The pandemic, increasing respiratory illnesses, a rise in geriatric patients, an increase in preterm births, and more contributed to the ventilator market’s growth. Over the years, most clinicians and hospitals have relied on ventilators manufactured outside India. The expense of importing, its installation, availability of spares and worldwide demand for ventilators during the pandemic have been a constant grievance in the medical community in India. Due to the increased demand for ventilators, hospitals are now looking at Indian manufacturers who have excelled in building affordable invasive and non-invasive ventilators. Advanced microprocessor-controlled ventilation technology, in combination with the benefits of new innovative respiratory modes, has created opportunities to enhance patient care and quality of service. 
Integration of volume capnogram, a graphical representation of exhaled carbon dioxide concentration over time, to ventilators has emerged as a valuable tool for assessing ventilation status and helps in guiding clinical decision-making. Volume capnography is promising in diagnosing and monitoring pulmonary embolism, a potentially life-threatening condition. Also, this technology enables effective ventilation settings ensuring optimal patient care.
Patient-ventilator synchrony is crucial for optimizing ventilatory performance and aiding in patient recovery. Recent trends have focused on enhancing synchrony to minimize patient-ventilator asynchrony, promoting adequate respiratory support through advanced triggering and sensing mechanisms, and developing dual- or multi-level ventilation modes. These innovations improve the coordination between the patient’s respiratory efforts and the ventilator’s assistance, enhancing patient comfort.
Auto-Weaning, an automated interactive respiratory control system, with learning from the various patient respiratory performance and physiological inputs, including clinical conditions, is being viewed as a highly appreciated feature. Unlike the limited weaning facility of earlier machines, the advent of machine learning and faster processing power of systems, ventilators of today have built a capacity of self-learning to assist clinicians during their decision-making process.
The ongoing development of intelligent ventilation modes, the integration of artificial intelligence algorithms, and the utilization of biomarkers and predictive models hold promising potential for further improving patient outcomes and enhancing the delivery of ventilatory support.