; digital phenotyping platforms detect relapses days earlier; computer vision systems enhance PTSD screening. However, challenges exist including algorithmic bias against minorities, privacy concerns, system opacity, and over-reliance risks. The chapter explores future directions emphasizing human-AI collaborative frameworks, federated learning for privacy protection, and explainable AI for transparency. Successful implementation requires interdisciplinary collaboration between computer scientists, healthcare professionals, ethicists, and policymakers for equitable mental health care delivery.
- 3 July 2026
- Published by IGI Global Scientific Publishing
- p. 2311-2345
AI in Mental Health
Mental health disorders affect 970+ million people globally, with 75%+ treatment gaps in developing countries. This chapter examines AI's transformative potential through machine learning, natural language processing, computer vision, and wearable technologies enabling early detection, personalized interventions, and continuous monitoring. Current applications show promise: CBT chatbots like Woebot and Wysa reduce depression/anxiety symptoms; digital phenotyping platforms detect relapses days earlier; computer vision systems enhance PTSD screening. However, challenges exist including algorithmic bias against minorities, privacy concerns, system opacity, and over-reliance risks. The chapter explores future directions emphasizing human-AI collaborative frameworks, federated learning for privacy protection, and explainable AI for transparency. Successful implementation requires interdisciplinary collaboration between computer scientists, healthcare professionals, ethicists, and policymakers for equitable mental health care delivery.
- 19 June 2026
- Published by IGI Global Scientific Publishing
- p. 2311-2345
AI in Mental Health
Mental health disorders affect 970+ million people globally, with 75%+ treatment gaps in developing countries. This chapter examines AI's transformative potential through machine learning, natural language processing, computer vision, and wearable technologies enabling early detection, personalized interventions, and continuous monitoring. Current applications show promise: CBT chatbots like Woebot and Wysa reduce depression/anxiety symptoms; digital phenotyping platforms detect relapses days earlier; computer vision systems enhance PTSD screening. However, challenges exist including algorithmic bias against minorities, privacy concerns, system opacity, and over-reliance risks. The chapter explores future directions emphasizing human-AI collaborative frameworks, federated learning for privacy protection, and explainable AI for transparency. Successful implementation requires interdisciplinary collaboration between computer scientists, healthcare professionals, ethicists, and policymakers for equitable mental health care delivery.
- 19 June 2026
- Published by IGI Global Scientific Publishing
- p. 2311-2345
AI in Mental Health
Mental health disorders affect 970+ million people globally, with 75%+ treatment gaps in developing countries. This chapter examines AI's transformative potential through machine learning, natural language processing, computer vision, and wearable technologies enabling early detection, personalized interventions, and continuous monitoring. Current applications show promise: CBT chatbots like Woebot and Wysa reduce depression/anxiety symptoms; digital phenotyping platforms detect relapses days earlier; computer vision systems enhance PTSD screening. However, challenges exist including algorithmic bias against minorities, privacy concerns, system opacity, and over-reliance risks. The chapter explores future directions emphasizing human-AI collaborative frameworks, federated learning for privacy protection, and explainable AI for transparency. Successful implementation requires interdisciplinary collaboration between computer scientists, healthcare professionals, ethicists, and policymakers for equitable mental health care delivery.