The inferior hemocompatibility or antibacterial properties of blood‐contacting materials and devices are restraining factors that hinder their successful clinical utilization. To highlight these, a plasma‐enhanced modification strategy is favored for surface tailoring of an extensively used biomaterial, polypropylene (PP). The surface activation of the PPs is achieved by oxygen plasma etching and subsequent surface functionalization through amine‐rich precursor mediated coating by plasma glow discharge. After optimum plasma processing parameters are decided, heparin (anticoagulant and antithrombic drug) is either attached or covalently conjugated on the PPs’ surfaces. The aminated films produced at 75 W plasma power with 15 min exposure time are highly hydrophilic (34.72 ± 5.92°) and surface active (65.91 mJ m⁻²), facilitating high capacity heparin immobilization (≈440 µg cm⁻²) by covalent linkage. The kinetic‐blood coagulation rate and protein adhesion amount on the plasma‐mediated heparinized PPs are decreased about tenfold and 15‐fold, and platelet adhesion is markedly lowered. In addition, heparinized‐PP surfaces comprise superior antibacterial activity against gram‐positive/‐negative bacteria conveyed particularly by contact‐killing (99%). The heparin‐coating did not cause cytotoxicity on fibroblast cells, instead enhanced their proliferation, as shown by the (3‐(4,5‐Dimethylthiazol‐2‐yl)‐2,5‐Diphenyltetrazolium Bromide) assay. Overall, this simple methodology is highly proficient in becoming a universal strategy for developing dual‐functionalized blood‐contacting materials.

Polymer chemistry research has progressed through three methodological eras: the alchemist's intuitive trial‐and‐error, the scientist's rule‐based design, and the robot's algorithm‐guided automation. While approaches combining combinatorial chemistry with statistical design of experiments offer a systematic approach to polymer discovery, they struggle with complex design spaces, avoid human biases, and scale up. In response, the discipline has adopted automation and artificial intelligence (AI), culminating in self‐driving laboratories (SDLs), integrating high‐throughput experimentation into closed‐loop, AI‐assisted design‐build‐test‐learn cycles, enabling the rapid exploration of chemical spaces. However, while SDLs address throughput and complexity challenges, they introduce new forms of the original problems: algorithmic biases replace human biases, data sparsity creates constraints on design space navigation, and black‐box AI models create transparency issues, complicating interpretation. These challenges emphasize a critical point: complete algorithmic autonomy is inadequate without human involvement. Human intuition, ethical judgment, and domain expertise are crucial for establishing research objectives, identifying anomalies, and ensuring adherence to ethical constraints. This perspective supports a hybrid model grounded in symbiotic autonomy, where adaptive collaboration between humans and AI enhances trust, creativity, and reproducibility. By incorporating human reasoning into adaptive AI‐assisted SDL workflows, next‐generation autonomous polymer discovery will be not only faster but also wiser.