  It has been argued [Tassieri, \textit{Soft Matter}, 2015, \textbf{11}, 5792]
that linear microrheology with optical tweezers (MOT) of living systems
``\textit{is not an option}'', because of the wide gap between the observation
time required to collect statistically valid data and the mutational times of
the organisms under study. Here, we have taken a first step towards a possible
solution of this problem by exploiting modern machine learning (ML) methods to
reduce the duration of MOT measurements from several tens of minutes down to
one second. This has been achieved by focusing on the analysis of computer
simulated trajectories of an optically trapped particle suspended in a set of
Newtonian fluids having viscosity values spanning three orders of magnitude,
i.e. from $10^{-3}$ to $1$ Pa$\cdot$s. When the particle trajectory is analysed
by means of conventional statistical mechanics principles, we explicate for the
first time in literature the relationship between the required duration of MOT
experiments ($T_m$) and the fluids relative viscosity ($\eta_r$) to achieve an
uncertainty as low as $1\%$; i.e., $T_m\cong 17\eta_r^3$ minutes. This has led
to further evidences explaining why conventional MOT measurements commonly
underestimate the materials' viscoelastic properties, especially in the case of
high viscous fluids or soft-solids such as gels and cells. Finally, we have
developed a ML algorithm to determine the viscosity of Newtonian fluids that
uses feature extraction on raw trajectories acquired at a kHz and for a
duration of only one second, yet capable of returning viscosity values carrying
an error as low as $\sim0.3\%$ at best; hence the opening of a doorway for MOT
in living systems.
