To Investors: A unique opportunity to be part of a venture with a clear path to becoming a hundred-million-dollar company, driven by technological innovation, market demand, and a visionary approach to health and wellness.
Potential predictive benefits are immense: Despite promising past advances, the implementation of predictive analytics in healthcare faces several challenges, including data privacy concerns, the need for large and diverse data sets to train AI models effectively, and the integration of these technologies into existing healthcare infrastructures. IntualityAI offers a portable prediction technology that interprets any data stream to produce intuitive future data reflecting subtle trends that foreshadow important alerts to needed actions and decisions.
This group benefits significantly from real-time health tracking to manage chronic conditions and prevent emergencies. This segment is prioritized due to the significant and growing market size, with the elderly care market projected to reach USD 2,882.66 billion by 2030 at a CAGR of 6.80%. The increasing prevalence of chronic diseases among the elderly make this segment particularly urgent and expansive.
North America dominates the elderly monitors market because of the increased incidence and prevalence of patients with lifestyle diseases. Furthermore, well-established reimbursement policies and an increase in customer purchasing power will drive the growth of the elderly monitors market in the region during the forecast period.
Asia-Pacific is expected to grow at the highest growth rate in the forecast period of 2023 to 2030 because of the growing sub-segmentation around the world Furthermore, the presence of major key players is expected to drive the growth of the elderly monitors market in the region in the coming years.
What are other key AI software companies that take into account human bias in their predictive analytics programs? A few Medical industry players are listed below, but predictive analytics programs are not likely even as advanced as IntualityAI, since Prediction is still not considered reliable or mainstream, and their software is therefore not even oriented toward Prediction:
IntualityAI: Specializes in offering a portable prediction technology that interprets any data stream to produce intuitive future data. This technology reflects subtle trends that foreshadow important alerts to needed actions and decisions . It is dedicated to filling the human behavioral gap in machine knowledge so machines can help make better decisions, highlighting its focus on humanizing AI and dealing with the complexities of human psychology and decision-making.
Wysa: A cognitive-behavioral-therapy AI chatbot used by millions worldwide. It has received recognition from the UK's National Health Service and the FDA, indicating its reliable use of AI in considering psychological factors .
Ada Health: Uses AI to help patients understand their symptoms and receive appropriate care. Its foundation on psychological principles for symptom analysis and care recommendations makes it a notable player in the field.
Elemental Cognition: Founded by David Ferrucci, the inventor of IBM's Watson, this company is developing machine learning that aims to increase trust from human users, indicating a focus on the psychological aspects of human-AI interaction .
SAS Healthcare Analytics, SAP Analytics Cloud, and MedeAnalytics: These software solutions process and analyze data collected by health monitors to provide actionable insights, implying a consideration of human factors in data interpretation.
While not explicitly stated as a priority, these companies, through their focus on health, decision-making, and therapeutic applications, inherently consider human biases and psychological factors in their predictive analytics programs. IntualityAI, in particular, places a strong emphasis on integrating psychological insights to bridge the gap between human cognition and machine intelligence, making it a prominent example of a software company that addresses human bias in its predictive analytics.
ElderCare is specifically focused on tracking patterns in continuous change. Any prediction methodology requires timely data points, and generates future timely data points while more loosely tracking correlations to data that influences the accuracy of those data streams. Past data patterns are used to predict future data patterns, all as relevant as practically possible, including real-time recognitions that specific patters are CHANGING.
Conclusion: Huge markets await. Predictive Analytics is Critical, as is Continuous Monitoring.